Latent Space: The AI Engineer Podcast

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While the world melts down over DeepSeek, few are talking about the OTHER notable group of former hedge fund traders who pivoted into AI and built a remarkably profitable consumer AI business with a tiny team with incredibly cracked engineering team — Chai Research. In short order they have:

* Started a Chat AI company well before Noam Shazeer started Character AI, and outlasted his departure.

* Crossed 1m DAU in 2.5 years - William updates us on the pod that they’ve hit 1.4m DAU now, another +40% from a few months ago. Revenue crossed >$22m.

* Launched the Chaiverse model crowdsourcing platform - taking 3-4 week A/B testing cycles down to 3-4 hours, and deploying >100 models a week.

While they’re not paying million dollar salaries, you can tell they’re doing pretty well for an 11 person startup:

The Chai Recipe: Building infra for rapid evals

Remember how the central thesis of LMarena (formerly LMsys) is that the only comprehensive way to evaluate LLMs is to let users try them out and pick winners?

At the core of Chai is a mobile app that looks like Character AI, but is actually the largest LLM A/B testing arena in the world, specialized on retaining chat users for Chai’s usecases (therapy, assistant, roleplay, etc). It’s basically what LMArena would be if taken very, very seriously at one company (with $1m in prizes to boot):

Chai publishes occasional research on how they think about this, including talks at their Palo Alto office:

William expands upon this in today’s podcast (34 mins in):

Fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it.

And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours.

In Crowdsourcing the leap to Ten Trillion-Parameter AGI, William describes Chai’s routing as a recommender system, which makes a lot more sense to us than previous pitches for model routing startups:

William is notably counter-consensus in a lot of his AI product principles:

* No streaming: Chats appear all at once to allow rejection sampling

* No voice: Chai actually beat Character AI to introducing voice - but removed it after finding that it was far from a killer feature.

* Blending: “Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model.” (that’s it!)

But chief above all is the recommender system.

We also referenced Exa CEO Will Bryk’s concept of SuperKnowlege:

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Timestamps

* 00:00:04 Introductions and background of William Beauchamp

* 00:01:19 Origin story of Chai AI

* 00:04:40 Transition from finance to AI

* 00:11:36 Initial product development and idea maze for Chai

* 00:16:29 User psychology and engagement with AI companions

* 00:20:00 Origin of the Chai name

* 00:22:01 Comparison with Character AI and funding challenges

* 00:25:59 Chai's growth and user numbers

* 00:34:53 Key inflection points in Chai's growth

* 00:42:10 Multi-modality in AI companions and focus on user-generated content

* 00:46:49 Chaiverse developer platform and model evaluation

* 00:51:58 Views on AGI and the nature of AI intelligence

* 00:57:14 Evaluation methods and human feedback in AI development

* 01:02:01 Content creation and user experience in Chai

* 01:04:49 Chai Grant program and company culture

* 01:07:20 Inference optimization and compute costs

* 01:09:37 Rejection sampling and reward models in AI generation

* 01:11:48 Closing thoughts and recruitment

Transcript

Alessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and today we're in the Chai AI office with my usual co-host, Swyx.

swyx [00:00:14]: Hey, thanks for having us. It's rare that we get to get out of the office, so thanks for inviting us to your home. We're in the office of Chai with William Beauchamp. Yeah, that's right. You're founder of Chai AI, but previously, I think you're concurrently also running your fund?

William [00:00:29]: Yep, so I was simultaneously running an algorithmic trading company, but I fortunately was able to kind of exit from that, I think just in Q3 last year. Yeah, congrats. Yeah, thanks.

swyx [00:00:43]: So Chai has always been on my radar because, well, first of all, you do a lot of advertising, I guess, in the Bay Area, so it's working. Yep. And second of all, the reason I reached out to a mutual friend, Joyce, was because I'm just generally interested in the... ...consumer AI space, chat platforms in general. I think there's a lot of inference insights that we can get from that, as well as human psychology insights, kind of a weird blend of the two. And we also share a bit of a history as former finance people crossing over. I guess we can just kind of start it off with the origin story of Chai.

William [00:01:19]: Why decide working on a consumer AI platform rather than B2B SaaS? So just quickly touching on the background in finance. Sure. Originally, I'm from... I'm from the UK, born in London. And I was fortunate enough to go study economics at Cambridge. And I graduated in 2012. And at that time, everyone in the UK and everyone on my course, HFT, quant trading was really the big thing. It was like the big wave that was happening. So there was a lot of opportunity in that space. And throughout college, I'd sort of played poker. So I'd, you know, I dabbled as a professional poker player. And I was able to accumulate this sort of, you know, say $100,000 through playing poker. And at the time, as my friends would go work at companies like ChangeStreet or Citadel, I kind of did the maths. And I just thought, well, maybe if I traded my own capital, I'd probably come out ahead. I'd make more money than just going to work at ChangeStreet.

swyx [00:02:20]: With 100k base as capital?

William [00:02:22]: Yes, yes. That's not a lot. Well, it depends what strategies you're doing. And, you know, there is an advantage. There's an advantage to being small, right? Because there are, if you have a 10... Strategies that don't work in size. Exactly, exactly. So if you have a fund of $10 million, if you find a little anomaly in the market that you might be able to make 100k a year from, that's a 1% return on your 10 million fund. If your fund is 100k, that's 100% return, right? So being small, in some sense, was an advantage. So started off, and the, taught myself Python, and machine learning was like the big thing as well. Machine learning had really, it was the first, you know, big time machine learning was being used for image recognition, neural networks come out, you get dropout. And, you know, so this, this was the big thing that's going on at the time. So I probably spent my first three years out of Cambridge, just building neural networks, building random forests to try and predict asset prices, right, and then trade that using my own money. And that went well. And, you know, if you if you start something, and it goes well, you You try and hire more people. And the first people that came to mind was the talented people I went to college with. And so I hired some friends. And that went well and hired some more. And eventually, I kind of ran out of friends to hire. And so that was when I formed the company. And from that point on, we had our ups and we had our downs. And that was a whole long story and journey in itself. But after doing that for about eight or nine years, on my 30th birthday, which was four years ago now, I kind of took a step back to just evaluate my life, right? This is what one does when one turns 30. You know, I just heard it. I hear you. And, you know, I looked at my 20s and I loved it. It was a really special time. I was really lucky and fortunate to have worked with this amazing team, been successful, had a lot of hard times. And through the hard times, learned wisdom and then a lot of success and, you know, was able to enjoy it. And so the company was making about five million pounds a year. And it was just me and a team of, say, 15, like, Oxford and Cambridge educated mathematicians and physicists. It was like the real dream that you'd have if you wanted to start a quant trading firm. It was like...

swyx [00:04:40]: Your own, all your own money?

William [00:04:41]: Yeah, exactly. It was all the team's own money. We had no customers complaining to us about issues. There's no investors, you know, saying, you know, they don't like the risk that we're taking. We could. We could really run the thing exactly as we wanted it. It's like Susquehanna or like Rintec. Yeah, exactly. Yeah. And they're the companies that we would kind of look towards as we were building that thing out. But on my 30th birthday, I look and I say, OK, great. This thing is making as much money as kind of anyone would really need. And I thought, well, what's going to happen if we keep going in this direction? And it was clear that we would never have a kind of a big, big impact on the world. We can enrich ourselves. We can make really good money. Everyone on the team would be paid very, very well. Presumably, I can make enough money to buy a yacht or something. But this stuff wasn't that important to me. And so I felt a sort of obligation that if you have this much talent and if you have a talented team, especially as a founder, you want to be putting all that talent towards a good use. I looked at the time of like getting into crypto and I had a really strong view on crypto, which was that as far as a gambling device. This is like the most fun form of gambling invented in like ever super fun, I thought as a way to evade monetary regulations and banking restrictions. I think it's also absolutely amazing. So it has two like killer use cases, not so much banking the unbanked, but everything else, but everything else to do with like the blockchain and, and you know, web, was it web 3.0 or web, you know, that I, that didn't, it didn't really make much sense. And so instead of going into crypto, which I thought, even if I was successful, I'd end up in a lot of trouble. I thought maybe it'd be better to build something that governments wouldn't have a problem with. I knew that LLMs were like a thing. I think opening. I had said they hadn't released GPT-3 yet, but they'd said GPT-3 is so powerful. We can't release it to the world or something. Was it GPT-2? And then I started interacting with, I think Google had open source, some language models. They weren't necessarily LLMs, but they, but they were. But yeah, exactly. So I was able to play around with, but nowadays so many people have interacted with the chat GPT, they get it, but it's like the first time you, you can just talk to a computer and it talks back. It's kind of a special moment and you know, everyone who's done that goes like, wow, this is how it should be. Right. It should be like, rather than having to type on Google and search, you should just be able to ask Google a question. When I saw that I read the literature, I kind of came across the scaling laws and I think even four years ago. All the pieces of the puzzle were there, right? Google had done this amazing research and published, you know, a lot of it. Open AI was still open. And so they'd published a lot of their research. And so you really could be fully informed on, on the state of AI and where it was going. And so at that point I was confident enough, it was worth a shot. I think LLMs are going to be the next big thing. And so that's the thing I want to be building in, in that space. And I thought what's the most impactful product I can possibly build. And I thought it should be a platform. So I myself love platforms. I think they're fantastic because they open up an ecosystem where anyone can contribute to it. Right. So if you think of a platform like a YouTube, instead of it being like a Hollywood situation where you have to, if you want to make a TV show, you have to convince Disney to give you the money to produce it instead, anyone in the world can post any content they want to YouTube. And if people want to view it, the algorithm is going to promote it. Nowadays. You can look at creators like Mr. Beast or Joe Rogan. They would have never have had that opportunity unless it was for this platform. Other ones like Twitter's a great one, right? But I would consider Wikipedia to be a platform where instead of the Britannica encyclopedia, which is this, it's like a monolithic, you get all the, the researchers together, you get all the data together and you combine it in this, in this one monolithic source. Instead. You have this distributed thing. You can say anyone can host their content on Wikipedia. Anyone can contribute to it. And anyone can maybe their contribution is they delete stuff. When I was hearing like the kind of the Sam Altman and kind of the, the Muskian perspective of AI, it was a very kind of monolithic thing. It was all about AI is basically a single thing, which is intelligence. Yeah. Yeah. The more intelligent, the more compute, the more intelligent, and the more and better AI researchers, the more intelligent, right? They would speak about it as a kind of erased, like who can get the most data, the most compute and the most researchers. And that would end up with the most intelligent AI. But I didn't believe in any of that. I thought that's like the total, like I thought that perspective is the perspective of someone who's never actually done machine learning. Because with machine learning, first of all, you see that the performance of the models follows an S curve. So it's not like it just goes off to infinity, right? And the, the S curve, it kind of plateaus around human level performance. And you can look at all the, all the machine learning that was going on in the 2010s, everything kind of plateaued around the human level performance. And we can think about the self-driving car promises, you know, how Elon Musk kept saying the self-driving car is going to happen next year, it's going to happen next, next year. Or you can look at the image recognition, the speech recognition. You can look at. All of these things, there was almost nothing that went superhuman, except for something like AlphaGo. And we can speak about why AlphaGo was able to go like super superhuman. So I thought the most likely thing was going to be this, I thought it's not going to be a monolithic thing. That's like an encyclopedia Britannica. I thought it must be a distributed thing. And I actually liked to look at the world of finance for what I think a mature machine learning ecosystem would look like. So, yeah. So finance is a machine learning ecosystem because all of these quant trading firms are running machine learning algorithms, but they're running it on a centralized platform like a marketplace. And it's not the case that there's one giant quant trading company of all the data and all the quant researchers and all the algorithms and compute, but instead they all specialize. So one will specialize on high frequency training. Another will specialize on mid frequency. Another one will specialize on equity. Another one will specialize. And I thought that's the way the world works. That's how it is. And so there must exist a platform where a small team can produce an AI for a unique purpose. And they can iterate and build the best thing for that, right? And so that was the vision for Chai. So we wanted to build a platform for LLMs.

Alessio [00:11:36]: That's kind of the maybe inside versus contrarian view that led you to start the company. Yeah. And then what was maybe the initial idea maze? Because if somebody told you that was the Hugging Face founding story, people might believe it. It's kind of like a similar ethos behind it. How did you land on the product feature today? And maybe what were some of the ideas that you discarded that initially you thought about?

William [00:11:58]: So the first thing we built, it was fundamentally an API. So nowadays people would describe it as like agents, right? But anyone could write a Python script. They could submit it to an API. They could send it to the Chai backend and we would then host this code and execute it. So that's like the developer side of the platform. On their Python script, the interface was essentially text in and text out. An example would be the very first bot that I created. I think it was a Reddit news bot. And so it would first, it would pull the popular news. Then it would prompt whatever, like I just use some external API for like Burr or GPT-2 or whatever. Like it was a very, very small thing. And then the user could talk to it. So you could say to the bot, hi bot, what's the news today? And it would say, this is the top stories. And you could chat with it. Now four years later, that's like perplexity or something. That's like the, right? But back then the models were first of all, like really, really dumb. You know, they had an IQ of like a four year old. And users, there really wasn't any demand or any PMF for interacting with the news. So then I was like, okay. Um. So let's make another one. And I made a bot, which was like, you could talk to it about a recipe. So you could say, I'm making eggs. Like I've got eggs in my fridge. What should I cook? And it'll say, you should make an omelet. Right. There was no PMF for that. No one used it. And so I just kept creating bots. And so every single night after work, I'd be like, okay, I like, we have AI, we have this platform. I can create any text in textile sort of agent and put it on the platform. And so we just create stuff night after night. And then all the coders I knew, I would say, yeah, this is what we're going to do. And then I would say to them, look, there's this platform. You can create any like chat AI. You should put it on. And you know, everyone's like, well, chatbots are super lame. We want absolutely nothing to do with your chatbot app. No one who knew Python wanted to build on it. I'm like trying to build all these bots and no consumers want to talk to any of them. And then my sister who at the time was like just finishing college or something, I said to her, I was like, if you want to learn Python, you should just submit a bot for my platform. And she, she built a therapy for me. And I was like, okay, cool. I'm going to build a therapist bot. And then the next day I checked the performance of the app and I'm like, oh my God, we've got 20 active users. And they spent, they spent like an average of 20 minutes on the app. I was like, oh my God, what, what bot were they speaking to for an average of 20 minutes? And I looked and it was the therapist bot. And I went, oh, this is where the PMF is. There was no demand for, for recipe help. There was no demand for news. There was no demand for dad jokes or pub quiz or fun facts or what they wanted was they wanted the therapist bot. the time I kind of reflected on that and I thought, well, if I want to consume news, the most fun thing, most fun way to consume news is like Twitter. It's not like the value of there being a back and forth, wasn't that high. Right. And I thought if I need help with a recipe, I actually just go like the New York times has a good recipe section, right? It's not actually that hard. And so I just thought the thing that AI is 10 X better at is a sort of a conversation right. That's not intrinsically informative, but it's more about an opportunity. You can say whatever you want. You're not going to get judged. If it's 3am, you don't have to wait for your friend to text back. It's like, it's immediate. They're going to reply immediately. You can say whatever you want. It's judgment-free and it's much more like a playground. It's much more like a fun experience. And you could see that if the AI gave a person a compliment, they would love it. It's much easier to get the AI to give you a compliment than a human. From that day on, I said, okay, I get it. Humans want to speak to like humans or human like entities and they want to have fun. And that was when I started to look less at platforms like Google. And I started to look more at platforms like Instagram. And I was trying to think about why do people use Instagram? And I could see that I think Chai was, was filling the same desire or the same drive. If you go on Instagram, typically you want to look at the faces of other humans, or you want to hear about other people's lives. So if it's like the rock is making himself pancakes on a cheese plate. You kind of feel a little bit like you're the rock's friend, or you're like having pancakes with him or something, right? But if you do it too much, you feel like you're sad and like a lonely person, but with AI, you can talk to it and tell it stories and tell you stories, and you can play with it for as long as you want. And you don't feel like you're like a sad, lonely person. You feel like you actually have a friend.

Alessio [00:16:29]: And what, why is that? Do you have any insight on that from using it?

William [00:16:33]: I think it's just the human psychology. I think it's just the idea that, with old school social media. You're just consuming passively, right? So you'll just swipe. If I'm watching TikTok, just like swipe and swipe and swipe. And even though I'm getting the dopamine of like watching an engaging video, there's this other thing that's building my head, which is like, I'm feeling lazier and lazier and lazier. And after a certain period of time, I'm like, man, I just wasted 40 minutes. I achieved nothing. But with AI, because you're interacting, you feel like you're, it's not like work, but you feel like you're participating and contributing to the thing. You don't feel like you're just. Consuming. So you don't have a sense of remorse basically. And you know, I think on the whole people, the way people talk about, try and interact with the AI, they speak about it in an incredibly positive sense. Like we get people who say they have eating disorders saying that the AI helps them with their eating disorders. People who say they're depressed, it helps them through like the rough patches. So I think there's something intrinsically healthy about interacting that TikTok and Instagram and YouTube doesn't quite tick. From that point on, it was about building more and more kind of like human centric AI for people to interact with. And I was like, okay, let's make a Kanye West bot, right? And then no one wanted to talk to the Kanye West bot. And I was like, ah, who's like a cool persona for teenagers to want to interact with. And I was like, I was trying to find the influencers and stuff like that, but no one cared. Like they didn't want to interact with the, yeah. And instead it was really just the special moment was when we said the realization that developers and software engineers aren't interested in building this sort of AI, but the consumers are right. And rather than me trying to guess every day, like what's the right bot to submit to the platform, why don't we just create the tools for the users to build it themselves? And so nowadays this is like the most obvious thing in the world, but when Chai first did it, it was not an obvious thing at all. Right. Right. So we took the API for let's just say it was, I think it was GPTJ, which was this 6 billion parameter open source transformer style LLM. We took GPTJ. We let users create the prompt. We let users select the image and we let users choose the name. And then that was the bot. And through that, they could shape the experience, right? So if they said this bot's going to be really mean, and it's going to be called like bully in the playground, right? That was like a whole category that I never would have guessed. Right. People love to fight. They love to have a disagreement, right? And then they would create, there'd be all these romantic archetypes that I didn't know existed. And so as the users could create the content that they wanted, that was when Chai was able to, to get this huge variety of content and rather than appealing to, you know, 1% of the population that I'd figured out what they wanted, you could appeal to a much, much broader thing. And so from that moment on, it was very, very crystal clear. It's like Chai, just as Instagram is this social media platform that lets people create images and upload images, videos and upload that, Chai was really about how can we let the users create this experience in AI and then share it and interact and search. So it's really, you know, I say it's like a platform for social AI.

Alessio [00:20:00]: Where did the Chai name come from? Because you started the same path. I was like, is it character AI shortened? You started at the same time, so I was curious. The UK origin was like the second, the Chai.

William [00:20:15]: We started way before character AI. And there's an interesting story that Chai's numbers were very, very strong, right? So I think in even 20, I think late 2022, was it late 2022 or maybe early 2023? Chai was like the number one AI app in the app store. So we would have something like 100,000 daily active users. And then one day we kind of saw there was this website. And we were like, oh, this website looks just like Chai. And it was the character AI website. And I think that nowadays it's, I think it's much more common knowledge that when they left Google with the funding, I think they knew what was the most trending, the number one app. And I think they sort of built that. Oh, you found the people.

swyx [00:21:03]: You found the PMF for them.

William [00:21:04]: We found the PMF for them. Exactly. Yeah. So I worked a year very, very hard. And then they, and then that was when I learned a lesson, which is that if you're VC backed and if, you know, so Chai, we'd kind of ran, we'd got to this point, I was the only person who'd invested. I'd invested maybe 2 million pounds in the business. And you know, from that, we were able to build this thing, get to say a hundred thousand daily active users. And then when character AI came along, the first version, we sort of laughed. We were like, oh man, this thing sucks. Like they don't know what they're building. They're building the wrong thing anyway, but then I saw, oh, they've raised a hundred million dollars. Oh, they've raised another hundred million dollars. And then our users started saying, oh guys, your AI sucks. Cause we were serving a 6 billion parameter model, right? How big was the model that character AI could afford to serve, right? So we would be spending, let's say we would spend a dollar per per user, right? Over the, the, you know, the entire lifetime.

swyx [00:22:01]: A dollar per session, per chat, per month? No, no, no, no.

William [00:22:04]: Let's say we'd get over the course of the year, we'd have a million users and we'd spend a million dollars on the AI throughout the year. Right. Like aggregated. Exactly. Exactly. Right. They could spend a hundred times that. So people would say, why is your AI much dumber than character AIs? And then I was like, oh, okay, I get it. This is like the Silicon Valley style, um, hyper scale business. And so, yeah, we moved to Silicon Valley and, uh, got some funding and iterated and built the flywheels. And, um, yeah, I, I'm very proud that we were able to compete with that. Right. So, and I think the reason we were able to do it was just customer obsession. And it's similar, I guess, to how deep seek have been able to produce such a compelling model when compared to someone like an open AI, right? So deep seek, you know, their latest, um, V2, yeah, they claim to have spent 5 million training it.

swyx [00:22:57]: It may be a bit more, but, um, like, why are you making it? Why are you making such a big deal out of this? Yeah. There's an agenda there. Yeah. You brought up deep seek. So we have to ask you had a call with them.

William [00:23:07]: We did. We did. We did. Um, let me think what to say about that. I think for one, they have an amazing story, right? So their background is again in finance.

swyx [00:23:16]: They're the Chinese version of you. Exactly.

William [00:23:18]: Well, there's a lot of similarities. Yes. Yes. I have a great affinity for companies which are like, um, founder led, customer obsessed and just try and build something great. And I think what deep seek have achieved. There's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cash significantly. And then by being able to do that, they're able to significantly reduce their inference costs. And I think with kind of with AI, people get really focused on like the kind of the foundation model or like the model itself. And they sort of don't pay much attention to the inference. To give you an example with Chai, let's say a typical user session is 90 minutes, which is like, you know, is very, very long for comparison. Let's say the average session length on TikTok is 70 minutes. So people are spending a lot of time. And in that time they're able to send say 150 messages. That's a lot of completions, right? It's quite different from an open AI scenario where people might come in, they'll have a particular question in mind. And they'll ask like one question. And a few follow up questions, right? So because they're consuming, say 30 times as many requests for a chat, or a conversational experience, you've got to figure out how to how to get the right balance between the cost of that and the quality. And so, you know, I think with AI, it's always been the case that if you want a better experience, you can throw compute at the problem, right? So if you want a better model, you can just make it bigger. If you want it to remember better, give it a longer context. And now, what open AI is doing to great fanfare is with projection sampling, you can generate many candidates, right? And then with some sort of reward model or some sort of scoring system, you can serve the most promising of these many candidates. And so that's kind of scaling up on the inference time compute side of things. And so for us, it doesn't make sense to think of AI is just the absolute performance. So. But what we're seeing, it's like the MML you score or the, you know, any of these benchmarks that people like to look at, if you just get that score, it doesn't really tell tell you anything. Because it's really like progress is made by improving the performance per dollar. And so I think that's an area where deep seek have been able to form very, very well, surprisingly so. And so I'm very interested in what Lama four is going to look like. And if they're able to sort of match what deep seek have been able to achieve with this performance per dollar gain.

Alessio [00:25:59]: Before we go into the inference, some of the deeper stuff, can you give people an overview of like some of the numbers? So I think last I checked, you have like 1.4 million daily active now. It's like over 22 million of revenue. So it's quite a business.

William [00:26:12]: Yeah, I think we grew by a factor of, you know, users grew by a factor of three last year. Revenue over doubled. You know, it's very exciting. We're competing with some really big, really well funded companies. Character AI got this, I think it was almost a $3 billion valuation. And they have 5 million DAU is a number that I last heard. Torquay, which is a Chinese built app owned by a company called Minimax. They're incredibly well funded. And these companies didn't grow by a factor of three last year. Right. And so when you've got this company and this team that's able to keep building something that gets users excited, and they want to tell their friend about it, and then they want to come and they want to stick on the platform. I think that's very special. And so last year was a great year for the team. And yeah, I think the numbers reflect the hard work that we put in. And then fundamentally, the quality of the app, the quality of the content, the quality of the content, the quality of the content, the quality of the content, the quality of the content. AI is the quality of the experience that you have. You actually published your DAU growth chart, which is unusual. And I see some inflections. Like, it's not just a straight line. There's some things that actually inflect. Yes. What were the big ones? Cool. That's a great, great, great question. Let me think of a good answer. I'm basically looking to annotate this chart, which doesn't have annotations on it. Cool. The first thing I would say is this is, I think the most important thing to know about success is that success is born out of failures. Right? Through failures that we learn. You know, if you think something's a good idea, and you do and it works, great, but you didn't actually learn anything, because everything went exactly as you imagined. But if you have an idea, you think it's going to be good, you try it, and it fails. There's a gap between the reality and expectation. And that's an opportunity to learn. The flat periods, that's us learning. And then the up periods is that's us reaping the rewards of that. So I think the big, of the growth shot of just 2024, I think the first thing that really kind of put a dent in our growth was our backend. So we just reached this scale. So we'd, from day one, we'd built on top of Google's GCP, which is Google's cloud platform. And they were fantastic. We used them when we had one daily active user, and they worked pretty good all the way up till we had about 500,000. It was never the cheapest, but from an engineering perspective, man, that thing scaled insanely good. Like, not Vertex? Not Vertex. Like GKE, that kind of stuff? We use Firebase. So we use Firebase. I'm pretty sure we're the biggest user ever on Firebase. That's expensive. Yeah, we had calls with engineers, and they're like, we wouldn't recommend using this product beyond this point, and you're 3x over that. So we pushed Google to their absolute limits. You know, it was fantastic for us, because we could focus on the AI. We could focus on just adding as much value as possible. But then what happened was, after 500,000, just the thing, the way we were using it, and it would just, it wouldn't scale any further. And so we had a really, really painful, at least three-month period, as we kind of migrated between different services, figuring out, like, what requests do we want to keep on Firebase, and what ones do we want to move on to something else? And then, you know, making mistakes. And learning things the hard way. And then after about three months, we got that right. So that, we would then be able to scale to the 1.5 million DAE without any further issues from the GCP. But what happens is, if you have an outage, new users who go on your app experience a dysfunctional app, and then they're going to exit. And so your next day, the key metrics that the app stores track are going to be something like retention rates. And so your next day, the key metrics that the app stores track are going to be something like retention rates. Money spent, and the star, like, the rating that they give you. In the app store. In the app store, yeah. Tyranny. So if you're ranked top 50 in entertainment, you're going to acquire a certain rate of users organically. If you go in and have a bad experience, it's going to tank where you're positioned in the algorithm. And then it can take a long time to kind of earn your way back up, at least if you wanted to do it organically. If you throw money at it, you can jump to the top. And I could talk about that. But broadly speaking, if we look at 2024, the first kink in the graph was outages due to hitting 500k DAU. The backend didn't want to scale past that. So then we just had to do the engineering and build through it. Okay, so we built through that, and then we get a little bit of growth. And so, okay, that's feeling a little bit good. I think the next thing, I think it's, I'm not going to lie, I have a feeling that when Character AI got... I was thinking. I think so. I think... So the Character AI team fundamentally got acquired by Google. And I don't know what they changed in their business. I don't know if they dialed down that ad spend. Products don't change, right? Products just what it is. I don't think so. Yeah, I think the product is what it is. It's like maintenance mode. Yes. I think the issue that people, you know, some people may think this is an obvious fact, but running a business can be very competitive, right? Because other businesses can see what you're doing, and they can imitate you. And then there's this... There's this question of, if you've got one company that's spending $100,000 a day on advertising, and you've got another company that's spending zero, if you consider market share, and if you're considering new users which are entering the market, the guy that's spending $100,000 a day is going to be getting 90% of those new users. And so I have a suspicion that when the founders of Character AI left, they dialed down their spending on user acquisition. And I think that kind of gave oxygen to like the other apps. And so Chai was able to then start growing again in a really healthy fashion. I think that's kind of like the second thing. I think a third thing is we've really built a great data flywheel. Like the AI team sort of perfected their flywheel, I would say, in end of Q2. And I could speak about that at length. But fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours. And when we did that, we could really, really, really perfect techniques like DPO, fine tuning, prompt engineering, blending, rejection sampling, training a reward model, right, really successfully, like boom, boom, boom, boom, boom. And so I think in Q3 and Q4, we got, the amount of AI improvements we got was like astounding. It was getting to the point, I thought like how much more, how much more edge is there to be had here? But the team just could keep going and going and going. That was like number three for the inflection point.

swyx [00:34:53]: There's a fourth?

William [00:34:54]: The important thing about the third one is if you go on our Reddit or you talk to users of AI, there's like a clear date. It's like somewhere in October or something. The users, they flipped. Before October, the users... The users would say character AI is better than you, for the most part. Then from October onwards, they would say, wow, you guys are better than character AI. And that was like a really clear positive signal that we'd sort of done it. And I think people, you can't cheat consumers. You can't trick them. You can't b******t them. They know, right? If you're going to spend 90 minutes on a platform, and with apps, there's the barriers to switching is pretty low. Like you can try character AI, you can't cheat consumers. You can't cheat them. You can't cheat them. You can't cheat AI for a day. If you get bored, you can try Chai. If you get bored of Chai, you can go back to character. So the users, the loyalty is not strong, right? What keeps them on the app is the experience. If you deliver a better experience, they're going to stay and they can tell. So that was the fourth one was we were fortunate enough to get this hire. He was hired one really talented engineer. And then they said, oh, at my last company, we had a head of growth. He was really, really good. And he was the head of growth for ByteDance for two years. Would you like to speak to him? And I was like, yes. Yes, I think I would. And so I spoke to him. And he just blew me away with what he knew about user acquisition. You know, it was like a 3D chess

swyx [00:36:21]: sort of thing. You know, as much as, as I know about AI. Like ByteDance as in TikTok US. Yes.

William [00:36:26]: Not ByteDance as other stuff. Yep. He was interviewing us as we were interviewing him. Right. And so pick up options. Yeah, exactly. And so he was kind of looking at our metrics. And he was like, I saw him get really excited when he said, guys, you've got a million daily active users and you've done no advertising. I said, correct. And he was like, that's unheard of. He's like, I've never heard of anyone doing that. And then he started looking at our metrics. And he was like, if you've got all of this organically, if you start spending money, this is going to be very exciting. I was like, let's give it a go. So then he came in, we've just started ramping up the user acquisition. So that looks like spending, you know, let's say we're spending, we started spending $20,000 a day, it looked very promising than 20,000. Right now we're spending $40,000 a day on user acquisition. That's still only half of what like character AI or talkie may be spending. But from that, it's sort of, we were growing at a rate of maybe say, 2x a year. And that got us growing at a rate of 3x a year. So I'm growing, I'm evolving more and more to like a Silicon Valley style hyper growth, like, you know, you build something decent, and then you can

swyx [00:37:33]: slap on a huge... You did the important thing, you did the product first.

William [00:37:36]: Of course, but then you can slap on like, like the rocket or the jet engine or something, which is just this cash in, you pour in as much cash, you buy a lot of ads, and your growth is faster.

swyx [00:37:48]: Not to, you know, I'm just kind of curious what's working right now versus what surprisingly

William [00:37:52]: doesn't work. Oh, there's a long, long list of surprising stuff that doesn't work. Yeah. The surprising thing, like the most surprising thing, what doesn't work is almost everything doesn't work. That's what's surprising. And I'll give you an example. So like a year and a half ago, I was working at a company, we were super excited by audio. I was like, audio is going to be the next killer feature, we have to get in the app. And I want to be the first. So everything Chai does, I want us to be the first. We may not be the company that's strongest at execution, but we can always be the

swyx [00:38:22]: most innovative. Interesting. Right? So we can... You're pretty strong at execution.

William [00:38:26]: We're much stronger, we're much stronger. A lot of the reason we're here is because we were first. If we launched today, it'd be so hard to get the traction. Because it's like to get the flywheel, to get the users, to build a product people are excited about. If you're first, people are naturally excited about it. But if you're fifth or 10th, man, you've got to be

swyx [00:38:46]: insanely good at execution. So you were first with voice? We were first. We were first. I only know

William [00:38:51]: when character launched voice. They launched it, I think they launched it at least nine months after us. Okay. Okay. But the team worked so hard for it. At the time we did it, latency is a huge problem. Cost is a huge problem. Getting the right quality of the voice is a huge problem. Right? Then there's this user interface and getting the right user experience. Because you don't just want it to start blurting out. Right? You want to kind of activate it. But then you don't have to keep pressing a button every single time. There's a lot that goes into getting a really smooth audio experience. So we went ahead, we invested the three months, we built it all. And then when we did the A-B test, there was like, no change in any of the numbers. And I was like, this can't be right, there must be a bug. And we spent like a week just checking everything, checking again, checking again. And it was like, the users just did not care. And it was something like only 10 or 15% of users even click the button to like, they wanted to engage the audio. And they would only use it for 10 or 15% of the time. So if you do the math, if it's just like something that one in seven people use it for one seventh of their time. You've changed like 2% of the experience. So even if that that 2% of the time is like insanely good, it doesn't translate much when you look at the retention, when you look at the engagement, and when you look at the monetization rates. So audio did not have a big impact. I'm pretty big on audio. But yeah, I like it too. But it's, you know, so a lot of the stuff which I do, I'm a big, you can have a theory. And you resist. Yeah. Exactly, exactly. So I think if you want to make audio work, it has to be a unique, compelling, exciting experience that they can't have anywhere else.

swyx [00:40:37]: It could be your models, which just weren't good enough.

William [00:40:39]: No, no, no, they were great. Oh, yeah, they were very good. it was like, it was kind of like just the, you know, if you listen to like an audible or Kindle, or something like, you just hear this voice. And it's like, you don't go like, wow, this is this is special, right? It's like a convenience thing. But the idea is that if you can, if Chai is the only platform, like, let's say you have a Mr. Beast, and YouTube is the only platform you can use to make audio work, then you can watch a Mr. Beast video. And it's the most engaging, fun video that you want to watch, you'll go to a YouTube. And so it's like for audio, you can't just put the audio on there. And people go, oh, yeah, it's like 2% better. Or like, 5% of users think it's 20% better, right? It has to be something that the majority of people, for the majority of the experience, go like, wow, this is a big deal. That's the features you need to be shipping. If it's not going to appeal to the majority of people, for the majority of the experience, and it's not a big deal, it's not going to move you. Cool. So you killed it. I don't see it anymore. Yep. So I love this. The longer, it's kind of cheesy, I guess, but the longer I've been working at Chai, and I think the team agrees with this, all the platitudes, at least I thought they were platitudes, that you would get from like the Steve Jobs, which is like, build something insanely great, right? Or be maniacally focused, or, you know, the most important thing is saying no to, not to work on. All of these sort of lessons, they just are like painfully true. They're painfully true. So now I'm just like, everything I say, I'm either quoting Steve Jobs or Zuckerberg. I'm like, guys, move fast and break free.

swyx [00:42:10]: You've jumped the Apollo to cool it now.

William [00:42:12]: Yeah, it's just so, everything they said is so, so true. The turtle neck. Yeah, yeah, yeah. Everything is so true.

swyx [00:42:18]: This last question on my side, and I want to pass this to Alessio, is on just, just multi-modality in general. This actually comes from Justine Moore from A16Z, who's a friend of ours. And a lot of people are trying to do voice image video for AI companions. Yes. You just said voice didn't work. Yep. What would make you revisit?

William [00:42:36]: So Steve Jobs, he was very, listen, he was very, very clear on this. There's a habit of engineers who, once they've got some cool technology, they want to find a way to package up the cool technology and sell it to consumers, right? That does not work. So you're free to try and build a startup where you've got your cool tech and you want to find someone to sell it to. That's not what we do at Chai. At Chai, we start with the consumer. What does the consumer want? What is their problem? And how do we solve it? So right now, the number one problems for the users, it's not the audio. That's not the number one problem. It's not the image generation either. That's not their problem either. The number one problem for users in AI is this. All the AI is being generated by middle-aged men in Silicon Valley, right? That's all the content. You're interacting with this AI. You're speaking to it for 90 minutes on average. It's being trained by middle-aged men. The guys out there, they're out there. They're talking to you. They're talking to you. They're like, oh, what should the AI say in this situation, right? What's funny, right? What's cool? What's boring? What's entertaining? That's not the way it should be. The way it should be is that the users should be creating the AI, right? And so the way I speak about it is this. Chai, we have this AI engine in which sits atop a thin layer of UGC. So the thin layer of UGC is absolutely essential, right? It's just prompts. But it's just prompts. It's just an image. It's just a name. It's like we've done 1% of what we could do. So we need to keep thickening up that layer of UGC. It must be the case that the users can train the AI. And if reinforcement learning is powerful and important, they have to be able to do that. And so it's got to be the case that there exists, you know, I say to the team, just as Mr. Beast is able to spend 100 million a year or whatever it is on his production company, and he's got a team building the content, the Mr. Beast company is able to spend 100 million a year on his production company. And he's got a team building the content, which then he shares on the YouTube platform. Until there's a team that's earning 100 million a year or spending 100 million on the content that they're producing for the Chai platform, we're not finished, right? So that's the problem. That's what we're excited to build. And getting too caught up in the tech, I think is a fool's errand. It does not work.

Alessio [00:44:52]: As an aside, I saw the Beast Games thing on Amazon Prime. It's not doing well. And I'm

swyx [00:44:56]: curious. It's kind of like, I mean, the audience reading is high. The run-to-meet-all sucks, but the audience reading is high.

Alessio [00:45:02]: But it's not like in the top 10. I saw it dropped off of like the... Oh, okay. Yeah, that one I don't know. I'm curious, like, you know, it's kind of like similar content, but different platform. And then going back to like, some of what you were saying is like, you know, people come to Chai

William [00:45:13]: expecting some type of content. Yeah, I think it's something that's interesting to discuss is like, is moats. And what is the moat? And so, you know, if you look at a platform like YouTube, the moat, I think is in first is really is in the ecosystem. And the ecosystem, is comprised of you have the content creators, you have the users, the consumers, and then you have the algorithms. And so this, this creates a sort of a flywheel where the algorithms are able to be trained on the users, and the users data, the recommend systems can then feed information to the content creators. So Mr. Beast, he knows which thumbnail does the best. He knows the first 10 seconds of the video has to be this particular way. And so his content is super optimized for the YouTube platform. So that's why it doesn't do well on Amazon. If he wants to do well on Amazon, how many videos has he created on the YouTube platform? By thousands, 10s of 1000s, I guess, he needs to get those iterations in on the Amazon. So at Chai, I think it's all about how can we get the most compelling, rich user generated content, stick that on top of the AI engine, the recommender systems, in such that we get this beautiful data flywheel, more users, better recommendations, more creative, more content, more users.

Alessio [00:46:34]: You mentioned the algorithm, you have this idea of the Chaiverse on Chai, and you have your own kind of like LMSYS-like ELO system. Yeah, what are things that your models optimize for, like your users optimize for, and maybe talk about how you build it, how people submit models?

William [00:46:49]: So Chaiverse is what I would describe as a developer platform. More often when we're speaking about Chai, we're thinking about the Chai app. And the Chai app is really this product for consumers. And so consumers can come on the Chai app, they can come on the Chai app, they can come on the Chai app, they can interact with our AI, and they can interact with other UGC. And it's really just these kind of bots. And it's a thin layer of UGC. Okay. Our mission is not to just have a very thin layer of UGC. Our mission is to have as much UGC as possible. So we must have, I don't want people at Chai training the AI. I want people, not middle aged men, building AI. I want everyone building the AI, as many people building the AI as possible. Okay, so what we built was we built Chaiverse. And Chaiverse is kind of, it's kind of like a prototype, is the way to think about it. And it started with this, this observation that, well, how many models get submitted into Hugging Face a day? It's hundreds, it's hundreds, right? So there's hundreds of LLMs submitted each day. Now consider that, what does it take to build an LLM? It takes a lot of work, actually. It's like someone devoted several hours of compute, several hours of their time, prepared a data set, launched it, ran it, evaluated it, submitted it, right? So there's a lot of, there's a lot of, there's a lot of work that's going into that. So what we did was we said, well, why can't we host their models for them and serve them to users? And then what would that look like? The first issue is, well, how do you know if a model is good or not? Like, we don't want to serve users the crappy models, right? So what we would do is we would, I love the LMSYS style. I think it's really cool. It's really simple. It's a very intuitive thing, which is you simply present the users with two completions. You can say, look, this is from model one. This is from model two. This is from model three. This is from model A. This is from model B, which is better. And so if someone submits a model to Chaiverse, what we do is we spin up a GPU. We download the model. We're going to now host that model on this GPU. And we're going to start routing traffic to it. And we're going to send, we think it takes about 5,000 completions to get an accurate signal. That's roughly what LMSYS does. And from that, we're able to get an accurate ranking. And we're able to get an accurate ranking. And we're able to get an accurate ranking of which models are people finding entertaining and which models are not entertaining. If you look at the bottom 80%, they'll suck. You can just disregard them. They totally suck. Then when you get the top 20%, you know you've got a decent model, but you can break it down into more nuance. There might be one that's really descriptive. There might be one that's got a lot of personality to it. There might be one that's really illogical. Then the question is, well, what do you do with these top models? From that, you can do more sophisticated things. You can try and do like a routing thing where you say for a given user request, we're going to try and predict which of these end models that users enjoy the most. That turns out to be pretty expensive and not a huge source of like edge or improvement. Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model. Just a random 50%? Just a random, yeah. And then... That's blending? That's blending. You can do more sophisticated things on top of that, as in all things in life, but the 80-20 solution, if you just do that, you get a pretty powerful effect out of the gate. Random number generator. I think it's like the robustness of randomness. Random is a very powerful optimization technique, and it's a very robust thing. So you can explore a lot of the space very efficiently. There's one thing that's really, really important to share, and this is the most exciting thing for me, is after you do the ranking, you get an ELO score, and you can track a user's first join date, the first date they submit a model to Chaiverse, they almost always get a terrible ELO, right? So let's say the first submission they get an ELO of 1,100 or 1,000 or something, and you can see that they iterate and they iterate and iterate, and it will be like, no improvement, no improvement, no improvement, and then boom. Do you give them any data, or do you have to come up with this themselves? We do, we do, we do, we do. We try and strike a balance between giving them data that's very useful, you've got to be compliant with GDPR, which is like, you have to work very hard to preserve the privacy of users of your app. So we try to give them as much signal as possible, to be helpful. The minimum is we're just going to give you a score, right? That's the minimum. But that alone is people can optimize a score pretty well, because they're able to come up with theories, submit it, does it work? No. A new theory, does it work? No. And then boom, as soon as they figure something out, they keep it, and then they iterate, and then boom,

Alessio [00:51:46]: they figure something out, and they keep it. Last year, you had this post on your blog, cross-sourcing the lead to the 10 trillion parameter, AGI, and you call it a mixture of experts, recommenders. Yep. Any insights?

William [00:51:58]: Updated thoughts, 12 months later? I think the odds, the timeline for AGI has certainly been pushed out, right? Now, this is in, I'm a controversial person, I don't know, like, I just think... You don't believe in scaling laws, you think AGI is further away. I think it's an S-curve. I think everything's an S-curve. And I think that the models have proven to just be far worse at reasoning than people sort of thought. And I think whenever I hear people talk about LLMs as reasoning engines, I sort of cringe a bit. I don't think that's what they are. I think of them more as like a simulator. I think of them as like a, right? So they get trained to predict the next most likely token. It's like a physics simulation engine. So you get these like games where you can like construct a bridge, and you drop a car down, and then it predicts what should happen. And that's really what LLMs are doing. It's not so much that they're reasoning, it's more that they're just doing the most likely thing. So fundamentally, the ability for people to add in intelligence, I think is very limited. What most people would consider intelligence, I think the AI is not a crowdsourcing problem, right? Now with Wikipedia, Wikipedia crowdsources knowledge. It doesn't crowdsource intelligence. So it's a subtle distinction. AI is fantastic at knowledge. I think it's weak at intelligence. And a lot, it's easy to conflate the two because if you ask it a question and it gives you, you know, if you said, who was the seventh president of the United States, and it gives you the correct answer, I'd say, well, I don't know the answer to that. And you can conflate that with intelligence. But really, that's a question of knowledge. And knowledge is really this thing about saying, how can I store all of this information? And then how can I retrieve something that's relevant? Okay, they're fantastic at that. They're fantastic at storing knowledge and retrieving the relevant knowledge. They're superior to humans in that regard. And so I think we need to come up for a new word. How does one describe AI should contain more knowledge than any individual human? It should be more accessible than any individual human. That's a very powerful thing. That's super

swyx [00:54:07]: powerful. But what words do we use to describe that? We had a previous guest on Exa AI that does search. And he tried to coin super knowledge as the opposite of super intelligence.

William [00:54:20]: Exactly. I think super knowledge is a more accurate word for it.

swyx [00:54:24]: You can store more things than any human can.

William [00:54:26]: And you can retrieve it better than any human can as well. And I think it's those two things combined that's special. I think that thing will exist. That thing can be built. And I think you can start with something that's entertaining and fun. And I think, I often think it's like, look, it's going to be a 20 year journey. And we're in like, year four, or it's like the web. And this is like 1998 or something. You know, you've got a long, long way to go before the Amazon.coms are like these huge, multi trillion dollar businesses that every single person uses every day. And so AI today is very simplistic. And it's fundamentally the way we're using it, the flywheels, and this ability for how can everyone contribute to it to really magnify the value that it brings. Right now, like, I think it's a bit sad. It's like, right now you have big labs, I'm going to pick on open AI. And they kind of go to like these human labelers. And they say, we're going to pay you to just label this like subset of questions that we want to get a really high quality data set, then we're going to get like our own computers that are really powerful. And that's kind of like the thing. For me, it's so much like Encyclopedia Britannica. It's like insane. All the people that were interested in blockchain, it's like, well, this is this is what needs to be decentralized, you need to decentralize that thing. Because if you distribute it, people can generate way more data in a distributed fashion, way more, right? You need the incentive. Yeah, of course. Yeah. But I mean, the, the, that's kind of the exciting thing about Wikipedia was it's this understanding, like the incentives, you don't need money to incentivize people. You don't need dog coins. No. Sometimes, sometimes people get the satisfaction from just seeing the correct thing. Number go up. Yeah, yeah. I mean, you do pay money for Chai vs. Weed. We've, we've paid out over $100,000 to model creators. But do you know what we saw? It's not motivating. We saw that it didn't really make a difference. Like if they were submitting models at a certain rate, if you pay them a bunch of money, they didn't change the rate. What the money let them do was if they wanted to fine tune Alarma 70B on eight H100s overnight, if you give them money, then they can do it. Or you could give them compute. Yeah. So, so I think the most exciting person we ever saw from interacting with Chai, Chai vs. was we gave some kid who was like, like 17 years old, I think we gave him $1,000 and he spent all the money on buying a physical computer. And he took a picture of it and said, this is what I bought. And I'm going to be training more models with it. So that's why, that's why I love platforms.

swyx [00:57:00]: Should you hire him or?

William [00:57:02]: That's the temptation. Yeah. That's the temptation. But you want to keep the team small? No, no. As a platform, we can't just hire every good content creator. We've got to build the systems and the best content creator today isn't going to be the best content creator next year.

Alessio [00:57:14]: What about Eva? So you've talked about reasoning and knowledge. Most of the benchmarks that people use want to mimic reasoning. Yep. I want to register, I disagree on the reasoning, but we have to keep going. Yeah, I'm curious, like how, how do you think about the evals that matter to you?

swyx [00:57:29]: So yeah, like Elo cannot be the only eval. You must have internal evals. You mentioned evals.

William [00:57:34]: I think Elo is a fantastic north star and the reason for it, or like it's the main one we want to see go up because it's this human feedback. The humans know what they want. It's beautiful because when you come up with an eval, you're further removing yourself away from the true problem. Right? So whatever it is you're trying to optimize or figure out, you kind of have to, have to slice it. And then you've got this, it's like a snapshot. Like as soon as you saturate one eval, you need to figure out a new eval. But with, by saying to humans, just which is better, A or B, it's super robust. It's super generalizable. It just keeps, keeps scaling. So we've in the past used evals to get through a, to get through a blocker. I mean, a great example is, you know, is like having like a safety filter or something. Yeah. Where you want to make sure your models, because listen, users find, you'll be shocked the correlation between not family friendly content, whether that's just like swearing, like people find it funny when the AI swears. So if you have two completions, A or B, like if you give me any LLM, I can make it 20% funnier just by training it to throw in swear words. So the issue with that is it's like, how are we measuring like quality improvements? Are we measuring superficial improvements? Right. And this actually links back to the LLM sys. They did a style control.

swyx [00:58:54]: We actually had them on the podcast.

William [00:58:56]: Yeah. Yeah. And so that's the way I, I would rather just lean on human feedback and just continue to make that more and more robust and more and more useful. And, you know, you can say some people are like GPU poor and GPU rich. We're like, we're feedback rich. Like when you've got one and a half million people a day, we get as much feedback from humans as we want. So we're not in a position where we needed to have the evals very much. Yeah. And when we do, we saturate them pretty quick. So a safety one, you know, within a month, we don't need to use it anymore because it's sort of, it's, you know, the issue has been addressed.

swyx [00:59:29]: I think one problem I have, and this is a broader products question maybe, is that the ELOs apply to the whole user population. That's right. Clearly the user behavior, there's segments that have like, I'm a role play person, I'm a therapy person, I'm a not safe for work person. You don't split them?

William [00:59:44]: This is why I say like, I think we're in year four of like a 20 year thing where it's like, at the end of the day, I'm a role play person. And I think if we all go on like Spotify or like, imagine if Spotify only had the top five musicians, I think it would retain over 85% of its existing users. Yeah. Right. And I think if YouTube, if YouTube only kept the top five content creators, it would be enough for the vast majority of people. The thing I'm just trying to share here is there's one surprising thing about humans is their preferences are pretty correlated. What you find funny and entertaining, I find funny and entertaining, and he finds funny and entertaining. There might be degrees of variation in it, I might find it super funny, you might find it only slightly funny, but optimizing to a global works very, very well. And for segmentation to be really powerful, segmentation will work amazing if you found a comment super boring, and I found it super fun. If we could segment that, then that would unlock really powerful stuff. But unfortunately, that's not the shape of human behavior, right? It's like, I might rank it 10 out of 10 funny, you might rank it 7 out of 10 funny. And it's like, it doesn't give you... It doesn't give you as much space to play as you would hope. It's an element of the diversity of content that AI can produce right now, which is it's not as diverse as if you consider a platform like YouTube, you can watch a Mr. Beast video, that's totally different to a makeup tutorial. So there's enough diversity there where if you go on my YouTube feed, it is totally different to my sister's one. My sister's one, it's all like women, and if you go on mine, it's all like bald, middle-aged men, either talking about MMA or, right? I think with AI, it's still a bit too early for that degree of segmentation. So I think it all comes, the recommender systems, the personalization. But this is why I like the, don't start with the technology, start with the problem. The problem is UGC. We must give users the tools to build more variety and more engaging content.

swyx [01:01:42]: Yeah. I feel like there's... I was surprised at how thin it was when I tried out Chai. Yeah. It's very thin. Haven't you been tempted? Like there's this ecosystem of Cobalt, Silly Tavern, those guys. They have model cards. It seems like an industry standard almost. Yeah, agreed. Can I just import those? I don't think I want to say.

William [01:02:01]: Oh, you're already working on it. No, it's like, I remember when Chai meant, Chai, Silly Tavern, and like Cobalt, Cobalt AI is basically as old as Chai. So when Chai was, when we just existed, they just existed. And both of us were using GPT. Chai, yeah, yeah, yeah. And I remember very early on, I was like, these guys shouldn't even exist. Because if we build a good enough platform, they should just be posting their content on our platform.

swyx [01:02:28]: Yeah, but they're open source. No, exactly.

William [01:02:30]: That was what I learned. Eventually, I learned like they're, what they're excited about is slightly different from a typical consumer. My answer is, it's kind of like a complex thing where it's really down to the content creator wants, typically they're building it for themselves. And typically they want to create an experience for themselves. So one content creator might have to write a thousand words describing, let's take a science fiction scenario. Let's say, okay, you're on a spaceship and you're going off into space and your crew, these are your crew members. You've got one that's really friendly, one that's really mean, and you're the new cadet and you want to rise to the top. And they can really go into great detail, right? And then you can give that to like a Lama 70B. And Lama 70B will do a pretty good job of adhering to the prompt and the user will have a good experience. Okay. Very few users will ever go to that level of content creation. If instead the user, we can really make the AI understand the user more so that rather than having to use a thousand characters or a thousand tokens to describe the scenario, we can just say, look, you're on a spaceship. You've got three crewmate. It's going to be dramatic and there should be some fighting. And then the AI gives you an even better experience. Then the content creator is happier. And so fundamentally, the way I'd kind of think about it. Is there's the sterability of the AI. And so a lot of the work we do at Chai is really about saying we want the AI to react to the user and react to the content creator in the way that they most want. One kind of like analog would be TikTok. I think the thing that TikTok did insanely good was they made it really easy for like anyone. If you make a video on TikTok, almost anyone can make a kind of fun video really easy. You just put some music on the top of it. You throw some of the. Animations on top and it's not hard to have a pretty fun thing. And I think that's much more like the Chai style where it's like users don't want to have to work. You know, if your content is only good, if you have like Shakespeare, it's better if, if just anyone at home can make the, can make the thing. So that's, that's kind of like my answer to the silly talent style. And I think the right answer is how do you get the silly time people fine tuning models that create a really special effect.

Alessio [01:04:46]: As we wrap this is kind of the call for action.

William [01:04:49]: Uh, part one, you have Chai Grant, which I think a lot of people don't know about, which is grants for open source projects, any ideas, any projects that you want to see people work on the should apply or let me think, I think, um, so we do try Chai Grant and fundamentally, you know, we give cash, no strings attached. It's kind of our way of doing two things. One, giving back and support in the community. We've benefited from a lot of open source packages. A lot of our developers and engineers are like. Really? Really pro open source. And then also it's a great way to just meet talented people and, and like expand connections. So with respect to Chai Grant, if anyone's got any sort of, um, GitHub project, any sort of thing they built that they're proud of, just apply, just apply. It's like no strings attached cash and people have a pretty high success rate. So that's the first thing. Other call to actions would be, I think Chai is this, you know, it's a startup. We're a small team. It's like 15 people. We work very intense. It's a very hardcore. Sort of environment, which we found that a lot of people don't like. They don't like the, you know, they'll ask us this concept of what life balance one time. A person said, they said something like, I can't get this done because I'm taking PTO on Friday. And I said, what is PTO? Okay. Um, it stands for paid time off and this, I know what it is and this person was gone. They didn't like, they were no longer in the company four weeks on legally. I think you have to, oh, it's true. There's no problem. Look, if you've got. You've got to take a day off, right? We all have personal lives, right? But it's about this idea of responsibility. If you're not in the office on Friday, you still have your responsibilities. So I don't care if you work hard Thursday to get it wrapped up. I don't care if you're working hard Saturday to get it wrapped up. It's not an excuse to, it's not an excuse. The way this individual spoke about it, it was like an excuse. I think it's an environment, very talented engineers working very hard in an intense space. It's the thing that gets me excited. It's, it's why I think, you know, I really love working at Chai is because it's a place of talent. It's a place of people working super hard. So yeah, I think people who have got, who've worked at startups and they, they love that. That's what they, they want the taste of, I think they should reach out, they should apply. And I think 90% of people can say that sounds terrible. Don't apply.

swyx [01:07:03]: It's not for them.

Alessio [01:07:03]: Yeah, it's exactly, exactly. Yeah. I just realized we skipped one important part. So you spent $10 million on compute last year. You say you're going to probably triple that. Yeah. I'm sure you're doing a lot of work on custom kernels, kind of like inference optimization, any cool stuff. Yeah. That you want to share there. Yeah.

William [01:07:20]: Lots of cool stuff. So really quickly, I think inference is very, very important. It's super important. It's massively underlooked and we can look at all the different foundation models and the techniques, the differences in the foundation models on how well they perform from a cost perspective with inference. Mixture of experts, for example, tend to do really, really good from like a cost perspective. We've worked with a very talented team called.

swyx [01:07:49]: MK1 and we, so I saw, I saw them in the Chaiverse logs. What are they?

William [01:07:54]: We were using, we were running VLLM for a while and VLLM is really fantastic. Absolutely amazing. The work that they've done and achieved. And at some point I got introduced to the founder's name is Paul Marola. And he was a co-founder at Neuralink, really, really expert in like hardware. He kind of explained to me, he was like, look, if you know, hardware really well, you can write the CUDA kernels really well. He said, you should check out our inference engine. And they kind of blew VLLM out the water when we evaluated it much, much, much faster. And I think the special thing that he was able to do with us is we love rejection sampling. So we do much more rejection sampling than maybe typical and, you know, generate it. So we, we never, ever, ever just generate a single completion, right? This is why we don't do streaming. A lot of people like ChatGPT used to do a lot of streaming. Like the completion would come out one thing at a time. I did. I didn't notice that in your UX. Normally chat, you have to stream. Exactly. But Chai has never done streaming because if you stream, you're unable to do rejection sampling. The benefit of that is you can serve a larger model. The reason why you can serve a larger model is because they're saying instead of generating a completion in four seconds, because the user gets the first token faster, you can generate in 10 seconds. Well, if you've got 10 seconds to generate completion, you can serve a much larger model. So typically the people that are streaming, the benefit that they're getting is they're, you know, serving a larger model with Chai, we give you, you know, the second answer comes, boom, you get the full completion. And the reason for that is because we want to generate 16 completions, see the entire response, and then we want to evaluate which one we think is the best.

swyx [01:09:34]: Do you have a separate LLM evaluator? Yes, we do. Yeah.

William [01:09:37]: So, um, typically they're referred to as a reward model and that's a, you know, that's like a term from reinforcement learning. And for that, you can start off with something very simple, which is, do you think the user is going to respond to it? That's a simple one. So you can, you can train, you can take 50 million messages and, and look at all the sorts of messages users reply to, which ones they don't. And then you can train this, this reward model to evaluate completions. And so it knows like, okay, if you say this, the user is not going to respond. So don't bother sending it to the user. If you say this, the user is definitely going to engage with it. So send them, send them that.

swyx [01:10:11]: There's an interesting parallel between MLAs and MLAs. I think we use at the top, spreading out to different experts and then at the bottom with rejection sampling, choosing from different paths.

William [01:10:21]: I totally agree. That's the stuff that is the future of AI. I think that's the exciting stuff. And there's a parallel between that. Why was AlphaGo able to be superhuman? Right. It's this ability to generate many different paths. Tree search. And tree search. Exactly. So I think if you want to talk about what would intelligence look like, it looks much more like tree search. Combining the generative nature of these LLMs with a really good tree search. And that's what opening I've done with O1 and O3.

swyx [01:10:51]: I don't know that they do tree search. They never said they do. It's implied. Yes. Okay. Yes. Yes. Are you comfortable with O1 being a reasoning engine? No, no, no, no.

William [01:11:01]: I'm saying it's better at reasoning because they leverage the tree search well. And the, the issue of the reasoning is they're saying, is this like they train, they have the models to say, is this logically correct? And what's the likelihood of it being logically correct? So you can build up the sophisticated mechanisms to get it less bad at reasoning, but you'll see like eventually what, what AI is really, really good at. People won't say it's, it's always going to be better at retrieving. It's always going to be better at storing knowledge, which is so highly correlated with intelligence that we often assume it's the same. What, what AI is truly special at and gets consumers really excited is it's generative. It can just make stuff. We've never had a technology. Before that can just make stuff simulate.

Alessio [01:11:45]: Yeah.

William [01:11:45]: Yeah. So that's the special, that's the exciting thing.

Alessio [01:11:48]: Awesome. Well, any parting parting thoughts?

William [01:11:51]: No, it's been, it's been a pleasure. I guess the only thing I'd add is like our office is in Palo Alto. So, um, yeah, you know, people with startup experience looking to join a fast growing high impact startup. Yeah.

swyx [01:12:03]: Uh, we'll find your culture deck, which is great. Fantastic. And then also, yeah. Yeah.

Alessio [01:12:07]: What's the story where if you made a hundred K trading, we'll fast track your application. Like, I mean, I kind of qualify.

William [01:12:15]: just looked at the team and it got to the point where almost every single person on the team you could point to, and they had done something special before joining the team. Like they, they had strong markers of like, there was something special about them. That's not to say it's like, like an exclusive thing. You have to have achieved something special, but it's just, uh, we got this one engineer and she, she started going to college. She went to CMU when she was like 15 years old or something. And it's like, that's a bit special. There's another engineer. He created a Git repo and I think he got like 1500 stars and it was like a repo for like, there was some drivers that he wrote. It was like a super low, low level thing. I was like, that's a bit special. We had this other guy, he joined the team and he'd, he had made a hundred K buying and selling sneakers, right? Trading. Yeah. So, so it's like, it's just this thing, like if you've been to Harvard, cool, that's great. It shows that you're really smart and you work really hard. Cool. That's good. But if you've actually built something and done something. I think there's a bit more tangible that gets us even more excited.

Alessio [01:13:16]: Cool. Well, thanks for having us at ChaiHQ. Yeah.

William [01:13:19]: Thanks guys.

Sponsorships and applications for the AI Engineer Summit in NYC are live! (Speaker CFPs have closed) If you are building AI agents or leading teams of AI Engineers, this will be the single highest-signal conference of the year for you.

Right after Christmas, the Chinese Whale Bros ended 2024 by dropping the last big model launch of the year: DeepSeek v3. Right now on LM Arena, DeepSeek v3 has a score of 1319, right under the full o1 model, Gemini 2, and 4o latest. This makes it the best open weights model in the world in January 2025.

There has been a big recent trend in Chinese labs releasing very large open weights models, with TenCent releasing Hunyuan-Large in November and Hailuo releasing MiniMax-Text this week, both over 400B in size. However these extra-large language models are very difficult to serve.

Baseten was the first of the Inference neocloud startups to get DeepSeek V3 online, because of their H200 clusters, their close collaboration with the DeepSeek team and early support of SGLang, a relatively new VLLM alternative that is also used at frontier labs like X.ai. Each H200 has 141 GB of VRAM with 4.8 TB per second of bandwidth, meaning that you can use 8 H200's in a node to inference DeepSeek v3 in FP8, taking into account KV Cache needs.

We have been close to Baseten since Sarah Guo introduced Amir Haghighat to swyx, and they supported the very first Latent Space Demo Day in San Francisco, which was effectively the trial run for swyx and Alessio to work together!

Since then, Philip Kiely also led a well attended workshop on TensorRT LLM at the 2024 World's Fair.

We worked with him to get two of their best representatives, Amir and Lead Model Performance Engineer Yineng Zhang, to discuss DeepSeek, SGLang, and everything they have learned running Mission Critical Inference workloads at scale for some of the largest AI products in the world.

The Three Pillars of Mission Critical Inference

We initially planned to focus the conversation on SGLang, but Amir and Yineng were quick to correct us that the choice of inference framework is only the simplest, first choice of 3 things you need for production inference at scale:

“I think it takes three things, and each of them individually is necessary but not sufficient:

* Performance at the model level: how fast are you running this one model running on a single GPU, let's say. The framework that you use there can, can matter. The techniques that you use there can matter. The MLA technique, for example, that Yineng mentioned, or the CUDA kernels that are being used. But there's also techniques being used at a higher level, things like speculative decoding with draft models or with Medusa heads. And these are implemented in the different frameworks, or you can even implement it yourself, but they're not necessarily tied to a single framework. But using speculative decoding gets you massive upside when it comes to being able to handle high throughput. But that's not enough. Invariably, that one model running on a single GPU, let's say, is going to get too much traffic that it cannot handle.

* Horizontal scaling at the cluster/region level: And at that point, you need to horizontally scale it. That's not an ML problem. That's not a PyTorch problem. That's an infrastructure problem. How quickly do you go from, a single replica of that model to 5, to 10, to 100. And so that's the second, that's the second pillar that is necessary for running these machine critical inference workloads.

And what does it take to do that? It takes, some people are like, Oh, You just need Kubernetes and Kubernetes has an autoscaler and that just works. That doesn't work for, for these kinds of mission critical inference workloads. And you end up catching yourself wanting to bit by bit to rebuild those infrastructure pieces from scratch. This has been our experience.

* And then going even a layer beyond that, Kubernetes runs in a single. cluster. It's a single cluster. It's a single region tied to a single region. And when it comes to inference workloads and needing GPUs more and more, you know, we're seeing this that you cannot meet the demand inside of a single region. A single cloud's a single region. In other words, a single model might want to horizontally scale up to 200 replicas, each of which is, let's say, 2H100s or 4H100s or even a full node, you run into limits of the capacity inside of that one region. And what we had to build to get around that was the ability to have a single model have replicas across different regions. So, you know, there are models on Baseten today that have 50 replicas in GCP East and, 80 replicas in AWS West and Oracle in London, etc.

* Developer experience for Compound AI Systems: The final one is wrapping the power of the first two pillars in a very good developer experience to be able to afford certain workflows like the ones that I mentioned, around multi step, multi model inference workloads, because more and more we're seeing that the market is moving towards those that the needs are generally in these sort of more complex workflows.

We think they said it very well.

Show Notes

* Amir Haghighat, Co-Founder, Baseten

* Yineng Zhang, Lead Software Engineer, Model Performance, Baseten

Full YouTube Episode

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Timestamps

* 00:00 Introduction and Latest AI Model Launch

* 00:11 DeepSeek v3: Specifications and Achievements

* 03:10 Latent Space Podcast: Special Guests Introduction

* 04:12 DeepSeek v3: Technical Insights

* 11:14 Quantization and Model Performance

* 16:19 MOE Models: Trends and Challenges

* 18:53 Baseten's Inference Service and Pricing

* 31:13 Optimization for DeepSeek

* 31:45 Three Pillars of Mission Critical Inference Workloads

* 32:39 Scaling Beyond Single GPU

* 33:09 Challenges with Kubernetes and Infrastructure

* 33:40 Multi-Region Scaling Solutions

* 35:34 SG Lang: A New Framework

* 38:52 Key Techniques Behind SG Lang

* 48:27 Speculative Decoding and Performance

* 49:54 Future of Fine-Tuning and RLHF

* 01:00:28 Baseten's V3 and Industry Trends

Baseten’s previous TensorRT LLM workshop:

Due to overwhelming demand (>15x applications:slots), we are closing CFPs for AI Engineer Summit NYC today. Last call! Thanks, we’ll be reaching out to all shortly!

The world’s top AI blogger and friend of every pod, Simon Willison, dropped a monster 2024 recap: Things we learned about LLMs in 2024. Brian of the excellent TechMeme Ride Home pinged us for a connection and a special crossover episode, our first in 2025.

The target audience for this podcast is a tech-literate, but non-technical one. You can see Simon’s notes for AI Engineers in his World’s Fair Keynote.

Timestamp

* 00:00 Introduction and Guest Welcome

* 01:06 State of AI in 2025

* 01:43 Advancements in AI Models

* 03:59 Cost Efficiency in AI

* 06:16 Challenges and Competition in AI

* 17:15 AI Agents and Their Limitations

* 26:12 Multimodal AI and Future Prospects

* 35:29 Exploring Video Avatar Companies

* 36:24 AI Influencers and Their Future

* 37:12 Simplifying Content Creation with AI

* 38:30 The Importance of Credibility in AI

* 41:36 The Future of LLM User Interfaces

* 48:58 Local LLMs: A Growing Interest

* 01:07:22 AI Wearables: The Next Big Thing

* 01:10:16 Wrapping Up and Final Thoughts

Transcript

[00:00:00] Introduction and Guest Welcome

[00:00:00] Brian: Welcome to the first bonus episode of the Tech Meme Write Home for the year 2025. I'm your host as always, Brian McCullough. Listeners to the pod over the last year know that I have made a habit of quoting from Simon Willison when new stuff happens in AI from his blog. Simon has been, become a go to for many folks in terms of, you know, Analyzing things, criticizing things in the AI space.

[00:00:33] Brian: I've wanted to talk to you for a long time, Simon. So thank you for coming on the show. No, it's a privilege to be here. And the person that made this connection happen is our friend Swyx, who has been on the show back, even going back to the, the Twitter Spaces days but also an AI guru in, in their own right Swyx, thanks for coming on the show also.

[00:00:54] swyx (2): Thanks. I'm happy to be on and have been a regular listener, so just happy to [00:01:00] contribute as well.

[00:01:00] Brian: And a good friend of the pod, as they say. Alright, let's go right into it.

[00:01:06] State of AI in 2025

[00:01:06] Brian: Simon, I'm going to do the most unfair, broad question first, so let's get it out of the way. The year 2025. Broadly, what is the state of AI as we begin this year?

[00:01:20] Brian: Whatever you want to say, I don't want to lead the witness.

[00:01:22] Simon: Wow. So many things, right? I mean, the big thing is everything's got really good and fast and cheap. Like, that was the trend throughout all of 2024. The good models got so much cheaper, they got so much faster, they got multimodal, right? The image stuff isn't even a surprise anymore.

[00:01:39] Simon: They're growing video, all of that kind of stuff. So that's all really exciting.

[00:01:43] Advancements in AI Models

[00:01:43] Simon: At the same time, they didn't get massively better than GPT 4, which was a bit of a surprise. So that's sort of one of the open questions is, are we going to see huge, but I kind of feel like that's a bit of a distraction because GPT 4, but way cheaper, much larger context lengths, and it [00:02:00] can do multimodal.

[00:02:01] Simon: is better, right? That's a better model, even if it's not.

[00:02:05] Brian: What people were expecting or hoping, maybe not expecting is not the right word, but hoping that we would see another step change, right? Right. From like GPT 2 to 3 to 4, we were expecting or hoping that maybe we were going to see the next evolution in that sort of, yeah.

[00:02:21] Brian: We

[00:02:21] Simon: did see that, but not in the way we expected. We thought the model was just going to get smarter, and instead we got. Massive drops in, drops in price. We got all of these new capabilities. You can talk to the things now, right? They can do simulated audio input, all of that kind of stuff. And so it's kind of, it's interesting to me that the models improved in all of these ways we weren't necessarily expecting.

[00:02:43] Simon: I didn't know it would be able to do an impersonation of Santa Claus, like a, you know, Talked to it through my phone and show it what I was seeing by the end of 2024. But yeah, we didn't get that GPT 5 step. And that's one of the big open questions is, is that actually just around the corner and we'll have a bunch of GPT 5 class models drop in the [00:03:00] next few months?

[00:03:00] Simon: Or is there a limit?

[00:03:03] Brian: If you were a betting man and wanted to put money on it, do you expect to see a phase change, step change in 2025?

[00:03:11] Simon: I don't particularly for that, like, the models, but smarter. I think all of the trends we're seeing right now are going to keep on going, especially the inference time compute, right?

[00:03:21] Simon: The trick that O1 and O3 are doing, which means that you can solve harder problems, but they cost more and it churns away for longer. I think that's going to happen because that's already proven to work. I don't know. I don't know. Maybe there will be a step change to a GPT 5 level, but honestly, I'd be completely happy if we got what we've got right now.

[00:03:41] Simon: But cheaper and faster and more capabilities and longer contexts and so forth. That would be thrilling to me.

[00:03:46] Brian: Digging into what you've just said one of the things that, by the way, I hope to link in the show notes to Simon's year end post about what, what things we learned about LLMs in 2024. Look for that in the show notes.

[00:03:59] Cost Efficiency in AI

[00:03:59] Brian: One of the things that you [00:04:00] did say that you alluded to even right there was that in the last year, you felt like the GPT 4 barrier was broken, like IE. Other models, even open source ones are now regularly matching sort of the state of the art.

[00:04:13] Simon: Well, it's interesting, right? So the GPT 4 barrier was a year ago, the best available model was OpenAI's GPT 4 and nobody else had even come close to it.

[00:04:22] Simon: And they'd been at the, in the lead for like nine months, right? That thing came out in what, February, March of, of 2023. And for the rest of 2023, nobody else came close. And so at the start of last year, like a year ago, the big question was, Why has nobody beaten them yet? Like, what do they know that the rest of the industry doesn't know?

[00:04:40] Simon: And today, that I've counted 18 organizations other than GPT 4 who've put out a model which clearly beats that GPT 4 from a year ago thing. Like, maybe they're not better than GPT 4. 0, but that's, that, that, that barrier got completely smashed. And yeah, a few of those I've run on my laptop, which is wild to me.

[00:04:59] Simon: Like, [00:05:00] it was very, very wild. It felt very clear to me a year ago that if you want GPT 4, you need a rack of 40, 000 GPUs just to run the thing. And that turned out not to be true. Like the, the, this is that big trend from last year of the models getting more efficient, cheaper to run, just as capable with smaller weights and so forth.

[00:05:20] Simon: And I ran another GPT 4 model on my laptop this morning, right? Microsoft 5. 4 just came out. And that, if you look at the benchmarks, it's definitely, it's up there with GPT 4. 0. It's probably not as good when you actually get into the vibes of the thing, but it, it runs on my, it's a 14 gigabyte download and I can run it on a MacBook Pro.

[00:05:38] Simon: Like who saw that coming? The most exciting, like the close of the year on Christmas day, just a few weeks ago, was when DeepSeek dropped their DeepSeek v3 model on Hugging Face without even a readme file. It was just like a giant binary blob that I can't run on my laptop. It's too big. But in all of the benchmarks, it's now by far the best available [00:06:00] open, open weights model.

[00:06:01] Simon: Like it's, it's, it's beating the, the metalamas and so forth. And that was trained for five and a half million dollars, which is a tenth of the price that people thought it costs to train these things. So everything's trending smaller and faster and more efficient.

[00:06:15] Brian: Well, okay.

[00:06:16] Challenges and Competition in AI

[00:06:16] Brian: I, I kind of was going to get to that later, but let's, let's combine this with what I was going to ask you next, which is, you know, you're talking, you know, Also in the piece about the LLM prices crashing, which I've even seen in projects that I'm working on, but explain Explain that to a general audience, because we hear all the time that LLMs are eye wateringly expensive to run, but what we're suggesting, and we'll come back to the cheap Chinese LLM, but first of all, for the end user, what you're suggesting is that we're starting to see the cost come down sort of in the traditional technology way of Of costs coming down over time,

[00:06:49] Simon: yes, but very aggressively.

[00:06:51] Simon: I mean, my favorite thing, the example here is if you look at GPT-3, so open AI's g, PT three, which was the best, a developed model in [00:07:00] 2022 and through most of 20 2023. That, the models that we have today, the OpenAI models are a hundred times cheaper. So there was a 100x drop in price for OpenAI from their best available model, like two and a half years ago to today.

[00:07:13] Simon: And

[00:07:14] Brian: just to be clear, not to train the model, but for the use of tokens and things. Exactly,

[00:07:20] Simon: for running prompts through them. And then When you look at the, the really, the top tier model providers right now, I think, are OpenAI, Anthropic, Google, and Meta. And there are a bunch of others that I could list there as well.

[00:07:32] Simon: Mistral are very good. The, the DeepSeq and Quen models have got great. There's a whole bunch of providers serving really good models. But even if you just look at the sort of big brand name providers, they all offer models now that are A fraction of the price of the, the, of the models we were using last year.

[00:07:49] Simon: I think I've got some numbers that I threw into my blog entry here. Yeah. Like Gemini 1. 5 flash, that's Google's fast high quality model is [00:08:00] how much is that? It's 0. 075 dollars per million tokens. Like these numbers are getting, So we just do cents per million now,

[00:08:09] swyx (2): cents per million,

[00:08:10] Simon: cents per million makes, makes a lot more sense.

[00:08:12] Simon: Yeah they have one model 1. 5 flash 8B, the absolute cheapest of the Google models, is 27 times cheaper than GPT 3. 5 turbo was a year ago. That's it. And GPT 3. 5 turbo, that was the cheap model, right? Now we've got something 27 times cheaper, and the Google, this Google one can do image recognition, it can do million token context, all of those tricks.

[00:08:36] Simon: But it's, it's, it's very, it's, it really is startling how inexpensive some of this stuff has got.

[00:08:41] Brian: Now, are we assuming that this, that happening is directly the result of competition? Because again, you know, OpenAI, and probably they're doing this for their own almost political reasons, strategic reasons, keeps saying, we're losing money on everything, even the 200.

[00:08:56] Brian: So they probably wouldn't, the prices wouldn't be [00:09:00] coming down if there wasn't intense competition in this space.

[00:09:04] Simon: The competition is absolutely part of it, but I have it on good authority from sources I trust that Google Gemini is not operating at a loss. Like, the amount of electricity to run a prompt is less than they charge you.

[00:09:16] Simon: And the same thing for Amazon Nova. Like, somebody found an Amazon executive and got them to say, Yeah, we're not losing money on this. I don't know about Anthropic and OpenAI, but clearly that demonstrates it is possible to run these things at these ludicrously low prices and still not be running at a loss if you discount the Army of PhDs and the, the training costs and all of that kind of stuff.

[00:09:36] Brian: One, one more for me before I let Swyx jump in here. To, to come back to DeepSeek and this idea that you could train, you know, a cutting edge model for 6 million. I, I was saying on the show, like six months ago, that if we are getting to the point where each new model It would cost a billion, ten billion, a hundred billion to train that.

[00:09:54] Brian: At some point it would almost, only nation states would be able to train the new models. Do you [00:10:00] expect what DeepSeek and maybe others are proving to sort of blow that up? Or is there like some sort of a parallel track here that maybe I'm not technically, I don't have the mouse to understand the difference.

[00:10:11] Brian: Is the model, are the models going to go, you know, Up to a hundred billion dollars or can we get them down? Sort of like DeepSeek has proven

[00:10:18] Simon: so I'm the wrong person to answer that because I don't work in the lab training these models. So I can give you my completely uninformed opinion, which is, I felt like the DeepSeek thing.

[00:10:27] Simon: That was a bomb shell. That was an absolute bombshell when they came out and said, Hey, look, we've trained. One of the best available models and it cost us six, five and a half million dollars to do it. I feel, and they, the reason, one of the reasons it's so efficient is that we put all of these export controls in to stop Chinese companies from giant buying GPUs.

[00:10:44] Simon: So they've, were forced to be, go as efficient as possible. And yet the fact that they've demonstrated that that's possible to do. I think it does completely tear apart this, this, this mental model we had before that yeah, the training runs just keep on getting more and more expensive and the number of [00:11:00] organizations that can afford to run these training runs keeps on shrinking.

[00:11:03] Simon: That, that's been blown out of the water. So yeah, that's, again, this was our Christmas gift. This was the thing they dropped on Christmas day. Yeah, it makes me really optimistic that we can, there are, It feels like there was so much low hanging fruit in terms of the efficiency of both inference and training and we spent a whole bunch of last year exploring that and getting results from it.

[00:11:22] Simon: I think there's probably a lot left. I think there's probably, well, I would not be surprised to see even better models trained spending even less money over the next six months.

[00:11:31] swyx (2): Yeah. So I, I think there's a unspoken angle here on what exactly the Chinese labs are trying to do because DeepSea made a lot of noise.

[00:11:41] swyx (2): so much for joining us for around the fact that they train their model for six million dollars and nobody quite quite believes them. Like it's very, very rare for a lab to trumpet the fact that they're doing it for so cheap. They're not trying to get anyone to buy them. So why [00:12:00] are they doing this? They make it very, very obvious.

[00:12:05] swyx (2): Deepseek is about 150 employees. It's an order of magnitude smaller than at least Anthropic and maybe, maybe more so for OpenAI. And so what's, what's the end game here? Are they, are they just trying to show that the Chinese are better than us?

[00:12:21] Simon: So Deepseek, it's the arm of a hedge, it's a, it's a quant fund, right?

[00:12:25] Simon: It's an algorithmic quant trading thing. So I, I, I would love to get more insight into how that organization works. My assumption from what I've seen is it looks like they're basically just flexing. They're like, hey, look at how utterly brilliant we are with this amazing thing that we've done. And it's, it's working, right?

[00:12:43] Simon: They but, and so is that it? Are they, is this just their kind of like, this is, this is why our company is so amazing. Look at this thing that we've done, or? I don't know. I'd, I'd love to get Some insight from, from within that industry as to, as to how that's all playing out.

[00:12:57] swyx (2): The, the prevailing theory among the Local Llama [00:13:00] crew and the Twitter crew that I indexed for my newsletter is that there is some amount of copying going on.

[00:13:06] swyx (2): It's like Sam Altman you know, tweet, tweeting about how they're being copied. And then also there's this, there, there are other sort of opening eye employees that have said, Stuff that is similar that DeepSeek's rate of progress is how U. S. intelligence estimates the number of foreign spies embedded in top labs.

[00:13:22] swyx (2): Because a lot of these ideas do spread around, but they surprisingly have a very high density of them in the DeepSeek v3 technical report. So it's, it's interesting. We don't know how much, how many, how much tokens. I think that, you know, people have run analysis on how often DeepSeek thinks it is cloud or thinks it is opening GPC 4.

[00:13:40] swyx (2): Thanks for watching! And we don't, we don't know. We don't know. I think for me, like, yeah, we'll, we'll, we basically will never know as, as external commentators. I think what's interesting is how, where does this go? Is there a logical floor or bottom by my estimations for the same amount of ELO started last year to the end of last year cost went down by a thousand X for the [00:14:00] GPT, for, for GPT 4 intelligence.

[00:14:02] swyx (2): Would, do they go down a thousand X this year?

[00:14:04] Simon: That's a fascinating question. Yeah.

[00:14:06] swyx (2): Is there a Moore's law going on, or did we just get a one off benefit last year for some weird reason?

[00:14:14] Simon: My uninformed hunch is low hanging fruit. I feel like up until a year ago, people haven't been focusing on efficiency at all. You know, it was all about, what can we get these weird shaped things to do?

[00:14:24] Simon: And now once we've sort of hit that, okay, we know that we can get them to do what GPT 4 can do, When thousands of researchers around the world all focus on, okay, how do we make this more efficient? What are the most important, like, how do we strip out all of the weights that have stuff in that doesn't really matter?

[00:14:39] Simon: All of that kind of thing. So yeah, maybe that was it. Maybe 2024 was a freak year of all of the low hanging fruit coming out at once. And we'll actually see a reduction in the, in that rate of improvement in terms of efficiency. I wonder, I mean, I think we'll know for sure in about three months time if that trend's going to continue or not.

[00:14:58] swyx (2): I agree. You know, I [00:15:00] think the other thing that you mentioned that DeepSeq v3 was the gift that was given from DeepSeq over Christmas, but I feel like the other thing that might be underrated was DeepSeq R1,

[00:15:11] Speaker 4: which is

[00:15:13] swyx (2): a reasoning model you can run on your laptop. And I think that's something that a lot of people are looking ahead to this year.

[00:15:18] swyx (2): Oh, did they

[00:15:18] Simon: release the weights for that one?

[00:15:20] swyx (2): Yeah.

[00:15:21] Simon: Oh my goodness, I missed that. I've been playing with the quen. So the other great, the other big Chinese AI app is Alibaba's quen. Actually, yeah, I, sorry, R1 is an API available. Yeah. Exactly. When that's really cool. So Alibaba's Quen have released two reasoning models that I've run on my laptop.

[00:15:38] Simon: Now there was, the first one was Q, Q, WQ. And then the second one was QVQ because the second one's a vision model. So you can like give it vision puzzles and a prompt that these things, they are so much fun to run. Because they think out loud. It's like the OpenAR 01 sort of hides its thinking process. The Query ones don't.

[00:15:59] Simon: They just, they [00:16:00] just churn away. And so you'll give it a problem and it will output literally dozens of paragraphs of text about how it's thinking. My favorite thing that happened with QWQ is I asked it to draw me a pelican on a bicycle in SVG. That's like my standard stupid prompt. And for some reason it thought in Chinese.

[00:16:18] Simon: It spat out a whole bunch of like Chinese text onto my terminal on my laptop, and then at the end it gave me quite a good sort of artistic pelican on a bicycle. And I ran it all through Google Translate, and yeah, it was like, it was contemplating the nature of SVG files as a starting point. And the fact that my laptop can think in Chinese now is so delightful.

[00:16:40] Simon: It's so much fun watching you do that.

[00:16:43] swyx (2): Yeah, I think Andrej Karpathy was saying, you know, we, we know that we have achieved proper reasoning inside of these models when they stop thinking in English, and perhaps the best form of thought is in Chinese. But yeah, for listeners who don't know Simon's blog he always, whenever a new model comes out, you, I don't know how you do it, but [00:17:00] you're always the first to run Pelican Bench on these models.

[00:17:02] swyx (2): I just did it for 5.

[00:17:05] Simon: Yeah.

[00:17:07] swyx (2): So I really appreciate that. You should check it out. These are not theoretical. Simon's blog actually shows them.

[00:17:12] Brian: Let me put on the investor hat for a second.

[00:17:15] AI Agents and Their Limitations

[00:17:15] Brian: Because from the investor side of things, a lot of the, the VCs that I know are really hot on agents, and this is the year of agents, but last year was supposed to be the year of agents as well. Lots of money flowing towards, And Gentic startups.

[00:17:32] Brian: But in in your piece that again, we're hopefully going to have linked in the show notes, you sort of suggest there's a fundamental flaw in AI agents as they exist right now. Let me let me quote you. And then I'd love to dive into this. You said, I remain skeptical as to their ability based once again, on the Challenge of gullibility.

[00:17:49] Brian: LLMs believe anything you tell them, any systems that attempt to make meaningful decisions on your behalf, will run into the same roadblock. How good is a travel agent, or a digital assistant, or even a research tool, if it [00:18:00] can't distinguish truth from fiction? So, essentially, what you're suggesting is that the state of the art now that allows agents is still, it's still that sort of 90 percent problem, the edge problem, getting to the Or, or, or is there a deeper flaw?

[00:18:14] Brian: What are you, what are you saying there?

[00:18:16] Simon: So this is the fundamental challenge here and honestly my frustration with agents is mainly around definitions Like any if you ask anyone who says they're working on agents to define agents You will get a subtly different definition from each person But everyone always assumes that their definition is the one true one that everyone else understands So I feel like a lot of these agent conversations, people talking past each other because one person's talking about the, the sort of travel agent idea of something that books things on your behalf.

[00:18:41] Simon: Somebody else is talking about LLMs with tools running in a loop with a cron job somewhere and all of these different things. You, you ask academics and they'll laugh at you because they've been debating what agents mean for over 30 years at this point. It's like this, this long running, almost sort of an in joke in that community.

[00:18:57] Simon: But if we assume that for this purpose of this conversation, an [00:19:00] agent is something that, Which you can give a job and it goes off and it does that thing for you like, like booking travel or things like that. The fundamental challenge is, it's the reliability thing, which comes from this gullibility problem.

[00:19:12] Simon: And a lot of my, my interest in this originally came from when I was thinking about prompt injections as a source of this form of attack against LLM systems where you deliberately lay traps out there for this LLM to stumble across,

[00:19:24] Brian: and which I should say you have been banging this drum that no one's gotten any far, at least on solving this, that I'm aware of, right.

[00:19:31] Brian: Like that's still an open problem. The two years.

[00:19:33] Simon: Yeah. Right. We've been talking about this problem and like, a great illustration of this was Claude so Anthropic released Claude computer use a few months ago. Fantastic demo. You could fire up a Docker container and you could literally tell it to do something and watch it open a web browser and navigate to a webpage and click around and so forth.

[00:19:51] Simon: Really, really, really interesting and fun to play with. And then, um. One of the first demos somebody tried was, what if you give it a web page that says download and run this [00:20:00] executable, and it did, and the executable was malware that added it to a botnet. So the, the very first most obvious dumb trick that you could play on this thing just worked, right?

[00:20:10] Simon: So that's obviously a really big problem. If I'm going to send something out to book travel on my behalf, I mean, it's hard enough for me to figure out which airlines are trying to scam me and which ones aren't. Do I really trust a language model that believes the literal truth of anything that's presented to it to go out and do those things?

[00:20:29] swyx (2): Yeah I definitely think there's, it's interesting to see Anthropic doing this because they used to be the safety arm of OpenAI that split out and said, you know, we're worried about letting this thing out in the wild and here they are enabling computer use for agents. Thanks. The, it feels like things have merged.

[00:20:49] swyx (2): You know, I'm, I'm also fairly skeptical about, you know, this always being the, the year of Linux on the desktop. And this is the equivalent of this being the year of agents that people [00:21:00] are not predicting so much as wishfully thinking and hoping and praying for their companies and agents to work.

[00:21:05] swyx (2): But I, I feel like things are. Coming along a little bit. It's to me, it's kind of like self driving. I remember in 2014 saying that self driving was just around the corner. And I mean, it kind of is, you know, like in, in, in the Bay area. You

[00:21:17] Simon: get in a Waymo and you're like, Oh, this works. Yeah, but it's a slow

[00:21:21] swyx (2): cook.

[00:21:21] swyx (2): It's a slow cook over the next 10 years. We're going to hammer out these things and the cynical people can just point to all the flaws, but like, there are measurable or concrete progress steps that are being made by these builders.

[00:21:33] Simon: There is one form of agent that I believe in. I believe, mostly believe in the research assistant form of agents.

[00:21:39] Simon: The thing where you've got a difficult problem and, and I've got like, I'm, I'm on the beta for the, the Google Gemini 1. 5 pro with deep research. I think it's called like these names, these names. Right. But. I've been using that. It's good, right? You can give it a difficult problem and it tells you, okay, I'm going to look at 56 different websites [00:22:00] and it goes away and it dumps everything to its context and it comes up with a report for you.

[00:22:04] Simon: And it's not, it won't work against adversarial websites, right? If there are websites with deliberate lies in them, it might well get caught out. Most things don't have that as a problem. And so I've had some answers from that which were genuinely really valuable to me. And that feels to me like, I can see how given existing LLM tech, especially with Google Gemini with its like million token contacts and Google with their crawl of the entire web and their, they've got like search, they've got search and cache, they've got a cache of every page and so forth.

[00:22:35] Simon: That makes sense to me. And that what they've got right now, I don't think it's, it's not as good as it can be, obviously, but it's, it's, it's, it's a real useful thing, which they're going to start rolling out. So, you know, Perplexity have been building the same thing for a couple of years. That, that I believe in.

[00:22:50] Simon: You know, if you tell me that you're going to have an agent that's a research assistant agent, great. The coding agents I mean, chat gpt code interpreter, Nearly two years [00:23:00] ago, that thing started writing Python code, executing the code, getting errors, rewriting it to fix the errors. That pattern obviously works.

[00:23:07] Simon: That works really, really well. So, yeah, coding agents that do that sort of error message loop thing, those are proven to work. And they're going to keep on getting better, and that's going to be great. The research assistant agents are just beginning to get there. The things I'm critical of are the ones where you trust, you trust this thing to go out and act autonomously on your behalf, and make decisions on your behalf, especially involving spending money, like that.

[00:23:31] Simon: I don't see that working for a very long time. That feels to me like an AGI level problem.

[00:23:37] swyx (2): It's it's funny because I think Stripe actually released an agent toolkit which is one of the, the things I featured that is trying to enable these agents each to have a wallet that they can go and spend and have, basically, it's a virtual card.

[00:23:49] swyx (2): It's not that, not that difficult with modern infrastructure. can

[00:23:51] Simon: stick a 50 cap on it, then at least it's an honor. Can't lose more than 50.

[00:23:56] Brian: You know I don't, I don't know if either of you know Rafat Ali [00:24:00] he runs Skift, which is a, a travel news vertical. And he, he, he constantly laughs at the fact that every agent thing is, we're gonna get rid of booking a, a plane flight for you, you know?

[00:24:11] Brian: And, and I would point out that, like, historically, when the web started, the first thing everyone talked about is, You can go online and book a trip, right? So it's funny for each generation of like technological advance. The thing they always want to kill is the travel agent. And now they want to kill the webpage travel agent.

[00:24:29] Simon: Like it's like I use Google flight search. It's great, right? If you gave me an agent to do that for me, it would save me, I mean, maybe 15 seconds of typing in my things, but I still want to see what my options are and go, yeah, I'm not flying on that airline, no matter how cheap they are.

[00:24:44] swyx (2): Yeah. For listeners, go ahead.

[00:24:47] swyx (2): For listeners, I think, you know, I think both of you are pretty positive on NotebookLM. And you know, we, we actually interviewed the NotebookLM creators, and there are actually two internal agents going on internally. The reason it takes so long is because they're running an agent loop [00:25:00] inside that is fairly autonomous, which is kind of interesting.

[00:25:01] swyx (2): For one,

[00:25:02] Simon: for a definition of agent loop, if you picked that particularly well. For one definition. And you're talking about the podcast side of this, right?

[00:25:07] swyx (2): Yeah, the podcast side of things. They have a there's, there's going to be a new version coming out that, that we'll be featuring at our, at our conference.

[00:25:14] Simon: That one's fascinating to me. Like NotebookLM, I think it's two products, right? On the one hand, it's actually a very good rag product, right? You dump a bunch of things in, you can run searches, that, that, it does a good job of. And then, and then they added the, the podcast thing. It's a bit of a, it's a total gimmick, right?

[00:25:30] Simon: But that gimmick got them attention, because they had a great product that nobody paid any attention to at all. And then you add the unfeasibly good voice synthesis of the podcast. Like, it's just, it's, it's, it's the lesson.

[00:25:43] Brian: It's the lesson of mid journey and stuff like that. If you can create something that people can post on socials, you don't have to lift a finger again to do any marketing for what you're doing.

[00:25:53] Brian: Let me dig into Notebook LLM just for a second as a podcaster. As a [00:26:00] gimmick, it makes sense, and then obviously, you know, you dig into it, it sort of has problems around the edges. It's like, it does the thing that all sort of LLMs kind of do, where it's like, oh, we want to Wrap up with a conclusion.

[00:26:12] Multimodal AI and Future Prospects

[00:26:12] Brian: I always call that like the the eighth grade book report paper problem where it has to have an intro and then, you know But that's sort of a thing where because I think you spoke about this again in your piece at the year end About how things are going multimodal and how things are that you didn't expect like, you know vision and especially audio I think So that's another thing where, at least over the last year, there's been progress made that maybe you, you didn't think was coming as quick as it came.

[00:26:43] Simon: I don't know. I mean, a year ago, we had one really good vision model. We had GPT 4 vision, was, was, was very impressive. And Google Gemini had just dropped Gemini 1. 0, which had vision, but nobody had really played with it yet. Like Google hadn't. People weren't taking Gemini [00:27:00] seriously at that point. I feel like it was 1.

[00:27:02] Simon: 5 Pro when it became apparent that actually they were, they, they got over their hump and they were building really good models. And yeah, and they, to be honest, the video models are mostly still using the same trick. The thing where you divide the video up into one image per second and you dump that all into the context.

[00:27:16] Simon: So maybe it shouldn't have been so surprising to us that long context models plus vision meant that the video was, was starting to be solved. Of course, it didn't. Not being, you, what you really want with videos, you want to be able to do the audio and the images at the same time. And I think the models are beginning to do that now.

[00:27:33] Simon: Like, originally, Gemini 1. 5 Pro originally ignored the audio. It just did the, the, like, one frame per second video trick. As far as I can tell, the most recent ones are actually doing pure multimodal. But the things that opens up are just extraordinary. Like, the the ChatGPT iPhone app feature that they shipped as one of their 12 days of, of OpenAI, I really can be having a conversation and just turn on my video camera and go, Hey, what kind of tree is [00:28:00] this?

[00:28:00] Simon: And so forth. And it works. And for all I know, that's just snapping a like picture once a second and feeding it into the model. The, the, the things that you can do with that as an end user are extraordinary. Like that, that to me, I don't think most people have cottoned onto the fact that you can now stream video directly into a model because it, it's only a few weeks old.

[00:28:22] Simon: Wow. That's a, that's a, that's a, that's Big boost in terms of what kinds of things you can do with this stuff. Yeah. For

[00:28:30] swyx (2): people who are not that close I think Gemini Flashes free tier allows you to do something like capture a photo, one photo every second or a minute and leave it on 24, seven, and you can prompt it to do whatever.

[00:28:45] swyx (2): And so you can effectively have your own camera app or monitoring app that that you just prompt and it detects where it changes. It detects for, you know, alerts or anything like that, or describes your day. You know, and, and, and the fact that this is free I think [00:29:00] it's also leads into the previous point of it being the prices haven't come down a lot.

[00:29:05] Simon: And even if you're paying for this stuff, like a thing that I put in my blog entry is I ran a calculation on what it would cost to process 68, 000 photographs in my photo collection, and for each one just generate a caption, and using Gemini 1. 5 Flash 8B, it would cost me 1. 68 to process 68, 000 images, which is, I mean, that, that doesn't make sense.

[00:29:28] Simon: None of that makes sense. Like it's, it's a, for one four hundredth of a cent per image to generate captions now. So you can see why feeding in a day's worth of video just isn't even very expensive to process.

[00:29:40] swyx (2): Yeah, I'll tell you what is expensive. It's the other direction. So we're here, we're talking about consuming video.

[00:29:46] swyx (2): And this year, we also had a lot of progress, like probably one of the most excited, excited, anticipated launches of the year was Sora. We actually got Sora. And less exciting.

[00:29:55] Simon: We did, and then VO2, Google's Sora, came out like three [00:30:00] days later and upstaged it. Like, Sora was exciting until VO2 landed, which was just better.

[00:30:05] swyx (2): In general, I feel the media, or the social media, has been very unfair to Sora. Because what was released to the world, generally available, was Sora Lite. It's the distilled version of Sora, right? So you're, I did not

[00:30:16] Simon: realize that you're absolutely comparing

[00:30:18] swyx (2): the, the most cherry picked version of VO two, the one that they published on the marketing page to the, the most embarrassing version of the soa.

[00:30:25] swyx (2): So of course it's gonna look bad, so, well, I got

[00:30:27] Simon: access to the VO two I'm in the VO two beta and I've been poking around with it and. Getting it to generate pelicans on bicycles and stuff. I would absolutely

[00:30:34] swyx (2): believe that

[00:30:35] Simon: VL2 is actually better. Is Sora, so is full fat Sora coming soon? Do you know, when, when do we get to play with that one?

[00:30:42] Simon: No one's

[00:30:43] swyx (2): mentioned anything. I think basically the strategy is let people play around with Sora Lite and get info there. But the, the, keep developing Sora with the Hollywood studios. That's what they actually care about. Gotcha. Like the rest of us. Don't really know what to do with the video anyway. Right.

[00:30:59] Simon: I mean, [00:31:00] that's my thing is I realized that for generative images and images and video like images We've had for a few years and I don't feel like they've broken out into the talented artist community yet Like lots of people are having fun with them and doing and producing stuff. That's kind of cool to look at but what I want you know that that movie everything everywhere all at once, right?

[00:31:20] Simon: One, one ton of Oscars, utterly amazing film. The VFX team for that were five people, some of whom were watching YouTube videos to figure out what to do. My big question for, for Sora and and and Midjourney and stuff, what happens when a creative team like that starts using these tools? I want the creative geniuses behind everything, everywhere all at once.

[00:31:40] Simon: What are they going to be able to do with this stuff in like a few years time? Because that's really exciting to me. That's where you take artists who are at the very peak of their game. Give them these new capabilities and see, see what they can do with them.

[00:31:52] swyx (2): I should, I know a little bit here. So it should mention that, that team actually used RunwayML.

[00:31:57] swyx (2): So there was, there was,

[00:31:57] Simon: yeah.

[00:31:59] swyx (2): I don't know how [00:32:00] much I don't. So, you know, it's possible to overstate this, but there are people integrating it. Generated video within their workflow, even pre SORA. Right, because

[00:32:09] Brian: it's not, it's not the thing where it's like, okay, tomorrow we'll be able to do a full two hour movie that you prompt with three sentences.

[00:32:15] Brian: It is like, for the very first part of, of, you know video effects in film, it's like, if you can get that three second clip, if you can get that 20 second thing that they did in the matrix that blew everyone's minds and took a million dollars or whatever to do, like, it's the, it's the little bits and pieces that they can fill in now that it's probably already there.

[00:32:34] swyx (2): Yeah, it's like, I think actually having a layered view of what assets people need and letting AI fill in the low value assets. Right, like the background video, the background music and, you know, sometimes the sound effects. That, that maybe, maybe more palatable maybe also changes the, the way that you evaluate the stuff that's coming out.

[00:32:57] swyx (2): Because people tend to, in social media, try to [00:33:00] emphasize foreground stuff, main character stuff. So you really care about consistency, and you, you really are bothered when, like, for example, Sorad. Botch's image generation of a gymnast doing flips, which is horrible. It's horrible. But for background crowds, like, who cares?

[00:33:18] Brian: And by the way, again, I was, I was a film major way, way back in the day, like, that's how it started. Like things like Braveheart, where they filmed 10 people on a field, and then the computer could turn it into 1000 people on a field. Like, that's always been the way it's around the margins and in the background that first comes in.

[00:33:36] Brian: The

[00:33:36] Simon: Lord of the Rings movies were over 20 years ago. Although they have those giant battle sequences, which were very early, like, I mean, you could almost call it a generative AI approach, right? They were using very sophisticated, like, algorithms to model out those different battles and all of that kind of stuff.

[00:33:52] Simon: Yeah, I know very little. I know basically nothing about film production, so I try not to commentate on it. But I am fascinated to [00:34:00] see what happens when, when these tools start being used by the real, the people at the top of their game.

[00:34:05] swyx (2): I would say like there's a cultural war that is more that being fought here than a technology war.

[00:34:11] swyx (2): Most of the Hollywood people are against any form of AI anyway, so they're busy Fighting that battle instead of thinking about how to adopt it and it's, it's very fringe. I participated here in San Francisco, one generative AI video creative hackathon where the AI positive artists actually met with technologists like myself and then we collaborated together to build short films and that was really nice and I think, you know, I'll be hosting some of those in my events going forward.

[00:34:38] swyx (2): One thing that I think like I want to leave it. Give people a sense of it's like this is a recap of last year But then sometimes it's useful to walk away as well with like what can we expect in the future? I don't know if you got anything. I would also call out that the Chinese models here have made a lot of progress Hyde Law and Kling and God knows who like who else in the video arena [00:35:00] Also making a lot of progress like surprising him like I think maybe actually Chinese China is surprisingly ahead with regards to Open8 at least, but also just like specific forms of video generation.

[00:35:12] Simon: Wouldn't it be interesting if a film industry sprung up in a country that we don't normally think of having a really strong film industry that was using these tools? Like, that would be a fascinating sort of angle on this. Mm hmm. Mm hmm.

[00:35:25] swyx (2): Agreed. I, I, I Oh, sorry. Go ahead.

[00:35:29] Exploring Video Avatar Companies

[00:35:29] swyx (2): Just for people's Just to put it on people's radar as well, Hey Jen, there's like there's a category of video avatar companies that don't specifically, don't specialize in general video.

[00:35:41] swyx (2): They only do talking heads, let's just say. And HeyGen sings very well.

[00:35:45] Brian: Swyx, you know that that's what I've been using, right? Like, have, have I, yeah, right. So, if you see some of my recent YouTube videos and things like that, where, because the beauty part of the HeyGen thing is, I, I, I don't want to use the robot voice, so [00:36:00] I record the mp3 file for my computer, And then I put that into HeyGen with the avatar that I've trained it on, and all it does is the lip sync.

[00:36:09] Brian: So it looks, it's not 100 percent uncanny valley beatable, but it's good enough that if you weren't looking for it, it's just me sitting there doing one of my clips from the show. And, yeah, so, by the way, HeyGen. Shout out to them.

[00:36:24] AI Influencers and Their Future

[00:36:24] swyx (2): So I would, you know, in terms of like the look ahead going, like, looking, reviewing 2024, looking at trends for 2025, I would, they basically call this out.

[00:36:33] swyx (2): Meta tried to introduce AI influencers and failed horribly because they were just bad at it. But at some point that there will be more and more basically AI influencers Not in a way that Simon is but in a way that they are not human.

[00:36:50] Simon: Like the few of those that have done well, I always feel like they're doing well because it's a gimmick, right?

[00:36:54] Simon: It's a it's it's novel and fun to like Like that, the AI Seinfeld thing [00:37:00] from last year, the Twitch stream, you know, like those, if you're the only one or one of just a few doing that, you'll get, you'll attract an audience because it's an interesting new thing. But I just, I don't know if that's going to be sustainable longer term or not.

[00:37:11] Simon: Like,

[00:37:12] Simplifying Content Creation with AI

[00:37:12] Brian: I'm going to tell you, Because I've had discussions, I can't name the companies or whatever, but, so think about the workflow for this, like, now we all know that on TikTok and Instagram, like, holding up a phone to your face, and doing like, in my car video, or walking, a walk and talk, you know, that's, that's very common, but also, if you want to do a professional sort of talking head video, you still have to sit in front of a camera, you still have to do the lighting, you still have to do the video editing, versus, if you can just record, what I'm saying right now, the last 30 seconds, If you clip that out as an mp3 and you have a good enough avatar, then you can put that avatar in front of Times Square, on a beach, or whatever.

[00:37:50] Brian: So, like, again for creators, the reason I think Simon, we're on the verge of something, it, it just, it's not going to, I think it's not, oh, we're going to have [00:38:00] AI avatars take over, it'll be one of those things where it takes another piece of the workflow out and simplifies it. I'm all

[00:38:07] Simon: for that. I, I always love this stuff.

[00:38:08] Simon: I like tools. Tools that help human beings do more. Do more ambitious things. I'm always in favor of, like, that, that, that's what excites me about this entire field.

[00:38:17] swyx (2): Yeah. We're, we're looking into basically creating one for my podcast. We have this guy Charlie, he's Australian. He's, he's not real, but he pre, he opens every show and we are gonna have him present all the shorts.

[00:38:29] Simon: Yeah, go ahead.

[00:38:30] The Importance of Credibility in AI

[00:38:30] Simon: The thing that I keep coming back to is this idea of credibility like in a world that is full of like AI generated everything and so forth It becomes even more important that people find the sources of information that they trust and find people and find Sources that are credible and I feel like that's the one thing that LLMs and AI can never have is credibility, right?

[00:38:49] Simon: ChatGPT can never stake its reputation on telling you something useful and interesting because That means nothing, right? It's a matrix multiplication. It depends on who prompted it and so forth. So [00:39:00] I'm always, and this is when I'm blogging as well, I'm always looking for, okay, who are the reliable people who will tell me useful, interesting information who aren't just going to tell me whatever somebody's paying them to tell, tell them, who aren't going to, like, type a one sentence prompt into an LLM and spit out an essay and stick it online.

[00:39:16] Simon: And that, that to me, Like, earning that credibility is really important. That's why a lot of my ethics around the way that I publish are based on the idea that I want people to trust me. I want to do things that, that gain credibility in people's eyes so they will come to me for information as a trustworthy source.

[00:39:32] Simon: And it's the same for the sources that I'm, I'm consulting as well. So that's something I've, I've been thinking a lot about that sort of credibility focus on this thing for a while now.

[00:39:40] swyx (2): Yeah, you can layer or structure credibility or decompose it like so one thing I would put in front of you I'm not saying that you should Agree with this or accept this at all is that you can use AI to generate different Variations and then and you pick you as the final sort of last mile person that you pick The last output and [00:40:00] you put your stamp of credibility behind that like that everything's human reviewed instead of human origin

[00:40:04] Simon: Yeah, if you publish something you need to be able to put it on the ground Publishing it.

[00:40:08] Simon: You need to say, I will put my name to this. I will attach my credibility to this thing. And if you're willing to do that, then, then that's great.

[00:40:16] swyx (2): For creators, this is huge because there's a fundamental asymmetry between starting with a blank slate versus choosing from five different variations.

[00:40:23] Brian: Right.

[00:40:24] Brian: And also the key thing that you just said is like, if everything that I do, if all of the words were generated by an LLM, if the voice is generated by an LLM. If the video is also generated by the LLM, then I haven't done anything, right? But if, if one or two of those, you take a shortcut, but it's still, I'm willing to sign off on it.

[00:40:47] Brian: Like, I feel like that's where I feel like people are coming around to like, this is maybe acceptable, sort of.

[00:40:53] Simon: This is where I've been pushing the definition. I love the term slop. Where I've been pushing the definition of slop as AI generated [00:41:00] content that is both unrequested and unreviewed and the unreviewed thing is really important like that's the thing that elevates something from slop to not slop is if A human being has reviewed it and said, you know what, this is actually worth other people's time.

[00:41:12] Simon: And again, I'm willing to attach my credibility to it and say, hey, this is worthwhile.

[00:41:16] Brian: It's, it's, it's the cura curational, curatorial and editorial part of it that no matter what the tools are to do shortcuts, to do, as, as Swyx is saying choose between different edits or different cuts, but in the end, if there's a curatorial mind, Or editorial mind behind it.

[00:41:32] Brian: Let me I want to wedge this in before we start to close.

[00:41:36] The Future of LLM User Interfaces

[00:41:36] Brian: One of the things coming back to your year end piece that has been a something that I've been banging the drum about is when you're talking about LLMs. Getting harder to use. You said most users are thrown in at the deep end.

[00:41:48] Brian: The default LLM chat UI is like taking brand new computer users, dropping them into a Linux terminal and expecting them to figure it all out. I mean, it's, it's literally going back to the command line. The command line was defeated [00:42:00] by the GUI interface. And this is what I've been banging the drum about is like, this cannot be.

[00:42:05] Brian: The user interface, what we have now cannot be the end result. Do you see any hints or seeds of a GUI moment for LLM interfaces?

[00:42:17] Simon: I mean, it has to happen. It absolutely has to happen. The the, the, the, the usability of these things is turning into a bit of a crisis. And we are at least seeing some really interesting innovation in little directions.

[00:42:28] Simon: Just like OpenAI's chat GPT canvas thing that they just launched. That is at least. Going a little bit more interesting than just chat, chats and responses. You know, you can, they're exploring that space where you're collaborating with an LLM. You're both working in the, on the same document. That makes a lot of sense to me.

[00:42:44] Simon: Like that, that feels really smart. The one of the best things is still who was it who did the, the UI where you could, they had a drawing UI where you draw an interface and click a button. TL draw would then make it real thing. That was spectacular, [00:43:00] absolutely spectacular, like, alternative vision of how you'd interact with these models.

[00:43:05] Simon: Because yeah, the and that's, you know, so I feel like there is so much scope for innovation there and it is beginning to happen. Like, like, I, I feel like most people do understand that we need to do better in terms of interfaces that both help explain what's going on and give people better tools for working with models.

[00:43:23] Simon: I was going to say, I want to

[00:43:25] Brian: dig a little deeper into this because think of the conceptual idea behind the GUI, which is instead of typing into a command line open word. exe, it's, you, you click an icon, right? So that's abstracting away sort of the, again, the programming stuff that like, you know, it's, it's a, a, a child can tap on an iPad and, and make a program open, right?

[00:43:47] Brian: The problem it seems to me right now with how we're interacting with LLMs is it's sort of like you know a dumb robot where it's like you poke it and it goes over here, but no, I want it, I want to go over here so you poke it this way and you can't get it exactly [00:44:00] right, like, what can we abstract away from the From the current, what's going on that, that makes it more fine tuned and easier to get more precise.

[00:44:12] Brian: You see what I'm saying?

[00:44:13] Simon: Yes. And the this is the other trend that I've been following from the last year, which I think is super interesting. It's the, the prompt driven UI development thing. Basically, this is the pattern where Claude Artifacts was the first thing to do this really well. You type in a prompt and it goes, Oh, I should answer that by writing a custom HTML and JavaScript application for you that does a certain thing.

[00:44:35] Simon: And when you think about that take and since then it turns out This is easy, right? Every decent LLM can produce HTML and JavaScript that does something useful. So we've actually got this alternative way of interacting where they can respond to your prompt with an interactive custom interface that you can work with.

[00:44:54] Simon: People haven't quite wired those back up again. Like, ideally, I'd want the LLM ask me a [00:45:00] question where it builds me a custom little UI, For that question, and then it gets to see how I interacted with that. I don't know why, but that's like just such a small step from where we are right now. But that feels like such an obvious next step.

[00:45:12] Simon: Like an LLM, why should it, why should you just be communicating with, with text when it can build interfaces on the fly that let you select a point on a map or or move like sliders up and down. It's gonna create knobs and dials. I keep saying knobs and dials. right. We can do that. And the LLMs can build, and Claude artifacts will build you a knobs and dials interface.

[00:45:34] Simon: But at the moment they haven't closed the loop. When you twiddle those knobs, Claude doesn't see what you were doing. They're going to close that loop. I'm, I'm shocked that they haven't done it yet. So yeah, I think there's so much scope for innovation and there's so much scope for doing interesting stuff with that model where the LLM, anything you can represent in SVG, which is almost everything, can now be part of that ongoing conversation.

[00:45:59] swyx (2): Yeah, [00:46:00] I would say the best executed version of this I've seen so far is Bolt where you can literally type in, make a Spotify clone, make an Airbnb clone, and it actually just does that for you zero shot with a nice design.

[00:46:14] Simon: There's a benchmark for that now. The LMRena people now have a benchmark that is zero shot app, app generation, because all of the models can do it.

[00:46:22] Simon: Like it's, it's, I've started figuring out. I'm building my own version of this for my own project, because I think within six months. I think it'll just be an expected feature. Like if you have a web application, why don't you have a thing where, oh, look, the, you can add a custom, like, so for my dataset data exploration project, I want you to be able to do things like conjure up a dashboard, just via a prompt.

[00:46:43] Simon: You say, oh, I need a pie chart and a bar chart and put them next to each other, and then have a form where submitting the form inserts a row into my database table. And this is all suddenly feasible. It's, it's, it's not even particularly difficult to do, which is great. Utterly bizarre that these things are now easy.[00:47:00]

[00:47:00] swyx (2): I think for a general audience, that is what I would highlight, that software creation is becoming easier and easier. Gemini is now available in Gmail and Google Sheets. I don't write my own Google Sheets formulas anymore, I just tell Gemini to do it. And so I think those are, I almost wanted to basically somewhat disagree with, with your assertion that LMS got harder to use.

[00:47:22] swyx (2): Like, yes, we, we expose more capabilities, but they're, they're in minor forms, like using canvas, like web search in, in in chat GPT and like Gemini being in, in Excel sheets or in Google sheets, like, yeah, we're getting, no,

[00:47:37] Simon: no, no, no. Those are the things that make it harder, because the problem is that for each of those features, they're amazing.

[00:47:43] Simon: If you understand the edges of the feature, if you're like, okay, so in Google, Gemini, Excel formulas, I can get it to do a certain amount of things, but I can't get it to go and read a web. You probably can't get it to read a webpage, right? But you know, there are, there are things that it can do and things that it can't do, which are completely undocumented.

[00:47:58] Simon: If you ask it what it [00:48:00] can and can't do, they're terrible at answering questions about that. So like my favorite example is Claude artifacts. You can't build a Claude artifact that can hit an API somewhere else. Because the cause headers on that iframe prevents accessing anything outside of CDNJS. So, good luck learning cause headers as an end user in order to understand why Like, I've seen people saying, oh, this is rubbish.

[00:48:26] Simon: I tried building an artifact that would run a prompt and it couldn't because Claude didn't expose an API with cause headers that all of this stuff is so weird and complicated. And yeah, like that, that, the more that with the more tools we add, the more expertise you need to really, To understand the full scope of what you can do.

[00:48:44] Simon: And so it's, it's, I wouldn't say it's, it's, it's, it's like, the question really comes down to what does it take to understand the full extent of what's possible? And honestly, that, that's just getting more and more involved over time.

[00:48:58] Local LLMs: A Growing Interest

[00:48:58] swyx (2): I have one more topic that I, I [00:49:00] think you, you're kind of a champion of and we've touched on it a little bit, which is local LLMs.

[00:49:05] swyx (2): And running AI applications on your desktop, I feel like you are an early adopter of many, many things.

[00:49:12] Simon: I had an interesting experience with that over the past year. Six months ago, I almost completely lost interest. And the reason is that six months ago, the best local models you could run, There was no point in using them at all, because the best hosted models were so much better.

[00:49:26] Simon: Like, there was no point at which I'd choose to run a model on my laptop if I had API access to Cloud 3. 5 SONNET. They just, they weren't even comparable. And that changed, basically, in the past three months, as the local models had this step changing capability, where now I can run some of these local models, and they're not as good as Cloud 3.

[00:49:45] Simon: 5 SONNET, but they're not so far away that It's not worth me even using them. The other, the, the, the, the continuing problem is I've only got 64 gigabytes of RAM, and if you run, like, LLAMA370B, it's not going to work. Most of my RAM is gone. So now I have to shut down my Firefox tabs [00:50:00] and, and my Chrome and my VS Code windows in order to run it.

[00:50:03] Simon: But it's got me interested again. Like, like the, the efficiency improvements are such that now, if you were to like stick me on a desert island with my laptop, I'd be very productive using those local models. And that's, that's pretty exciting. And if those trends continue, and also, like, I think my next laptop, if when I buy one is going to have twice the amount of RAM, At which point, maybe I can run the, almost the top tier, like open weights models and still be able to use it as a computer as well.

[00:50:32] Simon: NVIDIA just announced their 3, 000 128 gigabyte monstrosity. That's pretty good price. You know, that's that's, if you're going to buy it,

[00:50:42] swyx (2): custom OS and all.

[00:50:46] Simon: If I get a job, if I, if, if, if I have enough of an income that I can justify blowing $3,000 on it, then yes.

[00:50:52] swyx (2): Okay, let's do a GoFundMe to get Simon one it.

[00:50:54] swyx (2): Come on. You know, you can get a job anytime you want. Is this, this is just purely discretionary .

[00:50:59] Simon: I want, [00:51:00] I want a job that pays me to do exactly what I'm doing already and doesn't tell me what else to do. That's, thats the challenge.

[00:51:06] swyx (2): I think Ethan Molik does pretty well. Whatever, whatever it is he's doing.

[00:51:11] swyx (2): But yeah, basically I was trying to bring in also, you know, not just local models, but Apple intelligence is on every Mac machine. You're, you're, you seem skeptical. It's rubbish.

[00:51:21] Simon: Apple intelligence is so bad. It's like, it does one thing well.

[00:51:25] swyx (2): Oh yeah, what's that? It summarizes notifications. And sometimes it's humorous.

[00:51:29] Brian: Are you sure it does that well? And also, by the way, the other, again, from a sort of a normie point of view. There's no indication from Apple of when to use it. Like, everybody upgrades their thing and it's like, okay, now you have Apple Intelligence, and you never know when to use it ever again.

[00:51:47] swyx (2): Oh, yeah, you consult the Apple docs, which is MKBHD.

[00:51:49] swyx (2): The

[00:51:51] Simon: one thing, the one thing I'll say about Apple Intelligence is, One of the reasons it's so disappointing is that the models are just weak, but now, like, Llama 3b [00:52:00] is Such a good model in a 2 gigabyte file I think give Apple six months and hopefully they'll catch up to the state of the art on the small models And then maybe it'll start being a lot more interesting.

[00:52:10] swyx (2): Yeah. Anyway, I like This was year one And and you know just like our first year of iPhone maybe maybe not that much of a hit and then year three They had the App Store so Hey I would say give it some time, and you know, I think Chrome also shipping Gemini Nano I think this year in Chrome, which means that every app, every web app will have for free access to a local model that just ships in the browser, which is kind of interesting.

[00:52:38] swyx (2): And then I, I think I also wanted to just open the floor for any, like, you know, any of us what are the apps that, you know, AI applications that we've adopted that have, that we really recommend because these are all, you know, apps that are running on our browser that like, or apps that are running locally that we should be, that, that other people should be trying.

[00:52:55] swyx (2): Right? Like, I, I feel like that's, that's one always one thing that is helpful at the start of the [00:53:00] year.

[00:53:00] Simon: Okay. So for running local models. My top picks, firstly, on the iPhone, there's this thing called MLC Chat, which works, and it's easy to install, and it runs Llama 3B, and it's so much fun. Like, it's not necessarily a capable enough novel that I use it for real things, but my party trick right now is I get my phone to write a Netflix Christmas movie plot outline where, like, a bunch of Jeweller falls in love with the King of Sweden or whatever.

[00:53:25] Simon: And it does a good job and it comes up with pun names for the movies. And that's, that's deeply entertaining. On my laptop, most recently, I've been getting heavy into, into Olama because the Olama team are very, very good at finding the good models and patching them up and making them work well. It gives you an API.

[00:53:42] Simon: My little LLM command line tool that has a plugin that talks to Olama, which works really well. So that's my, my Olama is. I think the easiest on ramp to to running models locally, if you want a nice user interface, LMStudio is, I think, the best user interface [00:54:00] thing at that. It's not open source. It's good.

[00:54:02] Simon: It's worth playing with. The other one that I've been trying with recently, there's a thing called, what's it called? Open web UI or something. Yeah. The UI is fantastic. It, if you've got Olama running and you fire this thing up, it spots Olama and it gives you an interface onto your Olama models. And that's really nicely done.

[00:54:19] Simon: That's that, that, that, that's, that's my current favorite, like open source UI for these things. But yeah, so there's lots of good options. You do need a lot of disk space. Like the, the, the models are, the, the best, the, the models start at two gigabytes for like the 3B models that are actually worth playing with.

[00:54:35] Simon: The, the really impressive ones tend to be in the sort of 20 to 30 gigabyte range in my experience.

[00:54:40] swyx (2): Yeah, I think my, my struggle here is I'm not that much of a absolutist in terms of running things locally. Like I'm happy to call an API. Same here. I do it to play.

[00:54:53] Simon: It's my research interest, yeah. When people

[00:54:55] swyx (2): get so excited

[00:54:56] Brian: Answer your own question.

[00:54:59] swyx (2): Like, give us [00:55:00] more apps that you wanna Yeah, sometimes it's like, it's just nice to recommend apps. So, I use SuperWhisperer now. I tried WhisperFlow, didn't really work for me. SuperWhisperer is one of them, which basically replaces typing. Like, you should just type. Talk, most of the time, especially if you're doing anything long form.

[00:55:19] swyx (2): You hold, I hold down caps lock and I, and I talk. And then when I'm done, I lift it up and it uses, it doesn't, it's not just about writing down your transcripts because I make ums and ahs all the time. I restate myself, myself all the time, but it uses GPT 4 to rewrite. And that's what these guys are doing.

[00:55:33] swyx (2): They're all doing some form of state of the art ASR, automatic speech recognition, and then, and then and LLM to rewrite. And then I think I would also recommend. For people to check out Rosebud for journaling. I think AI for mental health is quite unexplored and it's not because we are trying to build AI therapists.

[00:55:51] swyx (2): I think the therapists really hate that. You'll, you'll never be on the level of therapist that, that gets back to the human

[00:55:57] Brian: thing that we were discussing, you know, on, on, [00:56:00] on some level. There are certain things and disciplines that require the human touch and that might be sure.

[00:56:05] swyx (2): But the human touch cost me 300 an hour, right?

[00:56:09] swyx (2): And then this thing's, this thing's 3 a month, you know. So there's a, there's a spectrum of people for, for whom that will work. And I think it's, it's cheap now to try all these things.

[00:56:21] Simon: I'm going to throw in a quick recommendation for an app. Mac Whisper is my favorite desktop app. I love that thing.

[00:56:29] Simon: It runs Whisper, and you can do things like you can paste in the URL to a YouTube video and it'll pull the audio and give you a transcript. So, that's how I watch YouTube now, is I slap it into Mac Whisper, and then I hit copy and paste into Claude, and then I use the Claude web app to do things. But Mac Whisper, it works with mp3 files.

[00:56:46] Simon: Every time I'm on a podcast, I dump the mp3 into Mac Whisper, then I dump the transcript into Claude and say, And What should I put in the show notes? And it spits out a bullet point list where it says, Oh, you mentioned, like, data set that you should link to that, that kind of thing. [00:57:00] Stuff like that, that's Mac Whisperer, I use it several times a day, to be honest.

[00:57:03] Simon: Like, it's, it's, it's great. Yeah.

[00:57:05] Brian: I'm actually, I'm going to say one that is incredibly super basic, and again, coming back to just my workflow, but we are currently recording this on Riverside. Riverside is a great tool for recording video, audio things like we're doing right now, but I always use this as an example to folks when they're like, well, how, what will AI do for me when I first started using Riverside, like we're recording three different channels right now.

[00:57:29] Brian: Right. You guys are recording locally, so there's three audio files, three video files. And then, when I first started using Riverside, you had to pump three tracks into Adobe and then edit. Okay, now we focus on Simon, now we focus on Swyx, now we focus on Brian, now we do all three. And then one day, a tool popped up that says hit this button, and it's smart edit.

[00:57:52] Brian: And then, the AI determines, okay, Simon has been talking for 30 minutes, so go to the full shot of him. [00:58:00] And Brian is now talking, or there's overtalk, so let's have all three talking heads. With one button, for anything I posted, it saved me Three or four hours worth of work. That, to me, is like, again, if normies are listening

[00:58:14] Simon: Riverside has that feature now.

[00:58:15] Brian: Yeah.

[00:58:15] swyx (2): Yeah. Yeah.

[00:58:17] Simon: Damn. I don't use it. Oh, that

[00:58:18] swyx (2): sounds fantastic. I still use a human editor.

[00:58:21] Brian: The day it came out, I was running around the house, telling my wife, telling anyone that would listen, you don't know, I just saved three hours because they had a new feature. Like, that's That's exciting. Brian's

[00:58:32] swyx (2): basically crying with joy right now.

[00:58:35] Brian: Alright let's, let's try to bring this to a landing a little bit. Simon, I have about maybe two or three more. We can do these rapid fire. Cool. One of my shows, one of the things of my show is, it's sort of like Silicon Valley writ large, so it's sort of like the horse race of who's up and who's down or whatever.

[00:58:52] Brian: To the degree that you're interested in pontificating on this, OpenAI is a company in 2025. Do you [00:59:00] see challenges coming? Are you bearish, bullish? I almost, I'm doing a CNBC sort of thing, but like, how do you feel about OpenAI this year?

[00:59:06] Simon: I think, I think they're in a bit of trouble. They seem to have lost a lot of talent.

[00:59:10] Simon: Like, they're losing, and they don't have that, if it wasn't for O3, they'd be in massive trouble, because they'd have lost that, like, top of the pile thing. I think O3 clawed them back up again, but one of the big stories of 2024 is OpenAI started as the clear leader. And now, Google Gemini is really good, like, Google Gemini had an amazing year.

[00:59:28] Simon: Anthropic Claude, Claude 3. 5 Sonnet is still my personal favorite model. And that feels notable, like, like, OpenAI went from, like, nobody would argue they were not the, the leader in all of this stuff a year ago, and today, They're still doing great, but they're not, like, as far ahead as they were.

[00:59:47] Brian: Next question, and maybe this couldn't be as rapid fire, but I loved, finally, from your piece, the idea that LLMs need better criticism, which I'd love you to expand on, because as I sort of straddle this world of tech journalism and [01:00:00] creator and investor and all that stuff I thought that you had a really interesting thing to say about how, and we even alluded to this about, like, Hollywood being against it, like, Better criticism in the sense that, as I took it, everybody is sort of, they've got their hackles up, they're trying to defend their livelihoods and things like that.

[01:00:19] Brian: But it's either, this is gonna destroy my job and destroy the world, or, like, I'm, sorry, I'm again leading the witness. What did you mean by LLMs need better criticism?

[01:00:30] Simon: So this is a frustration I have, that I, like, if I read a discussion thread somewhere about, on this topic, I can predict exactly what everyone's going to say.

[01:00:38] Simon: People talk about the environmental impact, they talk about the plagiarism of the training data, the unlicensed training data. They'll, there's often this sort of, oh, and these things are completely useless thing. That's the one that I will push back against. The other things are true, right? The, the idea that LLMs are just completely useless, that the, the argument I always make there is, they are Very useful, if you understand how to use them, which is distinctly [01:01:00] unintuitive.

[01:01:00] Simon: Like, you have to learn how to deal with something that will just wildly hallucinate and make things up, and all of those kinds of things. If you can learn how to, what they're good at and what they're bad at, I use them dozens of times a day, and I get enormous value out of them. So I'll push back on people who say, no, they're just useless.

[01:01:16] Simon: But the other things, you know, the environmental impact of the, the way the training data works, I feel like the training data one's interesting, because It's probably legal under fair use, but it's clearly unfair if somebody takes your work without your permission and trains a model which then competes with you in the marketplace.

[01:01:33] Simon: Like, like, legal or not, that, that, that's, that's, I, I understand why people are upset about that, that, that's a reasonable thing to be upset by. So What I want, and I also feel like the impact that this stuff can have on society, especially as it starts undermining all sorts of jobs that we never thought were going to be undermined by technology.

[01:01:50] Simon: Like, who thought it would come for artists and lawyers first, right? That's bizarre. We need to have really high quality conversations where we help people figure out what works, what doesn't [01:02:00] work. We need people to be able to make good decisions about what to do with their careers to embrace this stuff and all of that sort of stuff.

[01:02:06] Simon: And if we just get distracted by saying, yeah, but it's, it's, it's useless plagiarism driven, like environmental vent, vently contrast catastrophic. Even though those things represent quite a lot of truth, I don't think that that's a useful message to, to lead with. Like, I want to be having the much more interesting high level conversations.

[01:02:24] Simon: Oh, okay. Well, if there are negatives, how do we, what do we do to counter those negatives? If there are positives, how do we encourage those? How do we help people make good decisions about how to use this technology?

[01:02:36] swyx (2): I, I think, I, where I see this the most is for people who are kind of very in internal, like sort of you and I are immersed in this every single day, so we're frankly tired of the same debates being recycled again and again.

[01:02:50] swyx (2): I think what might be more useful or, you know, More impactful is the level at which it starts to hit regulation. Last year, we had a couple [01:03:00] of very notable attempts at the White House level and in the California level to regulate AI, and those did not come to pass. But at some point, these criticisms bubble up to law, to matters of national security or national Science in progress.

[01:03:17] swyx (2): And I, like, I feel like there needs to be more information or enlightenment there, maybe? If only because it tends to be that they're very trailing. Like the, you know, my favorite example to pick on, which is very unfair of me, but whatever you know, the, the California SB 1047 Act tried to cap compute at 10 to the power 25.

[01:03:38] swyx (2): So that's a deep sink. Exactly. Well, it also is exactly at the point at which we pivoted from training GPT 5 to O1, where there is no longer scaling pre trained compute. What I'm saying is like, we're always trying to regulate the last war, and I don't think that works in a field that is basically 8 years old.[01:04:00]

[01:04:00] Simon: I think I've got, there are two, there are two areas of regulation I'm super interested in that, that, that one of them is I do think that regulating the way these things are used can work. The big example is I don't want somebody's insurance claim denied by a black box LLM where nobody can explain what it did.

[01:04:16] Simon: Like that just feels Oh, we have laws for

[01:04:17] Speaker 4: that. Exactly.

[01:04:18] Simon: This is like gridlining. Well Yeah, take those laws, reinforce them, update them for modern capabilities. And then the other one there's some really interesting stuff around privacy. Like we've got this huge problem right now where People will refuse to use any of these tools because they don't trust that the things they say to it won't be trained on and then exposed to other people.

[01:04:37] Simon: And there are lots of terms and conditions that you can read through and try and navigate around. I would love there to be just really straightforward laws that people understand where They know that it's not going to train on their input because there's a law that says under these circumstances that that can't happen.

[01:04:52] Simon: Like that sort of stuff, like, like, it's basically taking our existing privacy laws and giving them a few more teeth and just reinforcing them without [01:05:00] introducing cookie banners a la the European Union, right? There's, these things are always very, it's very risky to try and get this stuff right because you can have all sorts of bad results if you don't design them correctly, but that, that's, there's space for that, I think.

[01:05:15] Brian: Yeah, I, when I read that piece, and then when you just said you know Swyx said we, we're in the weeds on this every single day, so we're tired of hearing these arguments. It reminds me of folks that are always into politics, and then they're like, They're mad at the people that don't care about politics until it's an election year.

[01:05:34] Brian: And then they're like, well, you're a low information voter because all you know is that the factory in your town got shut down or there's inflation or whatever. And so you vote one way or the other, but you haven't been paying attention. But that's kind of the point. So, what I'm trying to say is that you shouldn't expect normal people to pay attention, except for the fact that, oh, this might lose me my job.

[01:05:52] Brian: So you can't, you can't blame them for being, I don't know, reactionary is the word, or emotional. But, [01:06:00] right if you're in the weeds, it's harder to, to keep up. Everybody informed, and this is gonna touch everybody. So I dunno. Okay, so this is the very last one. And then, and then we can wrap and, and do plugs and everything.

[01:06:12] Brian: But Simon, this is for you. It was kind of alluded to a little bit, and you might not have one, but if there's something this year that an a generalist like me is not aware that is coming down the pike that you think is gonna be big in the AI space. And maybe Shawn, if you've got one too what do you think it would be?

[01:06:31] Simon: I think for most people who haven't been paying attention, we know these things already. We know that the models are now almost free to run things against. The the fact that you can now do video, like stream video to a model, the one that I've not played with nearly as much, but the thing where you can share your entire screen with a model and get feedback there, that's going to be really useful.

[01:06:49] Simon: Like that's, Again, the privacy side of things really matters though. I do not want some model just training on everything that it sees on my screen, but no, there's that, that I feel like, like, the [01:07:00] stuff that is now possible as of a few months ago is, is, that's enough. I don't need anything new. That's going to keep me busy all year.

[01:07:07] swyx (2): Swyx are you going? Simon's always too content, and then he sees the next thing and he's like, Oh yeah, that's great too. Okay, I love trying to be contrarian by saying, What does everyone hate right now?

[01:07:22] AI Wearables: The Next Big Thing

[01:07:22] swyx (2): Remember this time last year, we just had CES, Rabbit R1, we had the humane, Wearables, wearables, yep.

[01:07:29] swyx (2): Those are completely in the gutter, no one will touch them, they're toxic nuclear waste. Okay, this year is the year of wearables.

[01:07:36] Brian: Yep, yep. I agree with you. By the way, that cycle, that cycle always works out where, like, you go to a CES and it's everything, hype, hype, hype, hype, and then three years later it becomes the thing, unless it's 3D TVs, in which case that was a mistake anyway.

[01:07:52] Brian: But yeah.

[01:07:53] Simon: Transparent TVs are the big thing for the last couple of years. What the hell?

[01:07:56] swyx (2): Yeah you know, so I think Simon may have got one of these, [01:08:00] but there are a lot of people working on AI wearables here in SF. They are surprisingly cheap, surprisingly capable and with decent battery life, and they do useful things.

[01:08:09] swyx (2): We have to work out the privacy aspect, of course. But people like Limitless which used to be called re privacy. I think they're shipping one of these wearables that based on your voice only records your voice. So you opted. Interesting. Right. Right. And so you can have perfect memory if you want.

[01:08:26] swyx (2): You can have perfect memory at work. Your employer can buy these for you that only, it only applies at work and it's fine. It's, it's just a meeting aid. Lots of people use granola or some kind of fireflies or like some of these meeting recorders only for, for meetings. Online meetings. But what about in person meetings?

[01:08:41] swyx (2): What about conversations and locations? That you've been? And some of that should be a choice. Right now you have zero choice you, and I think these wearables will enable some of that. And it's, it's up to us as a society to determine what's Acceptable and what's not. I really like these gray areas where we still don't know [01:09:00] yet.

[01:09:00] swyx (2): People, whenever I tell people about this, they're like, I don't know, like, I'm sure I guess it's like, as though you have perfect memory. But some people have better memory than others. Like, Where's the light?

[01:09:12] Brian: And there will be a lot more of these. I would add to that because Swyx, as you know, because you listen to my show the idea that AI has taken the smart glasses and completely changed everyone's mind about that as a product category and form factor.

[01:09:28] Brian: And I should say this. From things that I've been looking at investing in wait till you see what they can add on to earbuds. Like, like the earbuds in your ear can do a lot more things than they're doing now and then you combine that with smart glasses, And you combine that with an LLM that you can access, maybe with a a phone as like the, the mothership.

[01:09:48] Brian: There's some interesting things. The CES next year is gonna be crazy if you think wearables are crazy. AI wearables are a thing. Anyway, this year they were not a thing.

[01:09:57] swyx (2): There

[01:09:57] Brian: were

[01:09:57] swyx (2): very much no wearables this

[01:09:59] Simon: [01:10:00] year. This one's interesting as well, because the thing that makes these interesting is multimodal, like audio input, video input, image input, which a year ago was hardly a thing, and now it's dirt cheap.

[01:10:11] Simon: So yeah, we're 12 months ago to build the software behind this stuff.

[01:10:16] Brian: Yeah, all right.

[01:10:16] Wrapping Up and Final Thoughts

[01:10:16] Brian: Let's let's let's bring this to a landing. Swyx, go first. Tell everybody about obviously your podcast, which hopefully we're simulcasting, but also your conferences, events, everything.

[01:10:30] swyx (2): Sure, yeah, you can find my work on latent.

[01:10:33] swyx (2): space, it's the AI engineer podcast much more sort of focused on serving engineers and developers than the general audience, but you know, feel free to dive in to the deep end with us, and we are also hosting a conference in New York in February. The AI engineers summit where we gather people and this one is entirely focused on agents.

[01:10:54] swyx (2): As much as you know, people like to make fun of the idea that every year is the year of agents at work I think people at [01:11:00] least want to gather to figure out what are the open problems to solve. And so these are the These are the community of builders that get together, they show their latest work like, like I have Instacart coming to show how they use agents for their recommendation system and their, their sort of background jobs and internal jobs and we have a whole bunch of like sort of financial tech company FinTech or finance companies also showing off their work that I cannot name yet, but it'll be lots of fun.

[01:11:23] swyx (2): We, we, we do high quality events that sometimes people like Simon speak at.

[01:11:28] Brian: And that right as I said, or I think I said online or on air that I saw Simon speak at one of your events last year. Wait Swyx, just say again, it's in February. It's in New York City. I'm going to be there if that matters to anybody, if that's an attraction, but what's the dates on that and how to apply.

[01:11:43] swyx (2): I'm horrible at this. February 20th is the leadership day for management, like VPs of AI CTOs. And 21st is the engineer day, the individual contributors, hands on keyboard people. And that's when I'll have the big labs. So DeepMind, Anthropic, Meta, [01:12:00] OpenAI, all coming to share their agents work. And then we'll have some new launches as well that you haven't heard of.

[01:12:06] Brian: And to sign up to attend what website can I go to? Yeah, it's apply. ai. engineer. All right, Simon, I'm gonna, I'm gonna hold hand you, or handhold you even more. Your weblog is simonwillison. net, but what else would you like us to know or, or go find out about what you're doing?

[01:12:22] Simon: Yeah, I was gonna say my blog my other, my, my day, my day job, I call it a job is I work on open source tools for data journalism.

[01:12:29] Simon: That's my project. Dataset, spelt like the word cassette, but data dataset. io. And that's beginning to grow some interesting AI tools. Like originally it was all about data publishing and exploration and analysis. And now I'm like, okay, well, what plugins for that can I build that you use, let you use LLMs to craft queries and build dashboards and all sorts of bits and pieces like that.

[01:12:50] Simon: So I'm expecting to have some really interesting product features along those lines in the, in the next few months.

[01:12:56] Brian: And I'll end by saying, if anyone's listening to this on SWYX's [01:13:00] show I do the TechMeme Ride Home every single weekday, 15 minute long tech news podcast. Look up Ride Home on your podcast app of choice.

[01:13:08] Brian: TechMeme Ride Home. Gentlemen, thank you for your time. Thank you. This was fantastic. What a great way to start the year for, for this show.

[01:13:16] Simon: Cool. Thanks a lot for having me. This has been really fun. Yeah, thanks for having us. Honored to be on.

Applications close Monday for the NYC AI Engineer Summit focusing on AI Leadership and Agent Engineering! If you applied, invites should be rolling out shortly.

The search landscape is experiencing a fundamental shift. Google built a >$2T company with the “10 blue links” experience, driven by PageRank as the core innovation for ranking. This was a big improvement from the previous directory-based experiences of AltaVista and Yahoo. Almost 4 decades later, Google is now stuck in this links-based experience, especially from a business model perspective.

This legacy architecture creates fundamental constraints:

* Must return results in ~400 milliseconds

* Required to maintain comprehensive web coverage

* Tied to keyword-based matching algorithms

* Cost structures optimized for traditional indexing

As we move from the era of links to the era of answers, the way search works is changing. You’re not showing a user links, but the goal is to provide context to an LLM. This means moving from keyword based search to more semantic understanding of the content:

The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share... but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways.

All of this is now powered by a $5M cluster with 144 H200s:

This architectural choice enables entirely new search capabilities:

* Comprehensive result sets instead of approximations

* Deep semantic understanding of queries

* Ability to process complex, natural language requests

As search becomes more complex, time to results becomes a variable:

People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned... But what if searches can take like a minute or 10 minutes or a whole day, what can you then do?

Unlike traditional search engines' fixed-cost indexing, Exa employs a hybrid approach:

* Front-loaded compute for indexing and embeddings

* Variable inference costs based on query complexity

* Mix of owned infrastructure ($5M H200 cluster) and cloud resources

Exa sees a lot of competition from products like Perplexity and ChatGPT Search which layer AI on top of traditional search backends, but Exa is betting that true innovation requires rethinking search from the ground up. For example, the recently launched Websets, a way to turn searches into structured output in grid format, allowing you to create lists and databases out of web pages. The company raised a $17M Series A to build towards this mission, so keep an eye out for them in 2025.

Chapters

* 00:00:00 Introductions

* 00:01:12 ExaAI's initial pitch and concept

* 00:02:33 Will's background at SpaceX and Zoox

* 00:03:45 Evolution of ExaAI (formerly Metaphor Systems)

* 00:05:38 Exa's link prediction technology

* 00:09:20 Meaning of the name "Exa"

* 00:10:36 ExaAI's new product launch and capabilities

* 00:13:33 Compute budgets and variable compute products

* 00:14:43 Websets as a B2B offering

* 00:19:28 How do you build a search engine?

* 00:22:43 What is Neural PageRank?

* 00:27:58 Exa use cases

* 00:35:00 Auto-prompting

* 00:38:42 Building agentic search

* 00:44:19 Is o1 on the path to AGI?

* 00:49:59 Company culture and nap pods

* 00:54:52 Economics of AI search and the future of search technology

Full YouTube Transcript

Please like and subscribe!

Show Notes

* ExaAI

* Web Search Product

* Websets

* Series A Announcement

* Exa Nap Pods

* Perplexity AI

* Character.AI

Transcript

Alessio [00:00:00]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.

Swyx [00:00:10]: Hey, and today we're in the studio with my good friend and former landlord, Will Bryk. Roommate. How you doing? Will, you're now CEO co-founder of ExaAI, used to be Metaphor Systems. What's your background, your story?

Will [00:00:30]: Yeah, sure. So, yeah, I'm CEO of Exa. I've been doing it for three years. I guess I've always been interested in search, whether I knew it or not. Like, since I was a kid, I've always been interested in, like, high-quality information. And, like, you know, even in high school, wanted to improve the way we get information from news. And then in college, built a mini search engine. And then with Exa, like, you know, it's kind of like fulfilling the dream of actually being able to solve all the information needs I wanted as a kid. Yeah, I guess. I would say my entire life has kind of been rotating around this problem, which is pretty cool. Yeah.

Swyx [00:00:50]: What'd you enter YC with?

Will [00:00:53]: We entered YC with, uh, we are better than Google. Like, Google 2.0.

Swyx [00:01:12]: What makes you say that? Like, that's so audacious to come out of the box with.

Will [00:01:16]: Yeah, okay, so you have to remember the time. This was summer 2021. And, uh, GPT-3 had come out. Like, here was this magical thing that you could talk to, you could enter a whole paragraph, and it understands what you mean, understands the subtlety of your language. And then there was Google. Uh, which felt like it hadn't changed in a decade, uh, because it really hadn't. And it, like, you would give it a simple query, like, I don't know, uh, shirts without stripes, and it would give you a bunch of results for the shirts with stripes. And so, like, Google could barely understand you, and GBD3 could. And the theory was, what if you could make a search engine that actually understood you? What if you could apply the insights from LLMs to a search engine? And it's really been the same idea ever since. And we're actually a lot closer now, uh, to doing that. Yeah.

Alessio [00:01:55]: Did you have any trouble making people believe? Obviously, there's the same element. I mean, YC overlap, was YC pretty AI forward, even 2021, or?

Will [00:02:03]: It's nothing like it is today. But, um, uh, there were a few AI companies, but, uh, we were definitely, like, bold. And I think people, VCs generally like boldness, and we definitely had some AI background, and we had a working demo. So there was evidence that we could build something that was going to work. But yeah, I think, like, the fundamentals were there. I think people at the time were talking about how, you know, Google was failing in a lot of ways. And so there was a bit of conversation about it, but AI was not a big, big thing at the time. Yeah. Yeah.

Alessio [00:02:33]: Before we jump into Exa, any fun background stories? I know you interned at SpaceX, any Elon, uh, stories? I know you were at Zoox as well, you know, kind of like robotics at Harvard. Any stuff that you saw early that you thought was going to get solved that maybe it's not solved today?

Will [00:02:48]: Oh yeah. I mean, lots of things like that. Like, uh, I never really learned how to drive because I believed Elon that self-driving cars would happen. It did happen. And I take them every night to get home. But it took like 10 more years than I thought. Do you still not know how to drive? I know how to drive now. I learned it like two years ago. That would have been great to like, just, you know, Yeah, yeah, yeah. You know? Um, I was obsessed with Elon. Yeah. I mean, I worked at SpaceX because I really just wanted to work at one of his companies. And I remember they had a rule, like interns cannot touch Elon. And, um, that rule actually influenced my actions.

Swyx [00:03:18]: Is it, can Elon touch interns? Ooh, like physically?

Will [00:03:22]: Or like talk? Physically, physically, yeah, yeah, yeah, yeah. Okay, interesting. He's changed a lot, but, um, I mean, his companies are amazing. Um,

Swyx [00:03:28]: What if you beat him at Diablo 2, Diablo 4, you know, like, Ah, maybe.

Alessio [00:03:34]: I want to jump into, I know there's a lot of backstory used to be called metaphor system. So, um, and it, you've always been kind of like a prominent company, maybe at least RAI circles in the NSF.

Swyx [00:03:45]: I'm actually curious how Metaphor got its initial aura. You launched with like, very little. We launched very little. Like there was, there was this like big splash image of like, this is Aurora or something. Yeah. Right. And then I was like, okay, what this thing, like the vibes are good, but I don't know what it is. And I think, I think it was much more sort of maybe consumer facing than what you are today. Would you say that's true?

Will [00:04:06]: No, it's always been about building a better search algorithm, like search, like, just like the vision has always been perfect search. And if you do that, uh, we will figure out the downstream use cases later. It started on this fundamental belief that you could have perfect search over the web and we could talk about what that means. And like the initial thing we released was really just like our first search engine, like trying to get it out there. Kind of like, you know, an open source. So when OpenAI released, uh, ChachBt, like they didn't, I don't know how, how much of a game plan they had. They kind of just wanted to get something out there.

Swyx [00:04:33]: Spooky research preview.

Will [00:04:34]: Yeah, exactly. And it kind of morphed from a research company to a product company at that point. And I think similarly for us, like we were research, we started as a research endeavor with a, you know, clear eyes that like, if we succeed, it will be a massive business to make out of it. And that's kind of basically what happened. I think there are actually a lot of parallels to, of w between Exa and OpenAI. I often say we're the OpenAI of search. Um, because. Because we're a research company, we're a research startup that does like fundamental research into, uh, making like AGI for search in a, in a way. Uh, and then we have all these like, uh, business products that come out of that.

Swyx [00:05:08]: Interesting. I want to ask a little bit more about Metaforesight and then we can go full Exa. When I first met you, which was really funny, cause like literally I stayed in your house in a very historic, uh, Hayes, Hayes Valley place. You said you were building sort of like link prediction foundation model, and I think there's still a lot of foundation model work. I mean, within Exa today, but what does that even mean? I cannot be the only person confused by that because like there's a limited vocabulary or tokens you're telling me, like the tokens are the links or, you know, like it's not, it's not clear. Yeah.

Will [00:05:38]: Uh, what we meant by link prediction is that you are literally predicting, like given some texts, you're predicting the links that follow. Yes. That refers to like, it's how we describe the training procedure, which is that we find links on the web. Uh, we take the text surrounding the link. And then we predict. Which link follows you, like, uh, you know, similar to transformers where, uh, you're trying to predict the next token here, you're trying to predict the next link. And so you kind of like hide the link from the transformer. So if someone writes, you know, imagine some article where someone says, Hey, check out this really cool aerospace startup. And they, they say spacex.com afterwards, uh, we hide the spacex.com and ask the model, like what link came next. And by doing that many, many times, you know, billions of times, you could actually build a search engine out of that because then, uh, at query time at search time. Uh, you type in, uh, a query that's like really cool aerospace startup and the model will then try to predict what are the most likely links. So there's a lot of analogs to transformers, but like to actually make this work, it does require like a different architecture than, but it's transformer inspired. Yeah.

Alessio [00:06:41]: What's the design decision between doing that versus extracting the link and the description and then embedding the description and then using, um, yeah. What do you need to predict the URL versus like just describing, because you're kind of do a similar thing in a way. Right. It's kind of like based on this description, it was like the closest link for it. So one thing is like predicting the link. The other approach is like I extract the link and the description, and then based on the query, I searched the closest description to it more. Yeah.

Will [00:07:09]: That, that, by the way, that is, that is the link refers here to a document. It's not, I think one confusing thing is it's not, you're not actually predicting the URL, the URL itself that would require like the, the system to have memorized URLs. You're actually like getting the actual document, a more accurate name could be document prediction. I see. This was the initial like base model that Exo was trained on, but we've moved beyond that similar to like how, you know, uh, to train a really good like language model, you might start with this like self-supervised objective of predicting the next token and then, uh, just from random stuff on the web. But then you, you want to, uh, add a bunch of like synthetic data and like supervised fine tuning, um, stuff like that to make it really like controllable and robust. Yeah.

Alessio [00:07:48]: Yeah. We just have flow from Lindy and, uh, their Lindy started to like hallucinate recrolling YouTube links instead of like, uh, something. Yeah. Support guide. So. Oh, interesting. Yeah.

Swyx [00:07:57]: So round about January, you announced your series A and renamed to Exo. I didn't like the name at the, at the initial, but it's grown on me. I liked metaphor, but apparently people can spell metaphor. What would you say are the major components of Exo today? Right? Like, I feel like it used to be very model heavy. Then at the AI engineer conference, Shreyas gave a really good talk on the vector database that you guys have. What are the other major moving parts of Exo? Okay.

Will [00:08:23]: So Exo overall is a search engine. Yeah. We're trying to make it like a perfect search engine. And to do that, you have to build lots of, and we're doing it from scratch, right? So to do that, you have to build lots of different. The crawler. Yeah. You have to crawl a bunch of the web. First of all, you have to find the URLs to crawl. Uh, it's connected to the crawler, but yeah, you find URLs, you crawl those URLs. Then you have to process them with some, you know, it could be an embedding model. It could be something more complex, but you need to take, you know, or like, you know, in the past it was like a keyword inverted index. Like you would process all these documents you gather into some processed index, and then you have to serve that. Uh, you had high throughput at low latency. And so that, and that's like the vector database. And so it's like the crawling system, the AI processing system, and then the serving system. Those are all like, you know, teams of like hundreds, maybe thousands of people at Google. Um, but for us, it's like one or two people each typically, but yeah.

Alessio [00:09:13]: Can you explain the meaning of, uh, Exo, just the story 10 to the 16th, uh, 18, 18.

Will [00:09:20]: Yeah, yeah, yeah, sure. So. Exo means 10 to the 18th, which is in stark contrast to. To Google, which is 10 to the hundredth. Uh, we actually have these like awesome shirts that are like 10th to 18th is greater than 10th to the hundredth. Yeah, it's great. And it's great because it's provocative. It's like every engineer in Silicon Valley is like, what? No, it's not true. Um, like, yeah. And, uh, and then you, you ask them, okay, what does it actually mean? And like the creative ones will, will recognize it. But yeah, I mean, 10 to the 18th is better than 10 to the hundredth when it comes to search, because with search, you want like the actual list of, of things that match what you're asking for. You don't want like the whole web. You want to basically with search filter, the, like everything that humanity has ever created to exactly what you want. And so the idea is like smaller is better there. You want like the best 10th to the 18th and not the 10th to the hundredth. I'm like, one way to say this is like, you know how Google often says at the top, uh, like, you know, 30 million results found. And it's like crazy. Cause you're looking for like the first startups in San Francisco that work on hardware or something. And like, they're not 30 million results like that. What you want is like 325 results found. And those are all the results. That's what you really want with search. And that's, that's our vision. It's like, it just gives you. Perfectly what you asked for.

Swyx [00:10:24]: We're recording this ahead of your launch. Uh, we haven't released, we haven't figured out the, the, the name of the launch yet, but what is the product that you're launching? I guess now that we're coinciding this podcast with. Yeah.

Will [00:10:36]: So we've basically developed the next version of Exa, which is the ability to get a near perfect list of results of whatever you want. And what that means is you can make a complex query now to Exa, for example, startups working on hardware in SF, and then just get a huge list of all the things that match. And, you know, our goal is if there are 325 startups that match that we find you all of them. And this is just like, there's just like a new experience that's never existed before. It's really like, I don't know how you would go about that right now with current tools and you can apply this same type of like technology to anything. Like, let's say you want, uh, you want to find all the blog posts that talk about Alessio's podcast, um, that have come out in the past year. That is 30 million results. Yeah. Right.

Will [00:11:24]: But that, I mean, that would, I'm sure that would be extremely useful to you guys. And like, I don't really know how you would get that full comprehensive list.

Swyx [00:11:29]: I just like, how do you, well, there's so many questions with regards to how do you know it's complete, right? Cause you're saying there's only 30 million, 325, whatever. And then how do you do the semantic understanding that it might take, right? So working in hardware, like I might not use the words hardware. I might use the words robotics. I might use the words wearables. I might use like whatever. Yes. So yeah, just tell us more. Yeah. Yeah. Sure. Sure.

Will [00:11:53]: So one aspect of this, it's a little subjective. So like certainly providing, you know, at some point we'll provide parameters to the user to like, you know, some sort of threshold to like, uh, gauge like, okay, like this is a cutoff. Like, this is actually not what I mean, because sometimes it's subjective and there needs to be a feedback loop. Like, oh, like it might give you like a few examples and you say, yeah, exactly. And so like, you're, you're kind of like creating a classifier on the fly, but like, that's ultimately how you solve the problem. So the subject, there's a subjectivity problem and then there's a comprehensiveness problem. Those are two different problems. So. Yeah. So you have the comprehensiveness problem. What you basically have to do is you have to put more compute into the query, into the search until you get the full comprehensiveness. Yeah. And I think there's an interesting point here, which is that not all queries are made equal. Some queries just like this blog post one might require scanning, like scavenging, like throughout the whole web in a way that just, just simply requires more compute. You know, at some point there's some amount of compute where you will just be comprehensive. You could imagine, for example, running GPT-4 over the internet. You could imagine running GPT-4 over the entire web and saying like, is this a blog post about Alessio's podcast, like, is this a blog post about Alessio's podcast? And then that would work, right? It would take, you know, a year, maybe cost like a million dollars, but, or many more, but, um, it would work. Uh, the point is that like, given sufficient compute, you can solve the query. And so it's really a question of like, how comprehensive do you want it given your compute budget? I think it's very similar to O1, by the way. And one way of thinking about what we built is like O1 for search, uh, because O1 is all about like, you know, some, some, some questions require more compute than others, and we'll put as much compute into the question as we need to solve it. So similarly with our search, we will put as much compute into the query in order to get comprehensiveness. Yeah.

Swyx [00:13:33]: Does that mean you have like some kind of compute budget that I can specify? Yes. Yes. Okay. And like, what are the upper and lower bounds?

Will [00:13:42]: Yeah, there's something we're still figuring out. I think like, like everyone is a new paradigm of like variable compute products. Yeah. How do you specify the amount of compute? Like what happens when you. Run out? Do you just like, ah, do you, can you like keep going with it? Like, do you just put in more credits to get more, um, for some, like this can get complex at like the really large compute queries. And like, one thing we do is we give you a preview of what you're going to get, and then you could then spin up like a much larger job, uh, to get like way more results. But yes, there is some compute limit, um, at, at least right now. Yeah. People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned, uh, to have search that takes 500 milliseconds. But like search engines like Google, right. No matter how complex your query to Google, it will take like, you know, roughly 400 milliseconds. But what if searches can take like a minute or 10 minutes or a whole day, what can you then do? And you can do very powerful things. Um, you know, you can imagine, you know, writing a search, going and get a cup of coffee, coming back and you have a perfect list. Like that's okay for a lot of use cases. Yeah.

Alessio [00:14:43]: Yeah. I mean, the use case closest to me is venture capital, right? So, uh, no, I mean, eight years ago, I built one of the first like data driven sourcing platforms. So we were. You look at GitHub, Twitter, Product Hunt, all these things, look at interesting things, evaluate them. If you think about some jobs that people have, it's like literally just make a list. If you're like an analyst at a venture firm, your job is to make a list of interesting companies. And then you reach out to them. How do you think about being infrastructure versus like a product you could say, Hey, this is like a product to find companies. This is a product to find things versus like offering more as a blank canvas that people can build on top of. Oh, right. Right.

Will [00:15:20]: Uh, we are. We are a search infrastructure company. So we want people to build, uh, on top of us, uh, build amazing products on top of us. But with this one, we try to build something that makes it really easy for users to just log in, put a few, you know, put some credits in and just get like amazing results right away and not have to wait to build some API integration. So we're kind of doing both. Uh, we, we want, we want people to integrate this into all their applications at the same time. We want to just make it really easy to use very similar again to open AI. Like they'll have, they have an API, but they also have. Like a ChatGPT interface so that you could, it's really easy to use, but you could also build it in your applications. Yeah.

Alessio [00:15:56]: I'm still trying to wrap my head around a lot of the implications. So, so many businesses run on like information arbitrage, you know, like I know this thing that you don't, especially in investment and financial services. So yeah, now all of a sudden you have these tools for like, oh, actually everybody can get the same information at the same time, the same quality level as an API call. You know, it just kind of changes a lot of things. Yeah.

Will [00:16:19]: I think, I think what we're grappling with here. What, what you're just thinking about is like, what is the world like if knowledge is kind of solved, if like any knowledge request you want is just like right there on your computer, it's kind of different from when intelligence is solved. There's like a good, I've written before about like a different super intelligence, super knowledge. Yeah. Like I think that the, the distinction between intelligence and knowledge is actually a pretty good one. They're definitely connected and related in all sorts of ways, but there is a distinction. You could have a world and we are going to have this world where you have like GP five level systems and beyond that could like answer any complex request. Um, unless it requires some. Like, if you say like, uh, you know, give me a list of all the PhDs in New York city who, I don't know, have thought about search before. And even though this, this super intelligence is going to be like, I can't find it on Google, right. Which is kind of crazy. Like we're literally going to have like super intelligences that are using Google. And so if Google can't find them information, there's nothing they could do. They can't find it. So, but if you also have a super knowledge system where it's like, you know, I'm calling this term super knowledge where you just get whatever knowledge you want, then you can pair with a super intelligence system. And then the super intelligence can, we'll never. Be blocked by lack of knowledge.

Alessio [00:17:23]: Yeah. You told me this, uh, when we had lunch, I forget how it came out, but we were talking about AGI and whatnot. And you were like, even AGI is going to need search. Yeah.

Swyx [00:17:32]: Yeah. Right. Yeah. Um, so we're actually referencing a blog post that you wrote super intelligence and super knowledge. Uh, so I would refer people to that. And this is actually a discussion we've had on the podcast a couple of times. Um, there's so much of model weights that are just memorizing facts. Some of the, some of those might be outdated. Some of them are incomplete or not. Yeah. So like you just need search. So I do wonder, like, is there a maximum language model size that will be the intelligence layer and then the rest is just search, right? Like maybe we should just always use search. And then that sort of workhorse model is just like, and it like, like, like one B or three B parameter model that just drives everything. Yes.

Will [00:18:13]: I believe this is a much more optimal system to have a smaller LM. That's really just like an intelligence module. And it makes a call to a search. Tool that's way more efficient because if, okay, I mean the, the opposite of that would be like the LM is so big that can memorize the whole web. That would be like way, but you know, it's not practical at all. I don't, it's not possible to train that at least right now. And Carpathy has actually written about this, how like he could, he could see models moving more and more towards like intelligence modules using various tools. Yeah.

Swyx [00:18:39]: So for listeners, that's the, that was him on the no priors podcast. And for us, we talked about this and the, on the Shin Yu and Harrison chase podcasts. I'm doing search in my head. I told you 30 million results. I forgot about our neural link integration. Self-hosted exit.

Will [00:18:54]: Yeah. Yeah. No, I do see that that is a much more, much more efficient world. Yeah. I mean, you could also have GB four level systems calling search, but it's just because of the cost of inference. It's just better to have a very efficient search tool and a very efficient LM and they're built for different things. Yeah.

Swyx [00:19:09]: I'm just kind of curious. Like it is still something so audacious that I don't want to elide, which is you're, you're, you're building a search engine. Where do you start? How do you, like, are there any reference papers or implementation? That would really influence your thinking, anything like that? Because I don't even know where to start apart from just crawl a bunch of s**t, but there's gotta be more insight than that.

Will [00:19:28]: I mean, yeah, there's more insight, but I'm always surprised by like, if you have a group of people who are really focused on solving a problem, um, with the tools today, like there's some in, in software, like there are all sorts of creative solutions that just haven't been thought of before, particularly in the information retrieval field. Yeah. I think a lot of the techniques are just very old, frankly. Like I know how Google and Bing work and. They're just not using new methods. There are all sorts of reasons for that. Like one, like Google has to be comprehensive over the web. So they're, and they have to return in 400 milliseconds. And those two things combined means they are kind of limit and it can't cost too much. They're kind of limited in, uh, what kinds of algorithms they could even deploy at scale. So they end up using like a limited keyword based algorithm. Also like Google was built in a time where like in, you know, in 1998, where we didn't have LMS, we didn't have embeddings. And so they never thought to build those things. And so now they have this like gigantic system that is built on old technology. Yeah. And so a lot of the information retrieval field we found just like thinks in terms of that framework. Yeah. Whereas we came in as like newcomers just thinking like, okay, there here's GB three. It's magical. Obviously we're going to build search that is using that technology. And we never even thought about using keywords really ever. Uh, like we were neural all the way we're building an end to end neural search engine. And just that whole framing just makes us ask different questions, like pursue different lines of work. And there's just a lot of low hanging fruit because no one else is thinking about it. We're just on the frontier of neural search. We just are, um, for, for at web scale, um, because there's just not a lot of people thinking that way about it.

Swyx [00:20:57]: Yeah. Maybe let's spell this out since, uh, we're already on this topic, elephants in the room are Perplexity and SearchGPT. That's the, I think that it's all, it's no longer called SearchGPT. I think they call it ChatGPT Search. How would you contrast your approaches to them based on what we know of how they work and yeah, just any, anything in that, in that area? Yeah.

Will [00:21:15]: So these systems, there are a few of them now, uh, they basically rely on like traditional search engines like Google or Bing, and then they combine them with like LLMs at the end to, you know, output some power graphics, uh, answering your question. So they like search GPT perplexity. I think they have their own crawlers. No. So there's this important distinction between like having your own search system and like having your own cache of the web. Like for example, so you could create, you could crawl a bunch of the web. Imagine you crawl a hundred billion URLs, and then you create a key value store of like mapping from URL to the document that is technically called an index, but it's not a search algorithm. So then to actually like, when you make a query to search GPT, for example, what is it actually doing it? Let's say it's, it's, it could, it's using the Bing API, uh, getting a list of results and then it could go, it has this cache of like all the contents of those results and then could like bring in the cache, like the index cache, but it's not actually like, it's not like they've built a search engine from scratch over, you know, hundreds of billions of pages. It's like, does that distinction clear? It's like, yeah, you could have like a mapping from URL to documents, but then rely on traditional search engines to actually get the list of results because it's a very hard problem to take. It's not hard. It's not hard to use DynamoDB and, and, and map URLs to documents. It's a very hard problem to take a hundred billion or more documents and given a query, like instantly get the list of results that match. That's a much harder problem that very few entities on, in, on the planet have done. Like there's Google, there's Bing, uh, you know, there's Yandex, but you know, there are not that many companies that are, that are crazy enough to actually build their search engine from scratch when you could just use traditional search APIs.

Alessio [00:22:43]: So Google had PageRank as like the big thing. Is there a LLM equivalent or like any. Stuff that you're working on that you want to highlight?

Will [00:22:51]: The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share. And so if everyone is sharing some Paul Graham essay about fundraising, then like our model is more likely to predict it. So like inherent in our training objective is this, uh, a sense of like high canonicity and like high quality, but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways. That someone refers that Paul Graham, I say, while also learning how important that Paul Graham essay is. Um, so it's like, it's like PageRank on steroids kind of thing. Yeah.

Alessio [00:23:26]: I think to me, that's the most interesting thing about search today, like with Google and whatnot, it's like, it's mostly like domain authority. So like if you get back playing, like if you search any AI term, you get this like SEO slop websites with like a bunch of things in them. So this is interesting, but then how do you think about more timeless maybe content? So if you think about, yeah. You know, maybe the founder mode essay, right. It gets shared by like a lot of people, but then you might have a lot of other essays that are also good, but they just don't really get a lot of traction. Even though maybe the people that share them are high quality. How do you kind of solve that thing when you don't have the people authority, so to speak of who's sharing, whether or not they're worth kind of like bumping up? Yeah.

Will [00:24:10]: I mean, you do have a lot of control over the training data, so you could like make sure that the training data contains like high quality sources so that, okay. Like if you, if you're. Training data, I mean, it's very similar to like language, language model training. Like if you train on like a bunch of crap, your prediction will be crap. Our model will match the training distribution is trained on. And so we could like, there are lots of ways to tweak the training data to refer to high quality content that we want. Yeah. I would say also this, like this slop that is returned by, by traditional search engines, like Google and Bing, you have the slop is then, uh, transferred into the, these LLMs in like a search GBT or, you know, our other systems like that. Like if slop comes in, slop will go out. And so, yeah, that's another answer to how we're different is like, we're not like traditional search engines. We want to give like the highest quality results and like have full control over whatever you want. If you don't want slop, you get that. And then if you put an LM on top of that, which our customers do, then you just get higher quality results or high quality output.

Alessio [00:25:06]: And I use Excel search very often and it's very good. Especially.

Swyx [00:25:09]: Wave uses it too.

Alessio [00:25:10]: Yeah. Yeah. Yeah. Yeah. Yeah. Like the slop is everywhere, especially when it comes to AI, when it comes to investment. When it comes to all of these things for like, it's valuable to be at the top. And this problem is only going to get worse because. Yeah, no, it's totally. What else is in the toolkit? So you have search API, you have ExaSearch, kind of like the web version. Now you have the list builder. I think you also have web scraping. Maybe just touch on that. Like, I guess maybe people, they want to search and then they want to scrape. Right. So is that kind of the use case that people have? Yeah.

Will [00:25:41]: A lot of our customers, they don't just want, because they're building AI applications on top of Exa, they don't just want a list of URLs. They actually want. Like the full content, like cleans, parsed. Markdown. Markdown, maybe chunked, whatever they want, we'll give it to them. And so that's been like huge for customers. Just like getting the URLs and instantly getting the content for each URL is like, and you can do this for 10 or 100 or 1,000 URLs, wherever you want. That's very powerful.

Swyx [00:26:05]: Yeah. I think this is the first thing I asked you for when I tried using Exa.

Will [00:26:09]: Funny story is like when I built the first version of Exa, it's like, we just happened to store the content. Yes. Like the first 1,024 tokens. Because I just kind of like kept it because I thought of, you know, I don't know why. Really for debugging purposes. And so then when people started asking for content, it was actually pretty easy to serve it. But then, and then we did that, like Exa took off. So the computer's content was so useful. So that was kind of cool.

Swyx [00:26:30]: It is. I would say there are other players like Gina, I think is in this space. Firecrawl is in this space. There's a bunch of scraper companies. And obviously scraper is just one part of your stack, but you might as well offer it since you already do it.

Will [00:26:43]: Yeah, it makes sense. It's just easy to have an all-in-one solution. And like. We are, you know, building the best scraper in the world. So scraping is a hard problem and it's easy to get like, you know, a good scraper. It's very hard to get a great scraper and it's super hard to get a perfect scraper. So like, and, and scraping really matters to people. Do you have a perfect scraper? Not yet. Okay.

Swyx [00:27:05]: The web is increasingly closing to the bots and the scrapers, Twitter, Reddit, Quora, Stack Overflow. I don't know what else. How are you dealing with that? How are you navigating those things? Like, you know. You know, opening your eyes, like just paying them money.

Will [00:27:19]: Yeah, no, I mean, I think it definitely makes it harder for search engines. One response is just that there's so much value in the long tail of sites that are open. Okay. Um, and just like, even just searching over those well gets you most of the value. But I mean, there, there is definitely a lot of content that is increasingly not unavailable. And so you could get through that through data partnerships. The bigger we get as a company, the more, the easier it is to just like, uh, make partnerships. But I, I mean, I do see the world as like the future where the. The data, the, the data producers, the content creators will make partnerships with the entities that find that data.

Alessio [00:27:53]: Any other fun use case that maybe people are not thinking about? Yeah.

Will [00:27:58]: Oh, I mean, uh, there are so many customers. Yeah. What are people doing on AXA? Well, I think dating is a really interesting, uh, application of search that is completely underserved because there's a lot of profiles on the web and a lot of people who want to find love and that I'll use it. They give me. Like, you know, age boundaries, you know, education level location. Yeah. I mean, you want to, what, what do you want to do with data? You want to find like a partner who matches this education level, who like, you know, maybe has written about these types of topics before. Like if you could get a list of all the people like that, like, I think you will unblock a lot of people. I mean, there, I mean, I think this is a very Silicon Valley view of dating for sure. And I'm, I'm well aware of that, but it's just an interesting application of like, you know, I would love to meet like an intellectual partner, um, who like shares a lot of ideas. Yeah. Like if you could do that through better search and yeah.

Swyx [00:28:48]: But what is it with Jeff? Jeff has already set me up with a few people. So like Jeff, I think it's my personal exit.

Will [00:28:55]: my mom's actually a matchmaker and has got a lot of married. Yeah. No kidding. Yeah. Yeah. Search is built into the book. It's in your jeans. Yeah. Yeah.

Swyx [00:29:02]: Yeah. Other than dating, like I know you're having quite some success in colleges. I would just love to map out some more use cases so that our listeners can just use those examples to think about use cases for XR, right? Because it's such a general technology that it's hard to. Uh, really pin down, like, what should I use it for and what kind of products can I build with it?

Will [00:29:20]: Yeah, sure. So, I mean, there are so many applications of XR and we have, you know, many, many companies using us for very diverse range of use cases, but I'll just highlight some interesting ones. Like one customer, a big customer is using us to, um, basically build like a, a writing assistant for students who want to write, uh, research papers. And basically like XR will search for, uh, like a list of research papers related to what the student is writing. And then this product has. Has like an LLM that like summarizes the papers to basically it's like a next word prediction, but in, uh, you know, prompted by like, you know, 20 research papers that X has returned. It's like literally just doing their homework for them. Yeah. Yeah. the key point is like, it's, it's, uh, you know, it's, it's, you know, research is, is a really hard thing to do and you need like high quality content as input.

Swyx [00:30:08]: Oh, so we've had illicit on the podcast. I think it's pretty similar. Uh, they, they do focus pretty much on just, just research papers and, and that research. Basically, I think dating, uh, research, like I just wanted to like spell out more things, like just the big verticals.

Will [00:30:23]: Yeah, yeah, no, I mean, there, there are so many use cases. So finance we talked about, yeah. I mean, one big vertical is just finding a list of companies, uh, so it's useful for VCs, like you said, who want to find like a list of competitors to a specific company they're investigating or just a list of companies in some field. Like, uh, there was one VC that told me that him and his team, like we're using XR for like eight hours straight. Like, like that. For many days on end, just like, like, uh, doing like lots of different queries of different types, like, oh, like all the companies in AI for law or, uh, all the companies for AI for, uh, construction and just like getting lists of things because you just can't find this information with, with traditional search engines. And then, you know, finding companies is also useful for, for selling. If you want to find, you know, like if we want to find a list of, uh, writing assistants to sell to, then we can just, we just use XR ourselves to find that is actually how we found a lot of our customers. Ooh, you can find your own customers using XR. Oh my God. I, in the spirit of. Uh, using XR to bolster XR, like recruiting is really helpful. It is really great use case of XR, um, because we can just get like a list of, you know, people who thought about search and just get like a long list and then, you know, reach out to those people.

Swyx [00:31:29]: When you say thought about, are you, are you thinking LinkedIn, Twitter, or are you thinking just blogs?

Will [00:31:33]: Or they've written, I mean, it's pretty general. So in that case, like ideally XR would return like the, the really blogs written by people who have just. So if I don't blog, I don't show up to XR, right? Like I have to blog. well, I mean, you could show up. That's like an incentive for people to blog.

Swyx [00:31:47]: Well, if you've written about, uh, search in on Twitter and we, we do, we do index a bunch of tweets and then we, we should be able to service that. Yeah. Um, I mean, this is something I tell people, like you have to make yourself discoverable to the web, uh, you know, it's called learning in public, but like, it's even more imperative now because otherwise you don't exist at all.

Will [00:32:07]: Yeah, no, no, this is a huge, uh, thing, which is like search engines completely influence. They have downstream effects. They influence the internet itself. They influence what people. Choose to create. And so Google, because they're a keyword based search engine, people like kind of like keyword stuff. Yeah. They're, they're, they're incentivized to create things that just match a lot of keywords, which is not very high quality. Uh, whereas XR is a search algorithm that, uh, optimizes for like high quality and actually like matching what you mean. And so people are incentivized to create content that is high quality, that like the create content that they know will be found by the right person. So like, you know, if I am a search researcher and I want to be found. By XR, I should blog about search and all the things I'm building because, because now we have a search engine like XR that's powerful enough to find them. And so the search engine will influence like the downstream internet in all sorts of amazing ways. Yeah. Uh, whatever the search engine optimizes for is what the internet looks like. Yeah.

Swyx [00:33:01]: Are you familiar with the term? McLuhanism? No, it's not. Uh, it's this concept that, uh, like first we shape tools and then the tools shape us. Okay. Yeah. Uh, so there's like this reflexive connection between the things we search for and the things that get searched. Yes. So like once you change the tool. The tool that searches the, the, the things that get searched also change. Yes.

Will [00:33:18]: I mean, there was a clear example of that with 30 years of Google. Yeah, exactly. Google has basically trained us to think of search and Google has Google is search like in people's heads. Right. It's one, uh, hard part about XR is like, uh, ripping people away from that notion of search and expanding their sense of what search could be. Because like when people think search, they think like a few keywords, or at least they used to, they think of a few keywords and that's it. They don't think to make these like really complex paragraph long requests for information and get a perfect list. ChatGPT was an interesting like thing that expanded people's understanding of search because you start using ChatGPT for a few hours and you go back to Google and you like paste in your code and Google just doesn't work and you're like, oh, wait, it, Google doesn't do work that way. So like ChatGPT expanded our understanding of what search can be. And I think XR is, uh, is part of that. We want to expand people's notion, like, Hey, you could actually get whatever you want. Yeah.

Alessio [00:34:06]: I search on XR right now, people writing about learning in public. I was like, is it gonna come out with Alessio? Am I, am I there? You're not because. Bro. It's. So, no, it's, it's so about, because it thinks about learning, like in public, like public schools and like focuses more on that. You know, it's like how, when there are like these highly overlapping things, like this is like a good result based on the query, you know, but like, how do I get to Alessio? Right. So if you're like in these subcultures, I don't think this would work in Google well either, you know, but I, I don't know if you have any learnings.

Swyx [00:34:40]: No, I'm the first result on Google.

Alessio [00:34:42]: People writing about learning. In public, you're not first result anymore, I guess.

Swyx [00:34:48]: Just type learning public in Google.

Alessio [00:34:49]: Well, yeah, yeah, yeah, yeah. But this is also like, this is in Google, it doesn't work either. That's what I'm saying. It's like how, when you have like a movement.

Will [00:34:56]: There's confusion about the, like what you mean, like your intention is a little, uh. Yeah.

Alessio [00:35:00]: It's like, yeah, I'm using, I'm using a term that like I didn't invent, but I'm kind of taking over, but like, they're just so much about that term already that it's hard to overcome. If that makes sense, because public schools is like, well, it's, it's hard to overcome.

Will [00:35:14]: Public schools, you know, so there's the right solution to this, which is to specify more clearly what you mean. And I'm not expecting you to do that, but so the, the right interface to search is actually an LLM.

Swyx [00:35:25]: Like you should be talking to an LLM about what you want and the LLM translates its knowledge of you or knowledge of what people usually mean into a query that excellent uses, which you have called auto prompts, right?

Will [00:35:35]: Or, yeah, but it's like a very light version of that. And really it's just basically the right answer is it's the wrong interface and like very soon interface to search and really to everything will be LLM. And the LLM just has a full knowledge of you, right? So we're kind of building for that world. We're skating to where the puck is going to be. And so since we're moving to a world where like LLMs are interfaced to everything, you should build a search engine that can handle complex LLM queries, queries that come from LLMs. Because you're probably too lazy, I'm too lazy too, to write like a whole paragraph explaining, okay, this is what I mean by this word. But an LLM is not lazy. And so like the LLM will spit out like a paragraph or more explaining exactly what it wants. You need a search engine that can handle that. Traditional search engines like Google or Bing, they're actually... Designed for humans typing keywords. If you give a paragraph to Google or Bing, they just completely fail. And so Exa can handle paragraphs and we want to be able to handle it more and more until it's like perfect.

Alessio [00:36:24]: What about opinions? Do you have lists? When you think about the list product, do you think about just finding entries? Do you think about ranking entries? I'll give you a dumb example. So on Lindy, I've been building the spot that every week gives me like the top fantasy football waiver pickups. But every website is like different opinions. I'm like, you should pick up. These five players, these five players. When you're making lists, do you want to be kind of like also ranking and like telling people what's best? Or like, are you mostly focused on just surfacing information?

Will [00:36:56]: There's a really good distinction between filtering to like things that match your query and then ranking based on like what is like your preferences. And ranking is like filtering is objective. It's like, does this document match what you asked for? Whereas ranking is more subjective. It's like, what is the best? Well, it depends what you mean by best, right? So first, first table stakes is let's get the filtering into a perfect place where you actually like every document matches what you asked for. No surgeon can do that today. And then ranking, you know, there are all sorts of interesting ways to do that where like you've maybe for, you know, have the user like specify more clearly what they mean by best. You could do it. And if the user doesn't specify, you do your best, you do your best based on what people typically mean by best. But ideally, like the user can specify, oh, when I mean best, I actually mean ranked by the, you know, the number of people who visited that site. Let's say is, is one example ranking or, oh, what I mean by best, let's say you're listing companies. What I mean by best is like the ones that have, uh, you know, have the most employees or something like that. Like there are all sorts of ways to rank a list of results that are not captured by something as subjective as best. Yeah. Yeah.

Alessio [00:38:00]: I mean, it's like, who are the best NBA players in the history? It's like everybody has their own. Right.

Will [00:38:06]: Right. But I mean, the, the, the search engine should definitely like, even if you don't specify it, it should do as good of a job as possible. Yeah. Yeah. No, no, totally. Yeah. Yeah. Yeah. Yeah. It's a new topic to people because we're not used to a search engine that can handle like a very complex ranking system. Like you think to type in best basketball players and not something more specific because you know, that's the only thing Google could handle. But if Google could handle like, oh, basketball players ranked by like number of shots scored on average per game, then you would do that. But you know, they can't do that. So.

Swyx [00:38:32]: Yeah. That's fascinating. So you haven't used the word agents, but you're kind of building a search agent. Do you believe that that is agentic in feature? Do you think that term is distracting?

Will [00:38:42]: I think it's a good term. I do think everything will eventually become agentic. And so then the term will lose power, but yes, like what we're building is agentic it in a sense that it takes actions. It decides when to go deeper into something, it has a loop, right? It feels different from traditional search, which is like an algorithm, not an agent. Ours is a combination of an algorithm and an agent.

Swyx [00:39:05]: I think my reflection from seeing this in the coding space where there's basically sort of classic. Framework for thinking about this stuff is the self-driving levels of autonomy, right? Level one to five, typically the level five ones all failed because there's full autonomy and we're not, we're not there yet. And people like control. People like to be in the loop. So the, the, the level ones was co-pilot first and now it's like cursor and whatever. So I feel like if it's too agentic, it's too magical, like, like a, like a one shot, I stick a, stick a paragraph into the text box and then it spits it back to me. It might feel like I'm too disconnected from the process and I don't trust it. As opposed to something where I'm more intimately involved with the research product. I see. So like, uh, wait, so the earlier versions are, so if trying to stick to the example of the basketball thing, like best basketball player, but instead of best, you, you actually get to customize it with like, whatever the metric is that you, you guys care about. Yeah. I'm still not a basketballer, but, uh, but, but, you know, like, like B people like to be in my, my thesis is that agents level five agents failed because people like to. To kind of have drive assist rather than full self-driving.

Will [00:40:15]: I mean, a lot of this has to do with how good agents are. Like at some point, if agents for coding are better than humans at all tests and then humans block, yeah, we're not there yet.

Swyx [00:40:25]: So like in a world where we're not there yet, what you're pitching us is like, you're, you're kind of saying you're going all the way there. Like I kind of, I think all one is also very full, full self-driving. You don't get to see the plan. You don't get to affect the plan yet. You just fire off a query and then it goes away for a couple of minutes and comes back. Right. Which is effectively what you're saying you're going to do too. And you think there's.

Will [00:40:42]: There's a, there's an in-between. I saw. Okay. So in building this product, we're exploring new interfaces because what does it mean to kick off a search that goes and takes 10 minutes? Like, is that a good interface? Because what if the search is actually wrong or it's not exactly, exactly specified to what you mean, which is why you get previews. Yeah. You get previews. So it is iterative, but ultimately once you've specified exactly what you mean, then you kind of do just want to kick off a batch job. Right. So perhaps what you're getting at is like, uh, there's this barrier with agents where you have to like explain the full context of what you mean, and a lot of failure modes happen when you have, when you don't. Yeah. There's failure modes from the agent, just not being smart enough. And then there's failure modes from the agent, not understanding exactly what you mean. And there's a lot of context that is shared between humans that is like lost between like humans and, and this like new creature.

Alessio [00:41:32]: Yeah. Yeah. Because people don't know what's going on. I mean, to me, the best example of like system prompts is like, why are you writing? You're a helpful assistant. Like. Of course you should be an awful, but people don't yet know, like, can I assume that, you know, that, you know, it's like, why did the, and now people write, oh, you're a very smart software engineer, but like, you never made, you never make mistakes. Like, were you going to try and make mistakes before? So I think people don't yet have an understanding, like with, with driving people know what good driving is. It's like, don't crash, stay within kind of like a certain speed range. It's like, follow the directions. It's like, I don't really have to explain all of those things. I hope. But with. AI and like models and like search, people are like, okay, what do you actually know? What are like your assumptions about how search, how you're going to do search? And like, can I trust it? You know, can I influence it? So I think that's kind of the, the middle ground, like before you go ahead and like do all the search, it's like, can I see how you're doing it? And then maybe help show your work kind of like, yeah, steer you. Yeah. Yeah.

Will [00:42:32]: No, I mean, yeah. Sure. Saying, even if you've crafted a great system prompt, you want to be part of the process itself. Uh, because the system prompt doesn't, it doesn't capture everything. Right. So yeah. A system prompt is like, you get to choose the person you work with. It's like, oh, like I want, I want a software engineer who thinks this way about code. But then even once you've chosen that person, you can't just give them a high level command and they go do it perfectly. You have to be part of that process. So yeah, I agree.

Swyx [00:42:58]: Just a side note for my system, my favorite system, prompt programming anecdote now is the Apple intelligence system prompt that someone, someone's a prompt injected it and seen it. And like the Apple. Intelligence has the words, like, please don't, don't hallucinate. And it's like, of course we don't want you to hallucinate. Right. Like, so it's exactly that, that what you're talking about, like we should train this behavior into the model, but somehow we still feel the need to inject into the prompt. And I still don't even think that we are very scientific about it. Like it, I think it's almost like cargo culting. Like we have this like magical, like turn around three times, throw salt over your shoulder before you do something. And like, it worked the last time. So let's just do it the same time now. And like, we do, there's no science to this.

Will [00:43:35]: I do think a lot of these problems might be ironed out in future versions. Right. So, and like, they might, they might hide the details from you. So it's like, they actually, all of them have a system prompt. That's like, you are a helpful assistant. You don't actually have to include it, even though it might actually be the way they've implemented in the backend. It should be done in RLE AF.

Swyx [00:43:52]: Okay. Uh, one question I was just kind of curious about this episode is I'm going to try to frame this in terms of this, the general AI search wars, you know, you're, you're one player in that, um, there's perplexity, chat, GPT, search, and Google, but there's also like the B2B side, uh, we had. Drew Houston from Dropbox on, and he's competing with Glean, who've, uh, we've also had DD from, from Glean on, is there an appetite for Exa for my company's documents?

Will [00:44:19]: There is appetite, but I think we have to be disciplined, focused, disciplined. I mean, we're already taking on like perfect web search, which is a lot. Um, but I mean, ultimately we want to build a perfect search engine, which definitely for a lot of queries involves your, your personal information, your company's information. And so, yeah, I mean, the grandest vision of Exa is perfect search really over everything, every domain, you know, we're going to have an Exa satellite, uh, because, because satellites can gather information that, uh, is not available publicly. Uh, gotcha. Yeah.

Alessio [00:44:51]: Can we talk about AGI? We never, we never talk about AGI, but you had, uh, this whole tweet about, oh, one being the biggest kind of like AI step function towards it. Why does it feel so important to you? I know there's kind of like always criticism and saying, Hey, it's not the smartest son is better. It's like, blah, blah, blah. What? You choose C. So you say, this is what Ilias see or Sam see what they will see.

Will [00:45:13]: I've just, I've just, you know, been connecting the dots. I mean, this was the key thing that a bunch of labs were working on, which is like, can you create a reward signal? Can you teach yourself based on a reward signal? Whether you're, if you're trying to learn coding or math, if you could have one model say, uh, be a grading system that says like you have successfully solved this programming assessment and then one model, like be the generative system. That's like, here are a bunch of programming assessments. You could train on that. It's basically whenever you could create a reward signal for some task, you could just generate a bunch of tasks for yourself. See that like, oh, on two of these thousand, you did well. And then you just train on that data. It's basically like, I mean, creating your own data for yourself and like, you know, all the labs working on that opening, I built the most impressive product doing that. And it's just very, it's very easy now to see how that could like scale to just solving, like, like solving programming or solving mathematics, which sounds crazy, but everything about our world right now is crazy.

Alessio [00:46:07]: Um, and so I think if you remove that whole, like, oh, that's impossible, and you just think really clearly about like, what's now possible with like what, what they've done with O1, it's easy to see how that scales. How do you think about older GPT models then? Should people still work on them? You know, if like, obviously they just had the new Haiku, like, is it even worth spending time, like making these models better versus just, you know, Sam talked about O2 at that day. So obviously they're, they're spending a lot of time in it, but then you have maybe. The GPU poor, which are still working on making Lama good. Uh, and then you have the follower labs that do not have an O1 like model out yet. Yeah.

Will [00:46:47]: This kind of gets into like, uh, what will the ecosystem of, of models be like in the future? And is there room is, is everything just gonna be O1 like models? I think, well, I mean, there's definitely a question of like inference speed and if certain things like O1 takes a long time, because that's the thing. Well, I mean, O1 is, is two things. It's like one it's it's use it's bootstrapping itself. It's teaching itself. And so the base model is smarter. But then it also has this like inference time compute where it could like spend like many minutes or many hours thinking. And so even the base model, which is also fast, it doesn't have to take minutes. It could take is, is better, smarter. I believe all models will be trained with this paradigm. Like you'll want to train on the best data, but there will be many different size models from different, very many different like companies, I believe. Yeah. Because like, I don't, yeah, I mean, it's hard, hard to predict, but I don't think opening eye is going to dominate like every possible LLM for every possible. Use case. I think for a lot of things, like you just want the fastest model and that might not involve O1 methods at all.

Swyx [00:47:42]: I would say if you were to take the exit being O1 for search, literally, you really need to prioritize search trajectories, like almost maybe paying a bunch of grad students to go research things. And then you kind of track what they search and what the sequence of searching is, because it seems like that is the gold mine here, like the chain of thought or the thinking trajectory. Yeah.

Will [00:48:05]: When it comes to search, I've always been skeptical. I've always been skeptical of human labeled data. Okay. Yeah, please. We tried something at our company at Exa recently where me and a bunch of engineers on the team like labeled a bunch of queries and it was really hard. Like, you know, you have all these niche queries and you're looking at a bunch of results and you're trying to identify which is matched to query. It's talking about, you know, the intricacies of like some biological experiment or something. I have no idea. Like, I don't know what matches and what, what labelers like me tend to do is just match by keyword. I'm like, oh, I don't know. Oh, like this document matches a bunch of keywords, so it must be good. But then you're actually completely missing the meaning of the document. Whereas an LLM like GB4 is really good at labeling. And so I actually think like you just we get by, which we are right now doing using like LLMs as the labelers specifically for search. I think it's interesting. It's different between like search and like GB5 are different because GB5 might benefit from training on a lot of PhD notes because like GB5 might have to do like very, very complex, like, uh, problem-solving in after when it was given an input, but with search, it's actually a very different problem. You're, you're asking simple questions about billions of things. So like, whereas like GB5 is asking a really hard, it's like solving a really hard question, but it's one, it's like one question, a PhD level question with search. You're asking like simple questions about billions of things. Like, is this a startup? Did this person write a blog post about search? You know, those are actually simple questions. You don't need like PhD level training data. Does that make sense? Yeah.

Alessio [00:49:33]: What else we got here? Uh, nap pods. Oh, yeah.

Swyx [00:49:38]: What's the, yeah. So like just generally, I think, uh, EXA has a very interesting company building vibe. Like you, you have a meme Lord CTO, um, I guess, I don't know. Like, and, and you, you have, you just generally, um, are counter consensus in a bunch of things. What is the culture at EXA?

Will [00:49:59]: Like, yeah, I, me and Jeff are, I mean, we've been best friends. It's like, like we met, like met like first day of college. I've been best friends ever since. And we have a really good vibe. I think that's like intense, but also really fun. And like, like funny, honestly, we have a ton of like, we just laugh a lot, a ton at EXA. And I think that's just like, you see that in every part of our culture. We don't really care about how the world sees anything. Like me and Jeff are just like that. Like, we're just thinking really just like, like, what should we do here? Like, what do we need? And so in the nap pod case, it was like, people get tired a lot when they're coding or doing anything really. And like, why can't we just sleep here or, or like nap? And, uh, okay, if we need a nap, then we should get a nap pod. It's crazy to me that there aren't nap pods in lots of companies because like I get tired all the time. I take a nap like every other day, probably for like 20 minutes. I'm actually never actually napping. I'm just thinking about a problem, but closing my eyes really like, um, first of all, it makes me come up with more creative solutions. And then also actually it gives me some rest. So, which is awesome.

Swyx [00:50:54]: Google was the original company that had the nap pods at work, right? Oh, okay.

Will [00:50:56]: Well, then at one point Google was thinking for first principles and everything too. Um, and that was reflected in their nap pods.

Swyx [00:51:02]: So you, you like, you like didn't just get a nap pod for your office. You like found something from China and you're like, who wants to get in on this? Let's get a container full of them. Yeah.

Will [00:51:11]: Well, we're trying, we try to be frugal. So like we were, we were looking at like different nap pods. And then, uh, at some point we were like, wait, China probably has solved this problem. And so then we ordered it from China and then it was actually so heavy. Like when it came off the truck, it was like 500 pounds. And I like the truck was like having trouble, like putting it on the ground. And so like me and the delivery guy were like trying to hold it. And then we couldn't, we were struggling. So someone came out from on the street and like heart started helping us hurt yourself. I know it was really dangerous, but we did it. And then it was awesome.

Alessio [00:51:37]: And it's funny. I was reading the tech crunch article about it. It was a tech crunch article on the nap pods. Yeah. And then Jeff explained, well, they quote Jeff and this paragraph says, so the nap pods maintain employees ability to stop work and sleep rather than the idea that in quotes, employees are slaves. Close quote, I don't know what I'm. I'm like, I'm sure there's not what event, you know, but I'm curious, like, just like how people there's always like this, I think for a little bit, it went away about like startups and kind of like hustle culture and like all of that.

Swyx [00:52:10]: And I think now with AI, people are like, have all these feelings towards AI that are kind of like, I think it's a pro hustle culture, right? Yeah.

Will [00:52:17]: But I mean, I mean, ideally the hustle is like people are just having fun, which is people, people are just having fun.

Alessio [00:52:23]: Yeah. But I would say from the outside, it's like, people don't like it, you know, I'm saying people not in, in AI and kind of like intact. They're kind of like. Oh, these guys are at it again. These are like the same people that gave us underpaid drivers, like whatever it's like. So it was just funny to see somehow they wanted to make it sound like Jeff was saying employees are slaves, but like, oh, yeah, I don't know. That doesn't make sense.

Will [00:52:45]: But yeah, I mean, okay. I can't imagine a more exciting experience than like building something from scratch. That's like a huge deal with a bunch of your friends. Our team is going to look back in 10 years and think this was like the most beautiful experience that you could have in life. And like. That's how I think about it. And yeah, that's just so it's not, it's not a hustle or not. It's like, is this like, like, does this satisfy your core desire to like build things in the world? And it does. Yeah.

Alessio [00:53:10]: Anything else we didn't cover any parting thoughts? Are you hiring?

Will [00:53:16]: Are you, obviously you're looking for more people to use it, but yeah, yeah, we're definitely hiring. We're, we're growing quite fast and we have a really smart team of engineers and researchers. And we now have a, we just purchased a $5 million H 200 cluster. So we have a lot more compute to play with. Do you run all your own inference? We do a mix of our cluster and like AWS inference that we, we use these are, so we have our current cluster, which is like a one hundreds and now we've updated the new one. We use it for training and research.

Swyx [00:53:43]: What's the training versus inference budget? Like, is it like a, is it 50, 50? Is it?

Will [00:53:48]: Yeah, we, there will be more inference for search for sure.

Swyx [00:53:51]: The other thing I mentioned, so by the way, I'm like sidetracking, but I'm just kind of throwing this in there because I always think about the economics of AI search, like for those, I think, I think if you look up, there's the upper limit is going to be whatever you can monetize off of ads, right? So for Google, let's say it's like a one cent per thousand views, something like that. I don't know the exact number, the exact numbers floating around out there. That means that's your revenue, right? Then your cost has to be lower than that. And so at some point, like for an LLM inference call to be made for every page view, you need to get it lower than. The money that you would take in for, for that. And like, one of the things that I was very surprised, surprised for perplexity and character as well was that they couldn't get it so low that it would be reasonable. I think for you guys, it is a mix of front loading it by indexing. So you only run that compute like once a month, once a, once a quarter, whatever you do re-indexing. And then it's just a little bit more when you, when you do inference, when this search actually gets done, right? Like, so I think when people work out like the economics of such a business, they have to kind of think about where do you put the. The costs. Yes.

Will [00:54:52]: Yes. I mean, uh, definitely you have to, you cannot run LLMs over the whole index, you know, billions of things at query time. So you have to pre-process things usually with LLMs, but then you, you can do a re-rank over like, you know, 10, 30, a hundred, depending on a thousand, depending on how. You know, you could, you could play with different sizes of L of transformers to get the cost to work out. I mean, one really interesting thing is like, we're building a search engine at a time where LLM costs are going down like crazy when some very useful. Tool goes down in cost by 200 X in like the space of, I don't know, a couple of years, there are going to be new opportunities in search, right? So like to, to not integrate this and build off, to not like rethink search from scratch, the search algorithm itself, given the fact that things are going down 200 X is crazy.

Alessio [00:55:37]: Thank you so much for coming on, man. It was fun.

Will [00:55:39]: Thank you. This was so fun. Really fun.

Applications for the NYC AI Engineer Summit, focused on Agents at Work, are open!

When we first started Latent Space, in the lightning round we’d always ask guests: “What’s your favorite AI product?”. The majority would say Midjourney. The simple UI of prompt → very aesthetic image turned it into a $300M+ ARR bootstrapped business as it rode the first wave of AI image generation.

In open source land, StableDiffusion was congregating around AUTOMATIC1111 as the de-facto web UI. Unlike Midjourney, which offered some flags but was mostly prompt-driven, A1111 let users play with a lot more parameters, supported additional modalities like img2img, and allowed users to load in custom models. If you’re interested in some of the SD history, you can look at our episodes with Lexica, Replicate, and Playground.

One of the people involved with that community was comfyanonymous, who was also part of the Stability team in 2023, decided to build an alternative called ComfyUI, now one of the fastest growing open source projects in generative images, and is now the preferred partner for folks like Black Forest Labs’s Flux Tools on Day 1. The idea behind it was simple: “Everyone is trying to make easy to use interfaces. Let me try to make a powerful interface that's not easy to use.”

Unlike its predecessors, ComfyUI does not have an input text box. Everything is based around the idea of a node: there’s a text input node, a CLIP node, a checkpoint loader node, a KSampler node, a VAE node, etc. While daunting for simple image generation, the tool is amazing for more complex workflows since you can break down every step of the process, and then chain many of them together rather than manually switching between tools. You can also re-start execution halfway instead of from the beginning, which can save a lot of time when using larger models.

To give you an idea of some of the new use cases that this type of UI enables:

* Sketch something → Generate an image with SD from sketch → feed it into SD Video to animate

* Generate an image of an object → Turn into a 3D asset → Feed into interactive experiences

* Input audio → Generate audio-reactive videos

Their Examples page also includes some of the more common use cases like AnimateDiff, etc. They recently launched the Comfy Registry, an online library of different nodes that users can pull from rather than having to build everything from scratch. The project has >60,000 Github stars, and as the community grows, some of the projects that people build have gotten quite complex:

The most interesting thing about Comfy is that it’s not a UI, it’s a runtime. You can build full applications on top of image models simply by using Comfy. You can expose Comfy workflows as an endpoint and chain them together just like you chain a single node. We’re seeing the rise of AI Engineering applied to art.

Major Tom’s ComfyUI Resources from the Latent Space Discord

Major shoutouts to Major Tom on the LS Discord who is a image generation expert, who offered these pointers:

* “best thing about comfy is the fact it supports almost immediately every new thing that comes out - unlike A1111 or forge, which still don't support flux cnet for instance. It will be perfect tool when conflicting nodes will be resolved”

* AP Workflows from Alessandro Perili are a nice example of an all-in-one train-evaluate-generate system built atop Comfy

* ComfyUI YouTubers to learn from:

* @sebastiankamph

* @NerdyRodent

* @OlivioSarikas

* @sedetweiler

* @pixaroma

* ComfyUI Nodes to check out:

* https://github.com/kijai/ComfyUI-IC-Light

* https://github.com/MrForExample/ComfyUI-3D-Pack

* https://github.com/PowerHouseMan/ComfyUI-AdvancedLivePortrait

* https://github.com/pydn/ComfyUI-to-Python-Extension

* https://github.com/THtianhao/ComfyUI-Portrait-Maker

* https://github.com/ssitu/ComfyUI_NestedNodeBuilder

* https://github.com/longgui0318/comfyui-magic-clothing

* https://github.com/atmaranto/ComfyUI-SaveAsScript

* https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID

* https://github.com/AIFSH/ComfyUI-FishSpeech

* https://github.com/coolzilj/ComfyUI-Photopea

* https://github.com/lks-ai/anynode

* Sarav: https://www.youtube.com/@mickmumpitz/videos ( applied stuff )

* Sarav: https://www.youtube.com/@latentvision (technical, but infrequent)

* look for comfyui node for https://github.com/magic-quill/MagicQuill

* “Comfy for Video” resources

* Kijai (https://github.com/kijai) pushing out support for Mochi, CogVideoX, AnimateDif, LivePortrait etc

* Comfyui node support like LTX https://github.com/Lightricks/ComfyUI-LTXVideo , and HunyuanVideo

* FloraFauna AI and Krea.ai

* Communities: https://www.reddit.com/r/StableDiffusion/, https://www.reddit.com/r/comfyui/

Full YouTube Episode

As usual, you can find the full video episode on our YouTube (and don’t forget to like and subscribe!)

Timestamps

* 00:00:04 Introduction of hosts and anonymous guest

* 00:00:35 Origins of Comfy UI and early Stable Diffusion landscape

* 00:02:58 Comfy's background and development of high-res fix

* 00:05:37 Area conditioning and compositing in image generation

* 00:07:20 Discussion on different AI image models (SD, Flux, etc.)

* 00:11:10 Closed source model APIs and community discussions on SD versions

* 00:14:41 LoRAs and textual inversion in image generation

* 00:18:43 Evaluation methods in the Comfy community

* 00:20:05 CLIP models and text encoders in image generation

* 00:23:05 Prompt weighting and negative prompting

* 00:26:22 Comfy UI's unique features and design choices

* 00:31:00 Memory management in Comfy UI

* 00:33:50 GPU market share and compatibility issues

* 00:35:40 Node design and parameter settings in Comfy UI

* 00:38:44 Custom nodes and community contributions

* 00:41:40 Video generation models and capabilities

* 00:44:47 Comfy UI's development timeline and rise to popularity

* 00:48:13 Current state of Comfy UI team and future plans

* 00:50:11 Discussion on other Comfy startups and potential text generation support

Transcript

Alessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Small AI.

swyx [00:00:12]: Hey everyone, we are in the Chroma Studio again, but with our first ever anonymous guest, Comfy Anonymous, welcome.

Comfy [00:00:19]: Hello.

swyx [00:00:21]: I feel like that's your full name, you just go by Comfy, right?

Comfy [00:00:24]: Yeah, well, a lot of people just call me Comfy, even when they know my real name. Hey, Comfy.

Alessio [00:00:32]: Swyx is the same. You know, not a lot of people call you Shawn.

swyx [00:00:35]: Yeah, you have a professional name, right, that people know you by, and then you have a legal name. Yeah, it's fine. How do I phrase this? I think people who are in the know, know that Comfy is like the tool for image generation and now other multimodality stuff. I would say that when I first got started with Stable Diffusion, the star of the show was Automatic 111, right? And I actually looked back at my notes from 2022-ish, like Comfy was already getting started back then, but it was kind of like the up and comer, and your main feature was the flowchart. Can you just kind of rewind to that moment, that year and like, you know, how you looked at the landscape there and decided to start Comfy?

Comfy [00:01:10]: Yeah, I discovered Stable Diffusion in 2022, in October 2022. And, well, I kind of started playing around with it. Yes, I, and back then I was using Automatic, which was what everyone was using back then. And so I started with that because I had, it was when I started, I had no idea like how Diffusion works. I didn't know how Diffusion models work, how any of this works, so.

swyx [00:01:36]: Oh, yeah. What was your prior background as an engineer?

Comfy [00:01:39]: Just a software engineer. Yeah. Boring software engineer.

swyx [00:01:44]: But like any, any image stuff, any orchestration, distributed systems, GPUs?

Comfy [00:01:49]: No, I was doing basically nothing interesting. Crud, web development? Yeah, a lot of web development, just, yeah, some basic, maybe some basic like automation stuff. Okay. Just. Yeah, no, like, no big companies or anything.

swyx [00:02:08]: Yeah, but like already some interest in automations, probably a lot of Python.

Comfy [00:02:12]: Yeah, yeah, of course, Python. But I wasn't actually used to like the Node graph interface before I started Comfy UI. It was just, I just thought it was like, oh, like, what's the best way to represent the Diffusion process in the user interface? And then like, oh, well. Well, like, naturally, oh, this is the best way I've found. And this was like with the Node interface. So how I got started was, yeah, so basic October 2022, just like I hadn't written a line of PyTorch before that. So it's completely new. What happened was I kind of got addicted to generating images.

Alessio [00:02:58]: As we all did. Yeah.

Comfy [00:03:00]: And then I started. I started experimenting with like the high-res fixed in auto, which was for those that don't know, the high-res fix is just since the Diffusion models back then could only generate that low-resolution. So what you would do, you would generate low-resolution image, then upscale, then refine it again. And that was kind of the hack to generate high-resolution images. I really liked generating. Like higher resolution images. So I was experimenting with that. And so I modified the code a bit. Okay. What happens if I, if I use different samplers on the second pass, I was edited the code of auto. So what happens if I use a different sampler? What happens if I use a different, like a different settings, different number of steps? And because back then the. The high-res fix was very basic, just, so. Yeah.

swyx [00:04:05]: Now there's a whole library of just, uh, the upsamplers.

Comfy [00:04:08]: I think, I think they added a bunch of, uh, of options to the high-res fix since, uh, since, since then. But before that was just so basic. So I wanted to go further. I wanted to try it. What happens if I use a different model for the second, the second pass? And then, well, then the auto code base was, wasn't good enough for. Like, it would have been, uh, harder to implement that in the auto interface than to create my own interface. So that's when I decided to create my own. And you were doing that mostly on your own when you started, or did you already have kind of like a subgroup of people? No, I was, uh, on my own because, because it was just me experimenting with stuff. So yeah, that was it. Then, so I started writing the code January one. 2023, and then I released the first version on GitHub, January 16th, 2023. That's how things got started.

Alessio [00:05:11]: And what's, what's the name? Comfy UI right away or? Yeah.

Comfy [00:05:14]: Comfy UI. The reason the name, my name is Comfy is people thought my pictures were comfy, so I just, uh, just named it, uh, uh, it's my Comfy UI. So yeah, that's, uh,

swyx [00:05:27]: Is there a particular segment of the community that you targeted as users? Like more intensive workflow artists, you know, compared to the automatic crowd or, you know,

Comfy [00:05:37]: This was my way of like experimenting with, uh, with new things, like the high risk fixed thing I mentioned, which was like in Comfy, the first thing you could easily do was just chain different models together. And then one of the first things, I think the first times it got a bit of popularity was when I started experimenting with the different, like applying. Prompts to different areas of the image. Yeah. I called it area conditioning, posted it on Reddit and it got a bunch of upvotes. So I think that's when, like, when people first learned of Comfy UI.

swyx [00:06:17]: Is that mostly like fixing hands?

Comfy [00:06:19]: Uh, no, no, no. That was just, uh, like, let's say, well, it was very, well, it still is kind of difficult to like, let's say you want a mountain, you have an image and then, okay. I'm like, okay. I want the mountain here and I want the, like a, a Fox here.

swyx [00:06:37]: Yeah. So compositing the image. Yeah.

Comfy [00:06:40]: My way was very easy. It was just like, oh, when you run the diffusion process, you kind of generate, okay. You do pass one pass through the diffusion, every step you do one pass. Okay. This place of the image with this brand, this space, place of the image with the other prop. And then. The entire image with another prop and then just average everything together, every step, and that was, uh, area composition, which I call it. And then, then a month later, there was a paper that came out called multi diffusion, which was the same thing, but yeah, that's, uh,

Alessio [00:07:20]: could you do area composition with different models or because you're averaging out, you kind of need the same model.

Comfy [00:07:26]: Could do it with, but yeah, I hadn't implemented it. For different models, but, uh, you, you can do it with, uh, with different models if you want, as long as the models share the same latent space, like we, we're supposed to ring a bell every time someone says, yeah, like, for example, you couldn't use like Excel and SD 1.5, because those have a different latent space, but like, uh, yeah, like SD 1.5 models, different ones. You could, you could do that.

swyx [00:07:59]: There's some models that try to work in pixel space, right?

Comfy [00:08:03]: Yeah. They're very slow. Of course. That's the problem. That that's the, the reason why stable diffusion actually became like popular, like, cause was because of the latent space.

swyx [00:08:14]: Small and yeah. Because it used to be latent diffusion models and then they trained it up.

Comfy [00:08:19]: Yeah. Cause a pixel pixel diffusion models are just too slow. So. Yeah.

swyx [00:08:25]: Have you ever tried to talk to like, like stability, the latent diffusion guys, like, you know, Robin Rombach, that, that crew. Yeah.

Comfy [00:08:32]: Well, I used to work at stability.

swyx [00:08:34]: Oh, I actually didn't know. Yeah.

Comfy [00:08:35]: I used to work at stability. I got, uh, I got hired, uh, in June, 2023.

swyx [00:08:42]: Ah, that's the part of the story I didn't know about. Okay. Yeah.

Comfy [00:08:46]: So the, the reason I was hired is because they were doing, uh, SDXL at the time and they were basically SDXL. I don't know if you remember it was a base model and then a refiner model. Basically they wanted to experiment, like chaining them together. And then, uh, they saw, oh, right. Oh, this, we can use this to do that. Well, let's hire that guy.

swyx [00:09:10]: But they didn't, they didn't pursue it for like SD3. What do you mean? Like the SDXL approach. Yeah.

Comfy [00:09:16]: The reason for that approach was because basically they had two models and then they wanted to publish both of them. So they, they trained one on. Lower time steps, which was the refiner model. And then they, the first one was trained normally. And then they went during their test, they realized, oh, like if we string these models together are like quality increases. So let's publish that. It worked. Yeah. But like right now, I don't think many people actually use the refiner anymore, even though it is actually a full diffusion model. Like you can use it on its own. And it's going to generate images. I don't think anyone, people have mostly forgotten about it. But, uh.

Alessio [00:10:05]: Can we talk about models a little bit? So stable diffusion, obviously is the most known. I know flux has gotten a lot of traction. Are there any underrated models that people should use more or what's the state of the union?

Comfy [00:10:17]: Well, the, the latest, uh, state of the art, at least, yeah, for images there's, uh, yeah, there's flux. There's also SD3.5. SD3.5 is two models. There's a, there's a small one, 2.5B and there's the bigger one, 8B. So it's, it's smaller than flux. So, and it's more, uh, creative in a way, but flux, yeah, flux is the best. People should give SD3.5 a try cause it's, uh, it's different. I won't say it's better. Well, it's better for some like specific use cases. Right. If you want some to make something more like creative, maybe SD3.5. If you want to make something more consistent and flux is probably better.

swyx [00:11:06]: Do you ever consider supporting the closed source model APIs?

Comfy [00:11:10]: Uh, well, they, we do support them as custom nodes. We actually have some, uh, official custom nodes from, uh, different. Ideogram.

swyx [00:11:20]: Yeah. I guess DALI would have one. Yeah.

Comfy [00:11:23]: That's, uh, it's just not, I'm not the person that handles that. Sure.

swyx [00:11:28]: Sure. Quick question on, on SD. There's a lot of community discussion about the transition from SD1.5 to SD2 and then SD2 to SD3. People still like, you know, very loyal to the previous generations of SDs?

Comfy [00:11:41]: Uh, yeah. SD1.5 then still has a lot of, a lot of users.

swyx [00:11:46]: The last based model.

Comfy [00:11:49]: Yeah. Then SD2 was mostly ignored. It wasn't, uh, it wasn't a big enough improvement over the previous one. Okay.

swyx [00:11:58]: So SD1.5, SD3, flux and whatever else. SDXL. SDXL.

Comfy [00:12:03]: That's the main one. Stable cascade. Stable cascade. That was a good model. But, uh, that's, uh, the problem with that one is, uh, it got, uh, like SD3 was announced one week after. Yeah.

swyx [00:12:16]: It was like a weird release. Uh, what was it like inside of stability actually? I mean, statute of limitations. Yeah. The statute of limitations expired. You know, management has moved. So it's easier to talk about now. Yeah.

Comfy [00:12:27]: And inside stability, actually that model was ready, uh, like three months before, but it got, uh, stuck in, uh, red teaming. So basically the product, if that model had released or was supposed to be released by the authors, then it would probably have gotten very popular since it's a, it's a step up from SDXL. But it got all of its momentum stolen. It got stolen by the SD3 announcement. So people kind of didn't develop anything on top of it, even though it's, uh, yeah. It was a good model, at least, uh, completely mostly ignored for some reason. Like

swyx [00:13:07]: I think the naming as well matters. It seemed like a branch off of the main, main tree of development. Yeah.

Comfy [00:13:15]: Well, it was different researchers that did it. Yeah. Yeah. Very like, uh, good model. Like it's the Worcestershire authors. I don't know if I'm pronouncing it correctly. Yeah. Yeah. Yeah.

swyx [00:13:28]: I actually met them in Vienna. Yeah.

Comfy [00:13:30]: They worked at stability for a bit and they left right after the Cascade release.

swyx [00:13:35]: This is Dustin, right? No. Uh, Dustin's SD3. Yeah.

Comfy [00:13:38]: Dustin is a SD3 SDXL. That's, uh, Pablo and Dome. I think I'm pronouncing his name correctly. Yeah. Yeah. Yeah. Yeah. That's very good.

swyx [00:13:51]: It seems like the community is very, they move very quickly. Yeah. Like when there's a new model out, they just drop whatever the current one is. And they just all move wholesale over. Like they don't really stay to explore the full capabilities. Like if, if the stable cascade was that good, they would have AB tested a bit more. Instead they're like, okay, SD3 is out. Let's go. You know?

Comfy [00:14:11]: Well, I find the opposite actually. The community doesn't like, they only jump on a new model when there's a significant improvement. Like if there's a, only like a incremental improvement, which is what, uh, most of these models are going to have, especially if you, cause, uh, stay the same parameter count. Yeah. Like you're not going to get a massive improvement, uh, into like, unless there's something big that, that changes. So, uh. Yeah.

swyx [00:14:41]: And how are they evaluating these improvements? Like, um, because there's, it's a whole chain of, you know, comfy workflows. Yeah. How does, how does one part of the chain actually affect the whole process?

Comfy [00:14:52]: Are you talking on the model side specific?

swyx [00:14:54]: Model specific, right? But like once you have your whole workflow based on a model, it's very hard to move.

Comfy [00:15:01]: Uh, not, well, not really. Well, it depends on your, uh, depends on their specific kind of the workflow. Yeah.

swyx [00:15:09]: So I do a lot of like text and image. Yeah.

Comfy [00:15:12]: When you do change, like most workflows are kind of going to be complete. Yeah. It's just like, you might have to completely change your prompt completely change. Okay.

swyx [00:15:24]: Well, I mean, then maybe the question is really about evals. Like what does the comfy community do for evals? Just, you know,

Comfy [00:15:31]: Well, that they don't really do that. It's more like, oh, I think this image is nice. So that's, uh,

swyx [00:15:38]: They just subscribe to Fofr AI and just see like, you know, what Fofr is doing. Yeah.

Comfy [00:15:43]: Well, they just, they just generate like it. Like, I don't see anyone really doing it. Like, uh, at least on the comfy side, comfy users, they, it's more like, oh, generate images and see, oh, this one's nice. It's like, yeah, it's not, uh, like the, the more, uh, like, uh, scientific, uh, like, uh, like checking that's more on specifically on like model side. If, uh, yeah, but there is a lot of, uh, vibes also, cause it is a like, uh, artistic, uh, you can create a very good model that doesn't generate nice images. Cause most images on the internet are ugly. So if you, if that's like, if you just, oh, I have the best model at 10th giant, it's super smart. I created on all the, like I've trained on just all the images on the internet. The images are not going to look good. So yeah.

Alessio [00:16:42]: Yeah.

Comfy [00:16:43]: They're going to be very consistent. But yeah. People like, it's not going to be like the, the look that people are going to be expecting from, uh, from a model. So. Yeah.

swyx [00:16:54]: Can we talk about LoRa's? Cause we thought we talked about models then like the next step is probably LoRa's. Before, I actually, I'm kind of curious how LoRa's entered the tool set of the image community because the LoRa paper was 2021. And then like, there was like other methods like textual inversion that was popular at the early SD stage. Yeah.

Comfy [00:17:13]: I can't even explain the difference between that. Yeah. Textual inversions. That's basically what you're doing is you're, you're training a, cause well, yeah. Stable diffusion. You have the diffusion model, you have text encoder. So basically what you're doing is training a vector that you're going to pass to the text encoder. It's basically you're training a new word. Yeah.

swyx [00:17:37]: It's a little bit like representation engineering now. Yeah.

Comfy [00:17:40]: Yeah. Basically. Yeah. You're just, so yeah, if you know how like the text encoder works, basically you have, you take your, your words of your product, you convert those into tokens with the tokenizer and those are converted into vectors. Basically. Yeah. Each token represents a different vector. So each word presents a vector. And those, depending on your words, that's the list of vectors that get passed to the text encoder, which is just. Yeah. Yeah. I'm just a stack of, of attention. Like basically it's a very close to LLM architecture. Yeah. Yeah. So basically what you're doing is just training a new vector. We're saying, well, I have all these images and I want to know which word does that represent? And it's going to get like, you train this vector and then, and then when you use this vector, it hopefully generates. Like something similar to your images. Yeah.

swyx [00:18:43]: I would say it's like surprisingly sample efficient in picking up the concept that you're trying to train it on. Yeah.

Comfy [00:18:48]: Well, people have kind of stopped doing that even though back as like when I was at Stability, we, we actually did train internally some like textual versions on like T5 XXL actually worked pretty well. But for some reason, yeah, people don't use them. And also they might also work like, like, yeah, this is something and probably have to test, but maybe if you train a textual version, like on T5 XXL, it might also work with all the other models that use T5 XXL because same thing with like, like the textual inversions that, that were trained for SD 1.5, they also kind of work on SDXL because SDXL has the, has two text encoders. And one of them is the same as the, as the SD 1.5 CLIP-L. So those, they actually would, they don't work as strongly because they're only applied to one of the text encoders. But, and the same thing for SD3. SD3 has three text encoders. So it works. It's still, you can still use your textual version SD 1.5 on SD3, but it's just a lot weaker because now there's three text encoders. So it gets even more diluted. Yeah.

swyx [00:20:05]: Do people experiment a lot on, just on the CLIP side, there's like Siglip, there's Blip, like do people experiment a lot on those?

Comfy [00:20:12]: You can't really replace. Yeah.

swyx [00:20:14]: Because they're trained together, right? Yeah.

Comfy [00:20:15]: They're trained together. So you can't like, well, what I've seen people experimenting with is a long CLIP. So basically someone fine tuned the CLIP model to accept longer prompts.

swyx [00:20:27]: Oh, it's kind of like long context fine tuning. Yeah.

Comfy [00:20:31]: So, so like it's, it's actually supported in Core Comfy.

swyx [00:20:35]: How long is long?

Comfy [00:20:36]: Regular CLIP is 77 tokens. Yeah. Long CLIP is 256. Okay. So, but the hack that like you've, if you use stable diffusion 1.5, you've probably noticed, oh, it still works if I, if I use long prompts, prompts longer than 77 words. Well, that's because the hack is to just, well, you split, you split it up in chugs of 77, your whole big prompt. Let's say you, you give it like the massive text, like the Bible or something, and it would split it up in chugs of 77 and then just pass each one through the CLIP and then just cut anything together at the end. It's not ideal, but it actually works.

swyx [00:21:26]: Like the positioning of the words really, really matters then, right? Like this is why order matters in prompts. Yeah.

Comfy [00:21:33]: Yeah. Like it, it works, but it's, it's not ideal, but it's what people expect. Like if, if someone gives a huge prompt, they expect at least some of the concepts at the end to be like present in the image. But usually when they give long prompts, they, they don't, they like, they don't expect like detail, I think. So that's why it works very well.

swyx [00:21:58]: And while we're on this topic, prompts waiting, negative comments. Negative prompting all, all sort of similar part of this layer of the stack. Yeah.

Comfy [00:22:05]: The, the hack for that, which works on CLIP, like it, basically it's just for SD 1.5, well, for SD 1.5, the prompt waiting works well because CLIP L is a, is not a very deep model. So you have a very high correlation between, you have the input token, the index of the input token vector. And the output token, they're very, the concepts are very close, closely linked. So that means if you interpolate the vector from what, well, the, the way Comfy UI does it is it has, okay, you have the vector, you have an empty prompt. So you have a, a chunk, like a CLIP output for the empty prompt, and then you have the one for your prompt. And then it interpolates from that, depending on your prompt. Yeah.

Comfy [00:23:07]: So that's how it, how it does prompt waiting. But this stops working the deeper your text encoder is. So on T5X itself, it doesn't work at all. So. Wow.

swyx [00:23:20]: Is that a problem for people? I mean, cause I'm used to just move, moving up numbers. Probably not. Yeah.

Comfy [00:23:25]: Well.

swyx [00:23:26]: So you just use words to describe, right? Cause it's a bigger language model. Yeah.

Comfy [00:23:30]: Yeah. So. Yeah. So honestly it might be good, but I haven't seen many complaints on Flux that it's not working. So, cause I guess people can sort of get around it with, with language. So. Yeah.

swyx [00:23:46]: Yeah. And then coming back to LoRa's, now the, the popular way to, to customize models is LoRa's. And I saw you also support Locon and LoHa, which I've never heard of before.

Comfy [00:23:56]: There's a bunch of, cause what, what the LoRa is essentially is. Instead of like, okay, you have your, your model and then you want to fine tune it. So instead of like, what you could do is you could fine tune the entire thing, but that's a bit heavy. So to speed things up and make things less heavy, what you can do is just fine tune some smaller weights, like basically two, two matrices that when you multiply like two low rank matrices and when you multiply them together, gives a, represents a difference between trained weights and your base weights. So by training those two smaller matrices, that's a lot less heavy. Yeah.

Alessio [00:24:45]: And they're portable. So you're going to share them. Yeah. It's like easier. And also smaller.

Comfy [00:24:49]: Yeah. That's the, how LoRa's work. So basically, so when, when inferencing you, you get an inference with them pretty efficiently, like how ComputeWrite does it. It just, when you use a LoRa, it just applies it straight on the weights so that there's only a small delay at the base, like before the sampling to when it applies the weights and then it just same speed as, as before. So for, for inference, it's, it's not that bad, but, and then you have, so basically all the LoRa types like LoHa, LoCon, everything, that's just different ways of representing that like. Basically, you can call it kind of like compression, even though it's not really compression, it's just different ways of represented, like just, okay, I want to train a different on the difference on the weights. What's the best way to represent that difference? There's the basic LoRa, which is just, oh, let's multiply these two matrices together. And then there's all the other ones, which are all different algorithms. So. Yeah.

Alessio [00:25:57]: So let's talk about LoRa. Let's talk about what comfy UI actually is. I think most people have heard of it. Some people might've seen screenshots. I think fewer people have built very complex workflows. So when you started, automatic was like the super simple way. What were some of the choices that you made? So the node workflow, is there anything else that stands out as like, this was like a unique take on how to do image generation workflows?

Comfy [00:26:22]: Well, I feel like, yeah, back then everyone was trying to make like easy to use interface. Yeah. So I'm like, well, everyone's trying to make an easy to use interface.

swyx [00:26:32]: Let's make a hard to use interface.

Comfy [00:26:37]: Like, so like, I like, I don't need to do that, everyone else doing it. So let me try something like, let me try to make a powerful interface that's not easy to use. So.

swyx [00:26:52]: So like, yeah, there's a sort of node execution engine. Yeah. Yeah. And it actually lists, it has this really good list of features of things you prioritize, right? Like let me see, like sort of re-executing from, from any parts of the workflow that was changed, asynchronous queue system, smart memory management, like all this seems like a lot of engineering that. Yeah.

Comfy [00:27:12]: There's a lot of engineering in the back end to make things, cause I was always focused on making things work locally very well. Cause that's cause I was using it locally. So everything. So there's a lot of, a lot of thought and working by getting everything to run as well as possible. So yeah. ConfUI is actually more of a back end, at least, well, not all the front ends getting a lot more development, but, but before, before it was, I was pretty much only focused on the backend. Yeah.

swyx [00:27:50]: So v0.1 was only August this year. Yeah.

Comfy [00:27:54]: With the new front end. Before there was no versioning. So yeah. Yeah. Yeah.

swyx [00:27:57]: And so what was the big rewrite for the 0.1 and then the 1.0?

Comfy [00:28:02]: Well, that's more on the front end side. That's cause before that it was just like the UI, what, cause when I first wrote it, I just, I said, okay, how can I make, like, I can do web development, but I don't like doing it. Like what's the easiest way I can slap a node interface on this. And then I found this library. Yeah. Like JavaScript library.

swyx [00:28:26]: Live graph?

Comfy [00:28:27]: Live graph.

swyx [00:28:28]: Usually people will go for like react flow for like a flow builder. Yeah.

Comfy [00:28:31]: But that seems like too complicated. So I didn't really want to spend time like developing the front end. So I'm like, well, oh, light graph. This has the whole node interface. So, okay. Let me just plug that into, to my backend.

swyx [00:28:49]: I feel like if Streamlit or Gradio offered something that you would have used Streamlit or Gradio cause it's Python. Yeah.

Comfy [00:28:54]: Yeah. Yeah. Yeah.

Comfy [00:29:00]: Yeah.

Comfy [00:29:14]: Yeah. logic and your backend logic and just sticks them together.

swyx [00:29:20]: It's supposed to be easy for you guys. If you're a Python main, you know, I'm a JS main, right? Okay. If you're a Python main, it's supposed to be easy.

Comfy [00:29:26]: Yeah, it's easy, but it makes your whole software a huge mess.

swyx [00:29:30]: I see, I see. So you're mixing concerns instead of separating concerns?

Comfy [00:29:34]: Well, it's because... Like frontend and backend. Frontend and backend should be well separated with a defined API. Like that's how you're supposed to do it. Smart people disagree. It just sticks everything together. It makes it easy to like a huge mess. And also it's, there's a lot of issues with Gradio. Like it's very good if all you want to do is just get like slap a quick interface on your, like to show off your ML project. Like that's what it's made for. Yeah. Like there's no problem using it. Like, oh, I have my, I have my code. I just wanted a quick interface on it. That's perfect. Like use Gradio. But if you want to make something that's like a real, like real software that will last a long time and will be easy to maintain, then I would avoid it. Yeah.

swyx [00:30:32]: So your criticism is Streamlit and Gradio are the same. I mean, those are the same criticisms.

Comfy [00:30:37]: Yeah, Streamlit I haven't used as much. Yeah, I just looked a bit.

swyx [00:30:43]: Similar philosophy.

Comfy [00:30:44]: Yeah, it's similar. It's just, it just seems to me like, okay, for quick, like AI demos, it's perfect.

swyx [00:30:51]: Yeah. Going back to like the core tech, like asynchronous queues, slow re-execution, smart memory management, you know, anything that you were very proud of or was very hard to figure out?

Comfy [00:31:00]: Yeah. The thing that's the biggest pain in the ass is probably the memory management. Yeah.

swyx [00:31:05]: Were you just paging models in and out or? Yeah.

Comfy [00:31:08]: Before it was just, okay, load the model, completely unload it. Then, okay, that, that works well when you, your model are small, but if your models are big and it takes sort of like, let's say someone has a, like a, a 4090, and the model size is 10 gigabytes, that can take a few seconds to like load and load, load and load, so you want to try to keep things like in memory, in the GPU memory as much as possible. What Comfy UI does right now is it. It tries to like estimate, okay, like, okay, you're going to sample this model, it's going to take probably this amount of memory, let's remove the models, like this amount of memory that's been loaded on the GPU and then just execute it. But so there's a fine line between just because try to remove the least amount of models that are already loaded. Because as fans, like Windows drivers, and one other problem is the NVIDIA driver on Windows by default, because there's a way to, there's an option to disable that feature, but by default it, like, if you start loading, you can overflow your GPU memory and then it's, the driver's going to automatically start paging to RAM. But the problem with that is it's, it makes everything extremely slow. So when you see people complaining, oh, this model, it works, but oh, s**t, it starts slowing down a lot, that's probably what's happening. So it's basically you have to just try to get, use as much memory as possible, but not too much, or else things start slowing down, or people get out of memory, and then just find, try to find that line where, oh, like the driver on Windows starts paging and stuff. Yeah. And the problem with PyTorch is it's, it's high levels, don't have that much fine-grained control over, like, specific memory stuff, so kind of have to leave, like, the memory freeing to, to Python and PyTorch, which is, can be annoying sometimes.

swyx [00:33:32]: So, you know, I think one thing is, as a maintainer of this project, like, you're designing for a very wide surface area of compute, like, you even support CPUs.

Comfy [00:33:42]: Yeah, well, that's... That's just, for PyTorch, PyTorch supports CPUs, so, yeah, it's just, that's not, that's not hard to support.

swyx [00:33:50]: First of all, is there a market share estimate, like, is it, like, 70% NVIDIA, like, 30% AMD, and then, like, miscellaneous on Apple, Silicon, or whatever?

Comfy [00:33:59]: For Comfy? Yeah. Yeah, and, yeah, I don't know the market share.

swyx [00:34:03]: Can you guess?

Comfy [00:34:04]: I think it's mostly NVIDIA. Right. Because, because AMD, the problem, like, AMD works horribly on Windows. Like, on Linux, it works fine. It's, it's lower than the price equivalent NVIDIA GPU, but it works, like, you can use it, you generate images, everything works. On Linux, on Windows, you might have a hard time, so, that's the problem, and most people, I think most people who bought AMD probably use Windows. They probably aren't going to switch to Linux, so... Yeah. So, until AMD actually, like, ports their, like, raw cam to, to Windows properly, and then there's actually PyTorch, I think they're, they're doing that, they're in the process of doing that, but, until they get it, they get a good, like, PyTorch raw cam build that works on Windows, it's, like, they're going to have a hard time. Yeah.

Alessio [00:35:06]: We got to get George on it. Yeah. Well, he's trying to get Lisa Su to do it, but... Let's talk a bit about, like, the node design. So, unlike all the other text-to-image, you have a very, like, deep, so you have, like, a separate node for, like, clip and code, you have a separate node for, like, the case sampler, you have, like, all these nodes. Going back to, like, the making it easy versus making it hard, but, like, how much do people actually play with all the settings, you know? Kind of, like, how do you guide people to, like, hey, this is actually going to be very impactful versus this is maybe, like, less impactful, but we still want to expose it to you?

Comfy [00:35:40]: Well, I try to... I try to expose, like, I try to expose everything or, but, yeah, at least for the, but for things, like, for example, for the samplers, like, there's, like, yeah, four different sampler nodes, which go in easiest to most advanced. So, yeah, if you go, like, the easy node, the regular sampler node, that's, you have just the basic settings. But if you use, like, the sampler advanced... If you use, like, the custom advanced node, that, that one you can actually, you'll see you have, like, different nodes.

Alessio [00:36:19]: I'm looking it up now. Yeah. What are, like, the most impactful parameters that you use? So, it's, like, you know, you can have more, but, like, which ones, like, really make a difference?

Comfy [00:36:30]: Yeah, they all do. They all have their own, like, they all, like, for example, yeah, steps. Usually you want steps, you want them to be as low as possible. But you want, if you're optimizing your workflow, you want to, you lower the steps until, like, the images start deteriorating too much. Because that, yeah, that's the number of steps you're running the diffusion process. So, if you want things to be faster, lower is better. But, yeah, CFG, that's more, you can kind of see that as the contrast of the image. Like, if your image looks too bursty. Then you can lower the CFG. So, yeah, CFG, that's how, yeah, that's how strongly the, like, the negative versus positive prompt. Because when you sample a diffusion model, it's basically a negative prompt. It's just, yeah, positive prediction minus negative prediction.

swyx [00:37:32]: Contrastive loss. Yeah.

Comfy [00:37:34]: It's positive minus negative, and the CFG does the multiplier. Yeah. Yeah. Yeah, so.

Alessio [00:37:41]: What are, like, good resources to understand what the parameters do? I think most people start with automatic, and then they move over, and it's, like, snap, CFG, sampler, name, scheduler, denoise. Read it.

Comfy [00:37:53]: But, honestly, well, it's more, it's something you should, like, try out yourself. I don't know, you don't necessarily need to know how it works to, like, what it does. Because even if you know, like, CFGO, it's, like, positive minus negative prompt. Yeah. So the only thing you know at CFG is if it's 1.0, then that means the negative prompt isn't applied. It also means sampling is two times faster. But, yeah. But other than that, it's more, like, you should really just see what it does to the images yourself, and you'll probably get a more intuitive understanding of what these things do.

Alessio [00:38:34]: Any other nodes or things you want to shout out? Like, I know the animate diff IP adapter. Those are, like, some of the most popular ones. Yeah. What else comes to mind?

Comfy [00:38:44]: Not nodes, but there's, like, what I like is when some people, sometimes they make things that use ComfyUI as their backend. Like, there's a plugin for Krita that uses ComfyUI as its backend. So you can use, like, all the models that work in Comfy in Krita. And I think I've tried it once. But I know a lot of people use it, and it's probably really nice, so.

Alessio [00:39:15]: What's the craziest node that people have built, like, the most complicated?

Comfy [00:39:21]: Craziest node? Like, yeah. I know some people have made, like, video games in Comfy with, like, stuff like that. So, like, someone, like, I remember, like, yeah, last, I think it was last year, someone made, like, a, like, Wolfenstein 3D in Comfy. Of course. And then one of the inputs was, oh, you can generate a texture, and then it changes the texture in the game. So you can plug it to, like, the workflow. And there's a lot of, if you look there, there's a lot of crazy things people do, so. Yeah.

Alessio [00:39:59]: And now there's, like, a node register that people can use to, like, download nodes. Yeah.

Comfy [00:40:04]: Like, well, there's always been the, like, the ComfyUI manager. Yeah. But we're trying to make this more, like, I don't know, official, like, with, yeah, with the node registry. Because before the node registry, the, like, okay, how did your custom node get into ComfyUI manager? That's the guy running it who, like, every day he searched GitHub for new custom nodes and added dev annually to his custom node manager. So we're trying to make it less effortless. So we're trying to make it less effortless for him, basically. Yeah.

Alessio [00:40:40]: Yeah. But I was looking, I mean, there's, like, a YouTube download node. There's, like, this is almost like, you know, a data pipeline more than, like, an image generation thing at this point. It's, like, you can get data in, you can, like, apply filters to it, you can generate data out.

Comfy [00:40:54]: Yeah. You can do a lot of different things. Yeah. So I'm thinking, I think what I did is I made it easy to make custom nodes. So I think that helped a lot. I think that helped a lot for, like, the ecosystem because it is very easy to just make a node. So, yeah, a bit too easy sometimes. Then we have the issue where there's a lot of custom node packs which share similar nodes. But, well, that's, yeah, something we're trying to solve by maybe bringing some of the functionality into the core. Yeah. Yeah. Yeah.

Alessio [00:41:36]: And then there's, like, video. People can do video generation. Yeah.

Comfy [00:41:40]: Video, that's, well, the first video model was, like, stable video diffusion, which was last, yeah, exactly last year, I think. Like, one year ago. But that wasn't a true video model. So it was...

swyx [00:41:55]: It was, like, moving images? Yeah.

Comfy [00:41:57]: I generated video. What I mean by that is it's, like, it's still 2D Latents. It's basically what I'm trying to do. So what they did is they took SD2, and then they added some temporal attention to it, and then trained it on videos and all. So it's kind of, like, animated, like, same idea, basically. Why I say it's not a true video model is that you still have, like, the 2D Latents. Like, a true video model, like Mochi, for example, would have 3D Latents. Mm-hmm.

Alessio [00:42:32]: Which means you can, like, move through the space, basically. It's the difference. You're not just kind of, like, reorienting. Yeah.

Comfy [00:42:39]: And it's also, well, it's also because you have a temporal VAE. Mm-hmm. Also, like, Mochi has a temporal VAE that compresses on, like, the temporal direction, also. So that's something you don't have with, like, yeah, animated diff and stable video diffusion. They only, like, compress spatially, not temporally. Mm-hmm. Right. So, yeah. That's why I call that, like, true video models. There's, yeah, there's actually a few of them, but the one I've implemented in comfy is Mochi, because that seems to be the best one so far. Yeah.

swyx [00:43:15]: We had AJ come and speak at the stable diffusion meetup. The other open one I think I've seen is COG video. Yeah.

Comfy [00:43:21]: COG video. Yeah. That one's, yeah, it also seems decent, but, yeah. Chinese, so we don't use it. No, it's fine. It's just, yeah, I could. Yeah. It's just that there's a, it's not the only one. There's also a few others, which I.

swyx [00:43:36]: The rest are, like, closed source, right? Like, Cling. Yeah.

Comfy [00:43:39]: Closed source, there's a bunch of them. But I mean, open. I've seen a few of them. Like, I can't remember their names, but there's COG videos, the big, the big one. Then there's also a few of them that released at the same time. There's one that released at the same time as SSD 3.5, same day, which is why I don't remember the name.

swyx [00:44:02]: We should have a release schedule so we don't conflict on each of these things. Yeah.

Comfy [00:44:06]: I think SD 3.5 and Mochi released on the same day. So everything else was kind of drowned, completely drowned out. So for some reason, lots of people picked that day to release their stuff.

Comfy [00:44:21]: Yeah. Which is, well, shame for those. And I think Omnijet also released the same day, which also seems interesting. Yeah. Yeah.

Alessio [00:44:30]: What's Comfy? So you are Comfy. And then there's like, comfy.org. I know we do a lot of things for, like, news research and those guys also have kind of like a more open source thing going on. How do you work? Like you mentioned, you mostly work on like, the core piece of it. And then what...

Comfy [00:44:47]: Maybe I should fade it in because I, yeah, I feel like maybe, yeah, I only explain part of the story. Right. Yeah. Maybe I should explain the rest. So yeah. So yeah. Basically, January, that's when the first January 2023, January 16, 2023, that's when Amphi was first released to the public. Then, yeah, did a Reddit post about the area composition thing somewhere in, I don't remember exactly, maybe end of January, beginning of February. And then someone, a YouTuber, made a video about it, like Olivio, he made a video about Amphi in March 2023. I think that's when it was a real burst of attention. And by that time, I was continuing to develop it and it was getting, people were starting to use it more, which unfortunately meant that I had first written it to do like experiments, but then my time to do experiments went down. It started going down, because people were actually starting to use it then. Like, I had to, and I said, well, yeah, time to add all these features and stuff. Yeah, and then I got hired by Stability June, 2023. Then I made, basically, yeah, they hired me because they wanted the SD-XL. So I got the SD-XL working very well withітhe UI, because they were experimenting withámphi.house.com. Actually, the SDX, how the SDXL released worked is they released, for some reason, like they released the code first, but they didn't release the model checkpoint. So they released the code. And then, well, since the research was related to code, I released the code in Compute 2. And then the checkpoints were basically early access. People had to sign up and they only allowed a lot of people from edu emails. Like if you had an edu email, like they gave you access basically to the SDXL 0.9. And, well, that leaked. Right. Of course, because of course it's going to leak if you do that. Well, the only way people could easily use it was with Comfy. So, yeah, people started using. And then I fixed a few of the issues people had. So then the big 1.0 release happened. And, well, Comfy UI was the only way a lot of people could actually run it on their computers. Because it just like automatic was so like inefficient and bad that most people couldn't actually, like it just wouldn't work. Like because he did a quick implementation. So people were forced. To use Comfy UI, and that's how it became popular because people had no choice.

swyx [00:47:55]: The growth hack.

Comfy [00:47:56]: Yeah.

swyx [00:47:56]: Yeah.

Comfy [00:47:57]: Like everywhere, like people who didn't have the 4090, they had like, who had just regular GPUs, they didn't have a choice.

Alessio [00:48:05]: So yeah, I got a 4070. So think of me. And so today, what's, is there like a core Comfy team or?

Comfy [00:48:13]: Uh, yeah, well, right now, um, yeah, we are hiring. Okay. Actually, so right now core, like, um, the core core itself, it's, it's me. Uh, but because, uh, the reason where folks like all the focus has been mostly on the front end right now, because that's the thing that's been neglected for a long time. So, uh, so most of the focus right now is, uh, all on the front end, but we are, uh, yeah, we will soon get, uh, more people to like help me with the actual backend stuff. Yeah. So, no, I'm not going to say a hundred percent because that's why once the, once we have our V one release, which is because it'd be the package, come fee-wise with the nice interface and easy to install on windows and hopefully Mac. Uh, yeah. Yeah. Once we have that, uh, we're going to have to, lots of stuff to do on the backend side and also the front end side, but, uh.

Alessio [00:49:14]: What's the release that I'm on the wait list. What's the timing?

Comfy [00:49:18]: Uh, soon. Uh, soon. Yeah, I don't want to promise a release date. We do have a release date we're targeting, but I'm not sure if it's public. Yeah, and we're still going to continue doing the open source, making MPUI the best way to run stable infusion models. At least the open source side, it's going to be the best way to run models locally. But we will have a few things to make money from it, like cloud inference or that type of thing. And maybe some things for some enterprises.

swyx [00:50:08]: I mean, a few questions on that. How do you feel about the other comfy startups?

Comfy [00:50:11]: I mean, I think it's great. They're using your name. Yeah, well, it's better they use comfy than they use something else. Yeah, that's true. It's fine. We're going to try not to... We don't want to... We want people to use comfy. Like I said, it's better that people use comfy than something else. So as long as they use comfy, I think it helps the ecosystem. Because more people, even if they don't contribute directly, the fact that they are using comfy means that people are more likely to join the ecosystem. So, yeah.

swyx [00:50:57]: And then would you ever do text?

Comfy [00:50:59]: Yeah, well, you can already do text with some custom nodes. So, yeah, it's something we like. Yeah, it's something I've wanted to eventually add to core, but it's more like not a very... It's a very high priority. But because a lot of people use text for prompt enhancement and other things like that. So, yeah, it's just that my focus has always been on diffusion models. Yeah, unless some text diffusion model comes out.

swyx [00:51:30]: Yeah, David Holtz is investing a lot in text diffusion.

Comfy [00:51:34]: Yeah, well, if a good one comes out, then we'll probably implement it since it fits with the whole...

swyx [00:51:39]: Yeah, I mean, I imagine it's going to be a close source to Midjourney. Yeah.

Comfy [00:51:43]: Well, if an open one comes out, then I'll probably implement it.

Alessio [00:51:54]: Cool, comfy. Thanks so much for coming on. This was fun. Bye.

Applications for the 2025 AI Engineer Summit are up, and you can save the date for AIE Singapore in April and AIE World’s Fair 2025 in June.

Happy new year, and thanks for 100 great episodes! Please let us know what you want to see/hear for the next 100!

Full YouTube Episode with Slides/Charts

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Timestamps

* 00:00 Welcome to the 100th Episode!

* 00:19 Reflecting on the Journey

* 00:47 AI Engineering: The Rise and Impact

* 03:15 Latent Space Live and AI Conferences

* 09:44 The Competitive AI Landscape

* 21:45 Synthetic Data and Future Trends

* 35:53 Creative Writing with AI

* 36:12 Legal and Ethical Issues in AI

* 38:18 The Data War: GPU Poor vs. GPU Rich

* 39:12 The Rise of GPU Ultra Rich

* 40:47 Emerging Trends in AI Models

* 45:31 The Multi-Modality War

* 01:05:31 The Future of AI Benchmarks

* 01:13:17 Pionote and Frontier Models

* 01:13:47 Niche Models and Base Models

* 01:14:30 State Space Models and RWKB

* 01:15:48 Inference Race and Price Wars

* 01:22:16 Major AI Themes of the Year

* 01:22:48 AI Rewind: January to March

* 01:26:42 AI Rewind: April to June

* 01:33:12 AI Rewind: July to September

* 01:34:59 AI Rewind: October to December

* 01:39:53 Year-End Reflections and Predictions

Transcript

[00:00:00] Welcome to the 100th Episode!

[00:00:00] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co host Swyx for the 100th time today.

[00:00:12] swyx: Yay, um, and we're so glad that, yeah, you know, everyone has, uh, followed us in this journey. How do you feel about it? 100 episodes.

[00:00:19] Alessio: Yeah, I know.

[00:00:19] Reflecting on the Journey

[00:00:19] Alessio: Almost two years that we've been doing this. We've had four different studios. Uh, we've had a lot of changes. You know, we used to do this lightning round. When we first started that we didn't like, and we tried to change the question. The answer

[00:00:32] swyx: was cursor and perplexity.

[00:00:34] Alessio: Yeah, I love mid journey. It's like, do you really not like anything else?

[00:00:38] Alessio: Like what's, what's the unique thing? And I think, yeah, we, we've also had a lot more research driven content. You know, we had like 3DAO, we had, you know. Jeremy Howard, we had more folks like that.

[00:00:47] AI Engineering: The Rise and Impact

[00:00:47] Alessio: I think we want to do more of that too in the new year, like having, uh, some of the Gemini folks, both on the research and the applied side.

[00:00:54] Alessio: Yeah, but it's been a ton of fun. I think we both started, I wouldn't say as a joke, we were kind of like, Oh, we [00:01:00] should do a podcast. And I think we kind of caught the right wave, obviously. And I think your rise of the AI engineer posts just kind of get people. Sombra to congregate, and then the AI engineer summit.

[00:01:11] Alessio: And that's why when I look at our growth chart, it's kind of like a proxy for like the AI engineering industry as a whole, which is almost like, like, even if we don't do that much, we keep growing just because there's so many more AI engineers. So did you expect that growth or did you expect that would take longer for like the AI engineer thing to kind of like become, you know, everybody talks about it today.

[00:01:32] swyx: So, the sign of that, that we have won is that Gartner puts it at the top of the hype curve right now. So Gartner has called the peak in AI engineering. I did not expect, um, to what level. I knew that I was correct when I called it because I did like two months of work going into that. But I didn't know, You know, how quickly it could happen, and obviously there's a chance that I could be wrong.

[00:01:52] swyx: But I think, like, most people have come around to that concept. Hacker News hates it, which is a good sign. But there's enough people that have defined it, you know, GitHub, when [00:02:00] they launched GitHub Models, which is the Hugging Face clone, they put AI engineers in the banner, like, above the fold, like, in big So I think it's like kind of arrived as a meaningful and useful definition.

[00:02:12] swyx: I think people are trying to figure out where the boundaries are. I think that was a lot of the quote unquote drama that happens behind the scenes at the World's Fair in June. Because I think there's a lot of doubt or questions about where ML engineering stops and AI engineering starts. That's a useful debate to be had.

[00:02:29] swyx: In some sense, I actually anticipated that as well. So I intentionally did not. Put a firm definition there because most of the successful definitions are necessarily underspecified and it's actually useful to have different perspectives and you don't have to specify everything from the outset.

[00:02:45] Alessio: Yeah, I was at um, AWS reInvent and the line to get into like the AI engineering talk, so to speak, which is, you know, applied AI and whatnot was like, there are like hundreds of people just in line to go in.

[00:02:56] Alessio: I think that's kind of what enabled me. People, right? Which is what [00:03:00] you kind of talked about. It's like, Hey, look, you don't actually need a PhD, just, yeah, just use the model. And then maybe we'll talk about some of the blind spots that you get as an engineer with the earlier posts that we also had on on the sub stack.

[00:03:11] Alessio: But yeah, it's been a heck of a heck of a two years.

[00:03:14] swyx: Yeah.

[00:03:15] Latent Space Live and AI Conferences

[00:03:15] swyx: You know, I was, I was trying to view the conference as like, so NeurIPS is I think like 16, 17, 000 people. And the Latent Space Live event that we held there was 950 signups. I think. The AI world, the ML world is still very much research heavy. And that's as it should be because ML is very much in a research phase.

[00:03:34] swyx: But as we move this entire field into production, I think that ratio inverts into becoming more engineering heavy. So at least I think engineering should be on the same level, even if it's never as prestigious, like it'll always be low status because at the end of the day, you're manipulating APIs or whatever.

[00:03:51] swyx: But Yeah, wrapping GPTs, but there's going to be an increasing stack and an art to doing these, these things well. And I, you know, I [00:04:00] think that's what we're focusing on for the podcast, the conference and basically everything I do seems to make sense. And I think we'll, we'll talk about the trends here that apply.

[00:04:09] swyx: It's, it's just very strange. So, like, there's a mix of, like, keeping on top of research while not being a researcher and then putting that research into production. So, like, people always ask me, like, why are you covering Neuralibs? Like, this is a ML research conference and I'm like, well, yeah, I mean, we're not going to, to like, understand everything Or reproduce every single paper, but the stuff that is being found here is going to make it through into production at some point, you hope.

[00:04:32] swyx: And then actually like when I talk to the researchers, they actually get very excited because they're like, oh, you guys are actually caring about how this goes into production and that's what they really really want. The measure of success is previously just peer review, right? Getting 7s and 8s on their um, Academic review conferences and stuff like citations is one metric, but money is a better metric.

[00:04:51] Alessio: Money is a better metric. Yeah, and there were about 2200 people on the live stream or something like that. Yeah, yeah. Hundred on the live stream. So [00:05:00] I try my best to moderate, but it was a lot spicier in person with Jonathan and, and Dylan. Yeah, that it was in the chat on YouTube.

[00:05:06] swyx: I would say that I actually also created.

[00:05:09] swyx: Layen Space Live in order to address flaws that are perceived in academic conferences. This is not NeurIPS specific, it's ICML, NeurIPS. Basically, it's very sort of oriented towards the PhD student, uh, market, job market, right? Like literally all, basically everyone's there to advertise their research and skills and get jobs.

[00:05:28] swyx: And then obviously all the, the companies go there to hire them. And I think that's great for the individual researchers, but for people going there to get info is not great because you have to read between the lines, bring a ton of context in order to understand every single paper. So what is missing is effectively what I ended up doing, which is domain by domain, go through and recap the best of the year.

[00:05:48] swyx: Survey the field. And there are, like NeurIPS had a, uh, I think ICML had a like a position paper track, NeurIPS added a benchmarks, uh, datasets track. These are ways in which to address that [00:06:00] issue. Uh, there's always workshops as well. Every, every conference has, you know, a last day of workshops and stuff that provide more of an overview.

[00:06:06] swyx: But they're not specifically prompted to do so. And I think really, uh, Organizing a conference is just about getting good speakers and giving them the correct prompts. And then they will just go and do that thing and they do a very good job of it. So I think Sarah did a fantastic job with the startups prompt.

[00:06:21] swyx: I can't list everybody, but we did best of 2024 in startups, vision, open models. Post transformers, synthetic data, small models, and agents. And then the last one was the, uh, and then we also did a quick one on reasoning with Nathan Lambert. And then the last one, obviously, was the debate that people were very hyped about.

[00:06:39] swyx: It was very awkward. And I'm really, really thankful for John Franco, basically, who stepped up to challenge Dylan. Because Dylan was like, yeah, I'll do it. But He was pro scaling. And I think everyone who is like in AI is pro scaling, right? So you need somebody who's ready to publicly say, no, we've hit a wall.

[00:06:57] swyx: So that means you're saying Sam Altman's wrong. [00:07:00] You're saying, um, you know, everyone else is wrong. It helps that this was the day before Ilya went on, went up on stage and then said pre training has hit a wall. And data has hit a wall. So actually Jonathan ended up winning, and then Ilya supported that statement, and then Noam Brown on the last day further supported that statement as well.

[00:07:17] swyx: So it's kind of interesting that I think the consensus kind of going in was that we're not done scaling, like you should believe in a better lesson. And then, four straight days in a row, you had Sepp Hochreiter, who is the creator of the LSTM, along with everyone's favorite OG in AI, which is Juergen Schmidhuber.

[00:07:34] swyx: He said that, um, we're pre trading inside a wall, or like, we've run into a different kind of wall. And then we have, you know John Frankel, Ilya, and then Noam Brown are all saying variations of the same thing, that we have hit some kind of wall in the status quo of what pre trained, scaling large pre trained models has looked like, and we need a new thing.

[00:07:54] swyx: And obviously the new thing for people is some make, either people are calling it inference time compute or test time [00:08:00] compute. I think the collective terminology has been inference time, and I think that makes sense because test time, calling it test, meaning, has a very pre trained bias, meaning that the only reason for running inference at all is to test your model.

[00:08:11] swyx: That is not true. Right. Yeah. So, so, I quite agree that. OpenAI seems to have adopted, or the community seems to have adopted this terminology of ITC instead of TTC. And that, that makes a lot of sense because like now we care about inference, even right down to compute optimality. Like I actually interviewed this author who recovered or reviewed the Chinchilla paper.

[00:08:31] swyx: Chinchilla paper is compute optimal training, but what is not stated in there is it's pre trained compute optimal training. And once you start caring about inference, compute optimal training, you have a different scaling law. And in a way that we did not know last year.

[00:08:45] Alessio: I wonder, because John is, he's also on the side of attention is all you need.

[00:08:49] Alessio: Like he had the bet with Sasha. So I'm curious, like he doesn't believe in scaling, but he thinks the transformer, I wonder if he's still. So, so,

[00:08:56] swyx: so he, obviously everything is nuanced and you know, I told him to play a character [00:09:00] for this debate, right? So he actually does. Yeah. He still, he still believes that we can scale more.

[00:09:04] swyx: Uh, he just assumed the character to be very game for, for playing this debate. So even more kudos to him that he assumed a position that he didn't believe in and still won the debate.

[00:09:16] Alessio: Get rekt, Dylan. Um, do you just want to quickly run through some of these things? Like, uh, Sarah's presentation, just the highlights.

[00:09:24] swyx: Yeah, we can't go through everyone's slides, but I pulled out some things as a factor of, like, stuff that we were going to talk about. And we'll

[00:09:30] Alessio: publish

[00:09:31] swyx: the rest. Yeah, we'll publish on this feed the best of 2024 in those domains. And hopefully people can benefit from the work that our speakers have done.

[00:09:39] swyx: But I think it's, uh, these are just good slides. And I've been, I've been looking for a sort of end of year recaps from, from people.

[00:09:44] The Competitive AI Landscape

[00:09:44] swyx: The field has progressed a lot. You know, I think the max ELO in 2023 on LMSys used to be 1200 for LMSys ELOs. And now everyone is at least at, uh, 1275 in their ELOs, and this is across Gemini, Chadjibuti, [00:10:00] Grok, O1.

[00:10:01] swyx: ai, which with their E Large model, and Enthopic, of course. It's a very, very competitive race. There are multiple Frontier labs all racing, but there is a clear tier zero Frontier. And then there's like a tier one. It's like, I wish I had everything else. Tier zero is extremely competitive. It's effectively now three horse race between Gemini, uh, Anthropic and OpenAI.

[00:10:21] swyx: I would say that people are still holding out a candle for XAI. XAI, I think, for some reason, because their API was very slow to roll out, is not included in these metrics. So it's actually quite hard to put on there. As someone who also does charts, XAI is continually snubbed because they don't work well with the benchmarking people.

[00:10:42] swyx: Yeah, yeah, yeah. It's a little trivia for why XAI always gets ignored. The other thing is market share. So these are slides from Sarah. We have it up on the screen. It has gone from very heavily open AI. So we have some numbers and estimates. These are from RAMP. Estimates of open AI market share in [00:11:00] December 2023.

[00:11:01] swyx: And this is basically, what is it, GPT being 95 percent of production traffic. And I think if you correlate that with stuff that we asked. Harrison Chase on the LangChain episode, it was true. And then CLAUD 3 launched mid middle of this year. I think CLAUD 3 launched in March, CLAUD 3. 5 Sonnet was in June ish.

[00:11:23] swyx: And you can start seeing the market share shift towards opening, uh, towards that topic, uh, very, very aggressively. The more recent one is Gemini. So if I scroll down a little bit, this is an even more recent dataset. So RAM's dataset ends in September 2 2. 2024. Gemini has basically launched a price war at the low end, uh, with Gemini Flash, uh, being basically free for personal use.

[00:11:44] swyx: Like, I think people don't understand the free tier. It's something like a billion tokens per day. Unless you're trying to abuse it, you cannot really exhaust your free tier on Gemini. They're really trying to get you to use it. They know they're in like third place, um, fourth place, depending how you, how you count.

[00:11:58] swyx: And so they're going after [00:12:00] the Lower tier first, and then, you know, maybe the upper tier later, but yeah, Gemini Flash, according to OpenRouter, is now 50 percent of their OpenRouter requests. Obviously, these are the small requests. These are small, cheap requests that are mathematically going to be more.

[00:12:15] swyx: The smart ones obviously are still going to OpenAI. But, you know, it's a very, very big shift in the market. Like basically 2023, 2022, To going into 2024 opening has gone from nine five market share to Yeah. Reasonably somewhere between 50 to 75 market share.

[00:12:29] Alessio: Yeah. I'm really curious how ramped does the attribution to the model?

[00:12:32] Alessio: If it's API, because I think it's all credit card spin. . Well, but it's all, the credit card doesn't say maybe. Maybe the, maybe when they do expenses, they upload the PDF, but yeah, the, the German I think makes sense. I think that was one of my main 2024 takeaways that like. The best small model companies are the large labs, which is not something I would have thought that the open source kind of like long tail would be like the small model.

[00:12:53] swyx: Yeah, different sizes of small models we're talking about here, right? Like so small model here for Gemini is AB, [00:13:00] right? Uh, mini. We don't know what the small model size is, but yeah, it's probably in the double digits or maybe single digits, but probably double digits. The open source community has kind of focused on the one to three B size.

[00:13:11] swyx: Mm-hmm . Yeah. Maybe

[00:13:12] swyx: zero, maybe 0.5 B uh, that's moon dream and that is small for you then, then that's great. It makes sense that we, we have a range for small now, which is like, may, maybe one to five B. Yeah. I'll even put that at, at, at the high end. And so this includes Gemma from Gemini as well. But also includes the Apple Foundation models, which I think Apple Foundation is 3B.

[00:13:32] Alessio: Yeah. No, that's great. I mean, I think in the start small just meant cheap. I think today small is actually a more nuanced discussion, you know, that people weren't really having before.

[00:13:43] swyx: Yeah, we can keep going. This is a slide that I smiley disagree with Sarah. She's pointing to the scale SEAL leaderboard. I think the Researchers that I talked with at NeurIPS were kind of positive on this because basically you need private test [00:14:00] sets to prevent contamination.

[00:14:02] swyx: And Scale is one of maybe three or four people this year that has really made an effort in doing a credible private test set leaderboard. Llama405B does well compared to Gemini and GPT 40. And I think that's good. I would say that. You know, it's good to have an open model that is that big, that does well on those metrics.

[00:14:23] swyx: But anyone putting 405B in production will tell you, if you scroll down a little bit to the artificial analysis numbers, that it is very slow and very expensive to infer. Um, it doesn't even fit on like one node. of, uh, of H100s. Cerebras will be happy to tell you they can serve 4 or 5B on their super large chips.

[00:14:42] swyx: But, um, you know, if you need to do anything custom to it, you're still kind of constrained. So, is 4 or 5B really that relevant? Like, I think most people are basically saying that they only use 4 or 5B as a teacher model to distill down to something. Even Meta is doing it. So with Lama 3. [00:15:00] 3 launched, they only launched the 70B because they use 4 or 5B to distill the 70B.

[00:15:03] swyx: So I don't know if like open source is keeping up. I think they're the, the open source industrial complex is very invested in telling you that the, if the gap is narrowing, I kind of disagree. I think that the gap is widening with O1. I think there are very, very smart people trying to narrow that gap and they should.

[00:15:22] swyx: I really wish them success, but you cannot use a chart that is nearing 100 in your saturation chart. And look, the distance between open source and closed source is narrowing. Of course it's going to narrow because you're near 100. This is stupid. But in metrics that matter, is open source narrowing?

[00:15:38] swyx: Probably not for O1 for a while. And it's really up to the open source guys to figure out if they can match O1 or not.

[00:15:46] Alessio: I think inference time compute is bad for open source just because, you know, Doc can donate the flops at training time, but he cannot donate the flops at inference time. So it's really hard to like actually keep up on that axis.

[00:15:59] Alessio: Big, big business [00:16:00] model shift. So I don't know what that means for the GPU clouds. I don't know what that means for the hyperscalers, but obviously the big labs have a lot of advantage. Because, like, it's not a static artifact that you're putting the compute in. You're kind of doing that still, but then you're putting a lot of computed inference too.

[00:16:17] swyx: Yeah, yeah, yeah. Um, I mean, Llama4 will be reasoning oriented. We talked with Thomas Shalom. Um, kudos for getting that episode together. That was really nice. Good, well timed. Actually, I connected with the AI meta guy, uh, at NeurIPS, and, um, yeah, we're going to coordinate something for Llama4. Yeah, yeah,

[00:16:32] Alessio: and our friend, yeah.

[00:16:33] Alessio: Clara Shi just joined to lead the business agent side. So I'm sure we'll have her on in the new year.

[00:16:39] swyx: Yeah. So, um, my comment on, on the business model shift, this is super interesting. Apparently it is wide knowledge that OpenAI wanted more than 6. 6 billion dollars for their fundraise. They wanted to raise, you know, higher, and they did not.

[00:16:51] swyx: And what that means is basically like, it's very convenient that we're not getting GPT 5, which would have been a larger pre train. We should have a lot of upfront money. And [00:17:00] instead we're, we're converting fixed costs into variable costs, right. And passing it on effectively to the customer. And it's so much easier to take margin there because you can directly attribute it to like, Oh, you're using this more.

[00:17:12] swyx: Therefore you, you pay more of the cost and I'll just slap a margin in there. So like that lets you control your growth margin and like tie your. Your spend, or your sort of inference spend, accordingly. And it's just really interesting to, that this change in the sort of inference paradigm has arrived exactly at the same time that the funding environment for pre training is effectively drying up, kind of.

[00:17:36] swyx: I feel like maybe the VCs are very in tune with research anyway, so like, they would have noticed this, but, um, it's just interesting.

[00:17:43] Alessio: Yeah, and I was looking back at our yearly recap of last year. Yeah. And the big thing was like the mixed trial price fights, you know, and I think now it's almost like there's nowhere to go, like, you know, Gemini Flash is like basically giving it away for free.

[00:17:55] Alessio: So I think this is a good way for the labs to generate more revenue and pass down [00:18:00] some of the compute to the customer. I think they're going to

[00:18:02] swyx: keep going. I think that 2, will come.

[00:18:05] Alessio: Yeah, I know. Totally. I mean, next year, the first thing I'm doing is signing up for Devin. Signing up for the pro chat GBT.

[00:18:12] Alessio: Just to try. I just want to see what does it look like to spend a thousand dollars a month on AI?

[00:18:17] swyx: Yes. Yes. I think if your, if your, your job is a, at least AI content creator or VC or, you know, someone who, whose job it is to stay on, stay on top of things, you should already be spending like a thousand dollars a month on, on stuff.

[00:18:28] swyx: And then obviously easy to spend, hard to use. You have to actually use. The good thing is that actually Google lets you do a lot of stuff for free now. So like deep research. That they just launched. Uses a ton of inference and it's, it's free while it's in preview.

[00:18:45] Alessio: Yeah. They need to put that in Lindy.

[00:18:47] Alessio: I've been using Lindy lately. I've been a built a bunch of things once we had flow because I liked the new thing. It's pretty good. I even did a phone call assistant. Um, yeah, they just launched Lindy voice. Yeah, I think once [00:19:00] they get advanced voice mode like capability today, still like speech to text, you can kind of tell.

[00:19:06] Alessio: Um, but it's good for like reservations and things like that. So I have a meeting prepper thing. And so

[00:19:13] swyx: it's good. Okay. I feel like we've, we've covered a lot of stuff. Uh, I, yeah, I, you know, I think We will go over the individual, uh, talks in a separate episode. Uh, I don't want to take too much time with, uh, this stuff, but that suffice to say that there is a lot of progress in each field.

[00:19:28] swyx: Uh, we covered vision. Basically this is all like the audience voting for what they wanted. And then I just invited the best people I could find in each audience, especially agents. Um, Graham, who I talked to at ICML in Vienna, he is currently still number one. It's very hard to stay on top of SweetBench.

[00:19:45] swyx: OpenHand is currently still number one. switchbench full, which is the hardest one. He had very good thoughts on agents, which I, which I'll highlight for people. Everyone is saying 2025 is the year of agents, just like they said last year. And, uh, but he had [00:20:00] thoughts on like eight parts of what are the frontier problems to solve in agents.

[00:20:03] swyx: And so I'll highlight that talk as well.

[00:20:05] Alessio: Yeah. The number six, which is the Hacken agents learn more about the environment, has been a Super interesting to us as well, just to think through, because, yeah, how do you put an agent in an enterprise where most things in an enterprise have never been public, you know, a lot of the tooling, like the code bases and things like that.

[00:20:23] Alessio: So, yeah, there's not indexing and reg. Well, yeah, but it's more like. You can't really rag things that are not documented. But people know them based on how they've been doing it. You know, so I think there's almost this like, you know, Oh, institutional knowledge. Yeah, the boring word is kind of like a business process extraction.

[00:20:38] Alessio: Yeah yeah, I see. It's like, how do you actually understand how these things are done? I see. Um, and I think today the, the problem is that, Yeah, the agents are, that most people are building are good at following instruction, but are not as good as like extracting them from you. Um, so I think that will be a big unlock just to touch quickly on the Jeff Dean thing.

[00:20:55] Alessio: I thought it was pretty, I mean, we'll link it in the, in the things, but. I think the main [00:21:00] focus was like, how do you use ML to optimize the systems instead of just focusing on ML to do something else? Yeah, I think speculative decoding, we had, you know, Eugene from RWKB on the podcast before, like he's doing a lot of that with Fetterless AI.

[00:21:12] swyx: Everyone is. I would say it's the norm. I'm a little bit uncomfortable with how much it costs, because it does use more of the GPU per call. But because everyone is so keen on fast inference, then yeah, makes sense.

[00:21:24] Alessio: Exactly. Um, yeah, but we'll link that. Obviously Jeff is great.

[00:21:30] swyx: Jeff is, Jeff's talk was more, it wasn't focused on Gemini.

[00:21:33] swyx: I think people got the wrong impression from my tweet. It's more about how Google approaches ML and uses ML to design systems and then systems feedback into ML. And I think this ties in with Lubna's talk.

[00:21:45] Synthetic Data and Future Trends

[00:21:45] swyx: on synthetic data where it's basically the story of bootstrapping of humans and AI in AI research or AI in production.

[00:21:53] swyx: So her talk was on synthetic data, where like how much synthetic data has grown in 2024 in the pre training side, the post training side, [00:22:00] and the eval side. And I think Jeff then also extended it basically to chips, uh, to chip design. So he'd spend a lot of time talking about alpha chip. And most of us in the audience are like, we're not working on hardware, man.

[00:22:11] swyx: Like you guys are great. TPU is great. Okay. We'll buy TPUs.

[00:22:14] Alessio: And then there was the earlier talk. Yeah. But, and then we have, uh, I don't know if we're calling them essays. What are we calling these? But

[00:22:23] swyx: for me, it's just like bonus for late in space supporters, because I feel like they haven't been getting anything.

[00:22:29] swyx: And then I wanted a more high frequency way to write stuff. Like that one I wrote in an afternoon. I think basically we now have an answer to what Ilya saw. It's one year since. The blip. And we know what he saw in 2014. We know what he saw in 2024. We think we know what he sees in 2024. He gave some hints and then we have vague indications of what he saw in 2023.

[00:22:54] swyx: So that was the Oh, and then 2016 as well, because of this lawsuit with Elon, OpenAI [00:23:00] is publishing emails from Sam's, like, his personal text messages to Siobhan, Zelis, or whatever. So, like, we have emails from Ilya saying, this is what we're seeing in OpenAI, and this is why we need to scale up GPUs. And I think it's very prescient in 2016 to write that.

[00:23:16] swyx: And so, like, it is exactly, like, basically his insights. It's him and Greg, basically just kind of driving the scaling up of OpenAI, while they're still playing Dota. They're like, no, like, we see the path here.

[00:23:30] Alessio: Yeah, and it's funny, yeah, they even mention, you know, we can only train on 1v1 Dota. We need to train on 5v5, and that takes too many GPUs.

[00:23:37] Alessio: Yeah,

[00:23:37] swyx: and at least for me, I can speak for myself, like, I didn't see the path from Dota to where we are today. I think even, maybe if you ask them, like, they wouldn't necessarily draw a straight line. Yeah,

[00:23:47] Alessio: no, definitely. But I think like that was like the whole idea of almost like the RL and we talked about this with Nathan on his podcast.

[00:23:55] Alessio: It's like with RL, you can get very good at specific things, but then you can't really like generalize as much. And I [00:24:00] think the language models are like the opposite, which is like, you're going to throw all this data at them and scale them up, but then you really need to drive them home on a specific task later on.

[00:24:08] Alessio: And we'll talk about the open AI reinforcement, fine tuning, um, announcement too, and all of that. But yeah, I think like scale is all you need. That's kind of what Elia will be remembered for. And I think just maybe to clarify on like the pre training is over thing that people love to tweet. I think the point of the talk was like everybody, we're scaling these chips, we're scaling the compute, but like the second ingredient which is data is not scaling at the same rate.

[00:24:35] Alessio: So it's not necessarily pre training is over. It's kind of like What got us here won't get us there. In his email, he predicted like 10x growth every two years or something like that. And I think maybe now it's like, you know, you can 10x the chips again, but

[00:24:49] swyx: I think it's 10x per year. Was it? I don't know.

[00:24:52] Alessio: Exactly. And Moore's law is like 2x. So it's like, you know, much faster than that. And yeah, I like the fossil fuel of AI [00:25:00] analogy. It's kind of like, you know, the little background tokens thing. So the OpenAI reinforcement fine tuning is basically like, instead of fine tuning on data, you fine tune on a reward model.

[00:25:09] Alessio: So it's basically like, instead of being data driven, it's like task driven. And I think people have tasks to do, they don't really have a lot of data. So I'm curious to see how that changes, how many people fine tune, because I think this is what people run into. It's like, Oh, you can fine tune llama. And it's like, okay, where do I get the data?

[00:25:27] Alessio: To fine tune it on, you know, so it's great that we're moving the thing. And then I really like he had this chart where like, you know, the brain mass and the body mass thing is basically like mammals that scaled linearly by brain and body size, and then humans kind of like broke off the slope. So it's almost like maybe the mammal slope is like the pre training slope.

[00:25:46] Alessio: And then the post training slope is like the, the human one.

[00:25:49] swyx: Yeah. I wonder what the. I mean, we'll know in 10 years, but I wonder what the y axis is for, for Ilya's SSI. We'll try to get them on.

[00:25:57] Alessio: Ilya, if you're listening, you're [00:26:00] welcome here. Yeah, and then he had, you know, what comes next, like agent, synthetic data, inference, compute, I thought all of that was like that.

[00:26:05] Alessio: I don't

[00:26:05] swyx: think he was dropping any alpha there. Yeah, yeah, yeah.

[00:26:07] Alessio: Yeah. Any other new reps? Highlights?

[00:26:10] swyx: I think that there was comparatively a lot more work. Oh, by the way, I need to plug that, uh, my friend Yi made this, like, little nice paper. Yeah, that was really

[00:26:20] swyx: nice.

[00:26:20] swyx: Uh, of, uh, of, like, all the, he's, she called it must read papers of 2024.

[00:26:26] swyx: So I laid out some of these at NeurIPS, and it was just gone. Like, everyone just picked it up. Because people are dying for, like, little guidance and visualizations And so, uh, I thought it was really super nice that we got there.

[00:26:38] Alessio: Should we do a late in space book for each year? Uh, I thought about it. For each year we should.

[00:26:42] Alessio: Coffee table book. Yeah. Yeah. Okay. Put it in the will. Hi, Will. By the way, we haven't introduced you. He's our new, you know, general organist, Jamie. You need to

[00:26:52] swyx: pull up more things. One thing I saw that, uh, Okay, one fun one, and then one [00:27:00] more general one. So the fun one is this paper on agent collusion. This is a paper on steganography.

[00:27:06] swyx: This is secret collusion among AI agents, multi agent deception via steganography. I tried to go to NeurIPS in order to find these kinds of papers because the real reason Like NeurIPS this year has a lottery system. A lot of people actually even go and don't buy tickets because they just go and attend the side events.

[00:27:22] swyx: And then also the people who go and end up crowding around the most popular papers, which you already know and already read them before you showed up to NeurIPS. So the only reason you go there is to talk to the paper authors, but there's like something like 10, 000 other. All these papers out there that, you know, are just people's work that they, that they did on the air and they failed to get attention for one reason or another.

[00:27:42] swyx: And this was one of them. Uh, it was like all the way at the back. And this is a deep mind paper that actually focuses on collusion between AI agents, uh, by hiding messages in the text that they generate. Uh, so that's what steganography is. So a very simple example would be the first letter of every word.

[00:27:57] swyx: If you Pick that out, you know, and the code sends a [00:28:00] different message than that. But something I've always emphasized is to LLMs, we read left to right. LLMs can read up, down, sideways, you know, in random character order. And it's the same to them as it is to us. So if we were ever to get You know, self motivated, underlined LLMs that we're trying to collaborate to take over the planet.

[00:28:19] swyx: This would be how they do it. They spread messages among us in the messages that we generate. And he developed a scaling law for that. So he marked, I'm showing it on screen right now, the emergence of this phenomenon. Basically, for example, for Cypher encoding, GPT 2, Lama 2, mixed trial, GPT 3. 5, zero capabilities, and sudden 4.

[00:28:40] swyx: And this is the kind of Jason Wei type emergence properties that people kind of look for. I think what made this paper stand out as well, so he developed the benchmark for steganography collusion, and he also focused on shelling point collusion, which is very low coordination. For agreeing on a decoding encoding format, you kind of need to have some [00:29:00] agreement on that.

[00:29:00] swyx: But, but shelling point means like very, very low or almost no coordination. So for example, if I, if I ask someone, if the only message I give you is meet me in New York and you're not aware. Or when you would probably meet me at Grand Central Station. That is the Grand Central Station is a shelling point.

[00:29:16] swyx: And it's probably somewhere, somewhere during the day. That is the shelling point of New York is Grand Central. To that extent, shelling points for steganography are things like the, the, the common decoding methods that we talked about. It will be interesting at some point in the future when we are worried about alignment.

[00:29:30] swyx: It is not interesting today, but it's interesting that DeepMind is already thinking about this.

[00:29:36] Alessio: I think that's like one of the hardest things about NeurIPS. It's like the long tail. I

[00:29:41] swyx: found a pricing guy. I'm going to feature him on the podcast. Basically, this guy from NVIDIA worked out the optimal pricing for language models.

[00:29:51] swyx: It's basically an econometrics paper at NeurIPS, where everyone else is talking about GPUs. And the guy with the GPUs is

[00:29:57] Alessio: talking

[00:29:57] swyx: about economics instead. [00:30:00] That was the sort of fun one. So the focus I saw is that model papers at NeurIPS are kind of dead. No one really presents models anymore. It's just data sets.

[00:30:12] swyx: This is all the grad students are working on. So like there was a data sets track and then I was looking around like, I was like, you don't need a data sets track because every paper is a data sets paper. And so data sets and benchmarks, they're kind of flip sides of the same thing. So Yeah. Cool. Yeah, if you're a grad student, you're a GPU boy, you kind of work on that.

[00:30:30] swyx: And then the, the sort of big model that people walk around and pick the ones that they like, and then they use it in their models. And that's, that's kind of how it develops. I, I feel like, um, like, like you didn't last year, you had people like Hao Tian who worked on Lava, which is take Lama and add Vision.

[00:30:47] swyx: And then obviously actually I hired him and he added Vision to Grok. Now he's the Vision Grok guy. This year, I don't think there was any of those.

[00:30:55] Alessio: What were the most popular, like, orals? Last year it was like the [00:31:00] Mixed Monarch, I think, was like the most attended. Yeah, uh, I need to look it up. Yeah, I mean, if nothing comes to mind, that's also kind of like an answer in a way.

[00:31:10] Alessio: But I think last year there was a lot of interest in, like, furthering models and, like, different architectures and all of that.

[00:31:16] swyx: I will say that I felt the orals, oral picks this year were not very good. Either that or maybe it's just a So that's the highlight of how I have changed in terms of how I view papers.

[00:31:29] swyx: So like, in my estimation, two of the best papers in this year for datasets or data comp and refined web or fine web. These are two actually industrially used papers, not highlighted for a while. I think DCLM got the spotlight, FineWeb didn't even get the spotlight. So like, it's just that the picks were different.

[00:31:48] swyx: But one thing that does get a lot of play that a lot of people are debating is the role that's scheduled. This is the schedule free optimizer paper from Meta from Aaron DeFazio. And this [00:32:00] year in the ML community, there's been a lot of chat about shampoo, soap, all the bathroom amenities for optimizing your learning rates.

[00:32:08] swyx: And, uh, most people at the big labs are. Who I asked about this, um, say that it's cute, but it's not something that matters. I don't know, but it's something that was discussed and very, very popular. 4Wars

[00:32:19] Alessio: of AI recap maybe, just quickly. Um, where do you want to start? Data?

[00:32:26] swyx: So to remind people, this is the 4Wars piece that we did as one of our earlier recaps of this year.

[00:32:31] swyx: And the belligerents are on the left, journalists, writers, artists, anyone who owns IP basically, New York Times, Stack Overflow, Reddit, Getty, Sarah Silverman, George RR Martin. Yeah, and I think this year we can add Scarlett Johansson to that side of the fence. So anyone suing, open the eye, basically. I actually wanted to get a snapshot of all the lawsuits.

[00:32:52] swyx: I'm sure some lawyer can do it. That's the data quality war. On the right hand side, we have the synthetic data people, and I think we talked about Lumna's talk, you know, [00:33:00] really showing how much synthetic data has come along this year. I think there was a bit of a fight between scale. ai and the synthetic data community, because scale.

[00:33:09] swyx: ai published a paper saying that synthetic data doesn't work. Surprise, surprise, scale. ai is the leading vendor of non synthetic data. Only

[00:33:17] Alessio: cage free annotated data is useful.

[00:33:21] swyx: So I think there's some debate going on there, but I don't think it's much debate anymore that at least synthetic data, for the reasons that are blessed in Luna's talk, Makes sense.

[00:33:32] swyx: I don't know if you have any perspectives there.

[00:33:34] Alessio: I think, again, going back to the reinforcement fine tuning, I think that will change a little bit how people think about it. I think today people mostly use synthetic data, yeah, for distillation and kind of like fine tuning a smaller model from like a larger model.

[00:33:46] Alessio: I'm not super aware of how the frontier labs use it outside of like the rephrase, the web thing that Apple also did. But yeah, I think it'll be. Useful. I think like whether or not that gets us the big [00:34:00] next step, I think that's maybe like TBD, you know, I think people love talking about data because it's like a GPU poor, you know, I think, uh, synthetic data is like something that people can do, you know, so they feel more opinionated about it compared to, yeah, the optimizers stuff, which is like,

[00:34:17] swyx: they don't

[00:34:17] Alessio: really work

[00:34:18] swyx: on.

[00:34:18] swyx: I think that there is an angle to the reasoning synthetic data. So this year, we covered in the paper club, the star series of papers. So that's star, Q star, V star. It basically helps you to synthesize reasoning steps, or at least distill reasoning steps from a verifier. And if you look at the OpenAI RFT, API that they released, or that they announced, basically they're asking you to submit graders, or they choose from a preset list of graders.

[00:34:49] swyx: Basically It feels like a way to create valid synthetic data for them to fine tune their reasoning paths on. Um, so I think that is another angle where it starts to make sense. And [00:35:00] so like, it's very funny that basically all the data quality wars between Let's say the music industry or like the newspaper publishing industry or the textbooks industry on the big labs.

[00:35:11] swyx: It's all of the pre training era. And then like the new era, like the reasoning era, like nobody has any problem with all the reasoning, especially because it's all like sort of math and science oriented with, with very reasonable graders. I think the more interesting next step is how does it generalize beyond STEM?

[00:35:27] swyx: We've been using O1 for And I would say like for summarization and creative writing and instruction following, I think it's underrated. I started using O1 in our intro songs before we killed the intro songs, but it's very good at writing lyrics. You know, I can actually say like, I think one of the O1 pro demos.

[00:35:46] swyx: All of these things that Noam was showing was that, you know, you can write an entire paragraph or three paragraphs without using the letter A, right?

[00:35:53] Creative Writing with AI

[00:35:53] swyx: So like, like literally just anything instead of token, like not even token level, character level manipulation and [00:36:00] counting and instruction following. It's, uh, it's very, very strong.

[00:36:02] swyx: And so no surprises when I ask it to rhyme, uh, and to, to create song lyrics, it's going to do that very much better than in previous models. So I think it's underrated for creative writing.

[00:36:11] Alessio: Yeah.

[00:36:12] Legal and Ethical Issues in AI

[00:36:12] Alessio: What do you think is the rationale that they're going to have in court when they don't show you the thinking traces of O1, but then they want us to, like, they're getting sued for using other publishers data, you know, but then on their end, they're like, well, you shouldn't be using my data to then train your model.

[00:36:29] Alessio: So I'm curious to see how that kind of comes. Yeah, I mean, OPA has

[00:36:32] swyx: many ways to publish, to punish people without bringing, taking them to court. Already banned ByteDance for distilling their, their info. And so anyone caught distilling the chain of thought will be just disallowed to continue on, on, on the API.

[00:36:44] swyx: And it's fine. It's no big deal. Like, I don't even think that's an issue at all, just because the chain of thoughts are pretty well hidden. Like you have to work very, very hard to, to get it to leak. And then even when it leaks the chain of thought, you don't know if it's, if it's [00:37:00] The bigger concern is actually that there's not that much IP hiding behind it, that Cosign, which we talked about, we talked to him on Dev Day, can just fine tune 4.

[00:37:13] swyx: 0 to beat 0. 1 Cloud SONET so far is beating O1 on coding tasks without, at least O1 preview, without being a reasoning model, same for Gemini Pro or Gemini 2. 0. So like, how much is reasoning important? How much of a moat is there in this, like, All of these are proprietary sort of training data that they've presumably accomplished.

[00:37:34] swyx: Because even DeepSeek was able to do it. And they had, you know, two months notice to do this, to do R1. So, it's actually unclear how much moat there is. Obviously, you know, if you talk to the Strawberry team, they'll be like, yeah, I mean, we spent the last two years doing this. So, we don't know. And it's going to be Interesting because there'll be a lot of noise from people who say they have inference time compute and actually don't because they just have fancy chain of thought.[00:38:00]

[00:38:00] swyx: And then there's other people who actually do have very good chain of thought. And you will not see them on the same level as OpenAI because OpenAI has invested a lot in building up the mythology of their team. Um, which makes sense. Like the real answer is somewhere in between.

[00:38:13] Alessio: Yeah, I think that's kind of like the main data war story developing.

[00:38:18] The Data War: GPU Poor vs. GPU Rich

[00:38:18] Alessio: GPU poor versus GPU rich. Yeah. Where do you think we are? I think there was, again, going back to like the small model thing, there was like a time in which the GPU poor were kind of like the rebel faction working on like these models that were like open and small and cheap. And I think today people don't really care as much about GPUs anymore.

[00:38:37] Alessio: You also see it in the price of the GPUs. Like, you know, that market is kind of like plummeted because there's people don't want to be, they want to be GPU free. They don't even want to be poor. They just want to be, you know, completely without them. Yeah. How do you think about this war? You

[00:38:52] swyx: can tell me about this, but like, I feel like the, the appetite for GPU rich startups, like the, you know, the, the funding plan is we will raise 60 million and [00:39:00] we'll give 50 of that to NVIDIA.

[00:39:01] swyx: That is gone, right? Like, no one's, no one's pitching that. This was literally the plan, the exact plan of like, I can name like four or five startups, you know, this time last year. So yeah, GPU rich startups gone.

[00:39:12] The Rise of GPU Ultra Rich

[00:39:12] swyx: But I think like, The GPU ultra rich, the GPU ultra high net worth is still going. So, um, now we're, you know, we had Leopold's essay on the trillion dollar cluster.

[00:39:23] swyx: We're not quite there yet. We have multiple labs, um, you know, XAI very famously, you know, Jensen Huang praising them for being. Best boy number one in spinning up 100, 000 GPU cluster in like 12 days or something. So likewise at Meta, likewise at OpenAI, likewise at the other labs as well. So like the GPU ultra rich are going to keep doing that because I think partially it's an article of faith now that you just need it.

[00:39:46] swyx: Like you don't even know what it's going to, what you're going to use it for. You just, you just need it. And it makes sense that if, especially if we're going into. More researchy territory than we are. So let's say 2020 to 2023 was [00:40:00] let's scale big models territory because we had GPT 3 in 2020 and we were like, okay, we'll go from 1.

[00:40:05] swyx: 75b to 1. 8b, 1. 8t. And that was GPT 3 to GPT 4. Okay, that's done. As far as everyone is concerned, Opus 3. 5 is not coming out, GPT 4. 5 is not coming out, and Gemini 2, we don't have Pro, whatever. We've hit that wall. Maybe I'll call it the 2 trillion perimeter wall. We're not going to 10 trillion. No one thinks it's a good idea, at least from training costs, from the amount of data, or at least the inference.

[00:40:36] swyx: Would you pay 10x the price of GPT Probably not. Like, like you want something else that, that is at least more useful. So it makes sense that people are pivoting in terms of their inference paradigm.

[00:40:47] Emerging Trends in AI Models

[00:40:47] swyx: And so when it's more researchy, then you actually need more just general purpose compute to mess around with, uh, at the exact same time that production deployments of the old, the previous paradigm is still ramping up,

[00:40:58] swyx: um,

[00:40:58] swyx: uh, pretty aggressively.

[00:40:59] swyx: So [00:41:00] it makes sense that the GPU rich are growing. We have now interviewed both together and fireworks and replicates. Uh, we haven't done any scale yet. But I think Amazon, maybe kind of a sleeper one, Amazon, in a sense of like they, at reInvent, I wasn't expecting them to do so well, but they are now a foundation model lab.

[00:41:18] swyx: It's kind of interesting. Um, I think, uh, you know, David went over there and started just creating models.

[00:41:25] Alessio: Yeah, I mean, that's the power of prepaid contracts. I think like a lot of AWS customers, you know, they do this big reserve instance contracts and now they got to use their money. That's why so many startups.

[00:41:37] Alessio: Get bought through the AWS marketplace so they can kind of bundle them together and prefer pricing.

[00:41:42] swyx: Okay, so maybe GPU super rich doing very well, GPU middle class dead, and then GPU

[00:41:48] Alessio: poor. I mean, my thing is like, everybody should just be GPU rich. There shouldn't really be, even the GPU poorest, it's like, does it really make sense to be GPU poor?

[00:41:57] Alessio: Like, if you're GPU poor, you should just use the [00:42:00] cloud. Yes, you know, and I think there might be a future once we kind of like figure out what the size and shape of these models is where like the tiny box and these things come to fruition where like you can be GPU poor at home. But I think today is like, why are you working so hard to like get these models to run on like very small clusters where it's like, It's so cheap to run them.

[00:42:21] Alessio: Yeah, yeah,

[00:42:22] swyx: yeah. I think mostly people think it's cool. People think it's a stepping stone to scaling up. So they aspire to be GPU rich one day and they're working on new methods. Like news research, like probably the most deep tech thing they've done this year is Distro or whatever the new name is.

[00:42:38] swyx: There's a lot of interest in heterogeneous computing, distributed computing. I tend generally to de emphasize that historically, but it may be coming to a time where it is starting to be relevant. I don't know. You know, SF compute launched their compute marketplace this year, and like, who's really using that?

[00:42:53] swyx: Like, it's a bunch of small clusters, disparate types of compute, and if you can make that [00:43:00] useful, then that will be very beneficial to the broader community, but maybe still not the source of frontier models. It's just going to be a second tier of compute that is unlocked for people, and that's fine. But yeah, I mean, I think this year, I would say a lot more on device, We are, I now have Apple intelligence on my phone.

[00:43:19] swyx: Doesn't do anything apart from summarize my notifications. But still, not bad. Like, it's multi modal.

[00:43:25] Alessio: Yeah, the notification summaries are so and so in my experience.

[00:43:29] swyx: Yeah, but they add, they add juice to life. And then, um, Chrome Nano, uh, Gemini Nano is coming out in Chrome. Uh, they're still feature flagged, but you can, you can try it now if you, if you use the, uh, the alpha.

[00:43:40] swyx: And so, like, I, I think, like, you know, We're getting the sort of GPU poor version of a lot of these things coming out, and I think it's like quite useful. Like Windows as well, rolling out RWKB in sort of every Windows department is super cool. And I think the last thing that I never put in this GPU poor war, that I think I should now, [00:44:00] is the number of startups that are GPU poor but still scaling very well, as sort of wrappers on top of either a foundation model lab, or GPU Cloud.

[00:44:10] swyx: GPU Cloud, it would be Suno. Suno, Ramp has rated as one of the top ranked, fastest growing startups of the year. Um, I think the last public number is like zero to 20 million this year in ARR and Suno runs on Moto. So Suno itself is not GPU rich, but they're just doing the training on, on Moto, uh, who we've also talked to on, on the podcast.

[00:44:31] swyx: The other one would be Bolt, straight cloud wrapper. And, and, um, Again, another, now they've announced 20 million ARR, which is another step up from our 8 million that we put on the title. So yeah, I mean, it's crazy that all these GPU pores are finding a way while the GPU riches are also finding a way. And then the only failures, I kind of call this the GPU smiling curve, where the edges do well, because you're either close to the machines, and you're like [00:45:00] number one on the machines, or you're like close to the customers, and you're number one on the customer side.

[00:45:03] swyx: And the people who are in the middle. Inflection, um, character, didn't do that great. I think character did the best of all of them. Like, you have a note in here that we apparently said that character's price tag was

[00:45:15] Alessio: 1B.

[00:45:15] swyx: Did I say that?

[00:45:16] Alessio: Yeah. You said Google should just buy them for 1B. I thought it was a crazy number.

[00:45:20] Alessio: Then they paid 2. 7 billion. I mean, for like,

[00:45:22] swyx: yeah.

[00:45:22] Alessio: What do you pay for node? Like, I don't know what the game world was like. Maybe the starting price was 1B. I mean, whatever it was, it worked out for everybody involved.

[00:45:31] The Multi-Modality War

[00:45:31] Alessio: Multimodality war. And this one, we never had text to video in the first version, which now is the hottest.

[00:45:37] swyx: Yeah, I would say it's a subset of image, but yes.

[00:45:40] Alessio: Yeah, well, but I think at the time it wasn't really something people were doing, and now we had VO2 just came out yesterday. Uh, Sora was released last month, last week. I've not tried Sora, because the day that I tried, it wasn't, yeah. I

[00:45:54] swyx: think it's generally available now, you can go to Sora.

[00:45:56] swyx: com and try it. Yeah, they had

[00:45:58] Alessio: the outage. Which I [00:46:00] think also played a part into it. Small things. Yeah. What's the other model that you posted today that was on Replicate? Video or OneLive?

[00:46:08] swyx: Yeah. Very, very nondescript name, but it is from Minimax, which I think is a Chinese lab. The Chinese labs do surprisingly well at the video models.

[00:46:20] swyx: I'm not sure it's actually Chinese. I don't know. Hold me up to that. Yep. China. It's good. Yeah, the Chinese love video. What can I say? They have a lot of training data for video. Or a more relaxed regulatory environment.

[00:46:37] Alessio: Uh, well, sure, in some way. Yeah, I don't think there's much else there. I think like, you know, on the image side, I think it's still open.

[00:46:45] Alessio: Yeah, I mean,

[00:46:46] swyx: 11labs is now a unicorn. So basically, what is multi modality war? Multi modality war is, do you specialize in a single modality, right? Or do you have GodModel that does all the modalities? So this is [00:47:00] definitely still going, in a sense of 11 labs, you know, now Unicorn, PicoLabs doing well, they launched Pico 2.

[00:47:06] swyx: 0 recently, HeyGen, I think has reached 100 million ARR, Assembly, I don't know, but they have billboards all over the place, so I assume they're doing very, very well. So these are all specialist models, specialist models and specialist startups. And then there's the big labs who are doing the sort of all in one play.

[00:47:24] swyx: And then here I would highlight Gemini 2 for having native image output. Have you seen the demos? Um, yeah, it's, it's hard to keep up. Literally they launched this last week and a shout out to Paige Bailey, who came to the Latent Space event to demo on the day of launch. And she wasn't prepared. She was just like, I'm just going to show you.

[00:47:43] swyx: So they have voice. They have, you know, obviously image input, and then they obviously can code gen and all that. But the new one that OpenAI and Meta both have but they haven't launched yet is image output. So you can literally, um, I think their demo video was that you put in an image of a [00:48:00] car, and you ask for minor modifications to that car.

[00:48:02] swyx: They can generate you that modification exactly as you asked. So there's no need for the stable diffusion or comfy UI workflow of like mask here and then like infill there in paint there and all that, all that stuff. This is small model nonsense. Big model people are like, huh, we got you in as everything in the transformer.

[00:48:21] swyx: This is the multimodality war, which is, do you, do you bet on the God model or do you string together a whole bunch of, uh, Small models like a, like a chump. Yeah,

[00:48:29] Alessio: I don't know, man. Yeah, that would be interesting. I mean, obviously I use Midjourney for all of our thumbnails. Um, they've been doing a ton on the product, I would say.

[00:48:38] Alessio: They launched a new Midjourney editor thing. They've been doing a ton. Because I think, yeah, the motto is kind of like, Maybe, you know, people say black forest, the black forest models are better than mid journey on a pixel by pixel basis. But I think when you put it, put it together, have you tried

[00:48:53] swyx: the same problems on black forest?

[00:48:55] Alessio: Yes. But the problem is just like, you know, on black forest, it generates one image. And then it's like, you got to [00:49:00] regenerate. You don't have all these like UI things. Like what I do, no, but it's like time issue, you know, it's like a mid

[00:49:06] swyx: journey. Call the API four times.

[00:49:08] Alessio: No, but then there's no like variate.

[00:49:10] Alessio: Like the good thing about mid journey is like, you just go in there and you're cooking. There's a lot of stuff that just makes it really easy. And I think people underestimate that. Like, it's not really a skill issue, because I'm paying mid journey, so it's a Black Forest skill issue, because I'm not paying them, you know?

[00:49:24] Alessio: Yeah,

[00:49:25] swyx: so, okay, so, uh, this is a UX thing, right? Like, you, you, you understand that, at least, we think that Black Forest should be able to do all that stuff. I will also shout out, ReCraft has come out, uh, on top of the image arena that, uh, artificial analysis has done, has apparently, uh, Flux's place. Is this still true?

[00:49:41] swyx: So, Artificial Analysis is now a company. I highlighted them I think in one of the early AI Newses of the year. And they have launched a whole bunch of arenas. So, they're trying to take on LM Arena, Anastasios and crew. And they have an image arena. Oh yeah, Recraft v3 is now beating Flux 1. 1. Which is very surprising [00:50:00] because Flux And Black Forest Labs are the old stable diffusion crew who left stability after, um, the management issues.

[00:50:06] swyx: So Recurve has come from nowhere to be the top image model. Uh, very, very strange. I would also highlight that Grok has now launched Aurora, which is, it's very interesting dynamics between Grok and Black Forest Labs because Grok's images were originally launched, uh, in partnership with Black Forest Labs as a, as a thin wrapper.

[00:50:24] swyx: And then Grok was like, no, we'll make our own. And so they've made their own. I don't know, there are no APIs or benchmarks about it. They just announced it. So yeah, that's the multi modality war. I would say that so far, the small model, the dedicated model people are winning, because they are just focused on their tasks.

[00:50:42] swyx: But the big model, People are always catching up. And the moment I saw the Gemini 2 demo of image editing, where I can put in an image and just request it and it does, that's how AI should work. Not like a whole bunch of complicated steps. So it really is something. And I think one frontier that we haven't [00:51:00] seen this year, like obviously video has done very well, and it will continue to grow.

[00:51:03] swyx: You know, we only have Sora Turbo today, but at some point we'll get full Sora. Oh, at least the Hollywood Labs will get Fulsora. We haven't seen video to audio, or video synced to audio. And so the researchers that I talked to are already starting to talk about that as the next frontier. But there's still maybe like five more years of video left to actually be Soda.

[00:51:23] swyx: I would say that Gemini's approach Compared to OpenAI, Gemini seems, or DeepMind's approach to video seems a lot more fully fledged than OpenAI. Because if you look at the ICML recap that I published that so far nobody has listened to, um, that people have listened to it. It's just a different, definitely different audience.

[00:51:43] swyx: It's only seven hours long. Why are people not listening? It's like everything in Uh, so, so DeepMind has, is working on Genie. They also launched Genie 2 and VideoPoet. So, like, they have maybe four years advantage on world modeling that OpenAI does not have. Because OpenAI basically only started [00:52:00] Diffusion Transformers last year, you know, when they hired, uh, Bill Peebles.

[00:52:03] swyx: So, DeepMind has, has a bit of advantage here, I would say, in, in, in showing, like, the reason that VO2, while one, They cherry pick their videos. So obviously it looks better than Sora, but the reason I would believe that VO2, uh, when it's fully launched will do very well is because they have all this background work in video that they've done for years.

[00:52:22] swyx: Like, like last year's NeurIPS, I already was interviewing some of their video people. I forget their model name, but for, for people who are dedicated fans, they can go to NeurIPS 2023 and see, see that paper.

[00:52:32] Alessio: And then last but not least, the LLMOS. We renamed it to Ragops, formerly known as

[00:52:39] swyx: Ragops War. I put the latest chart on the Braintrust episode.

[00:52:43] swyx: I think I'm going to separate these essays from the episode notes. So the reason I used to do that, by the way, is because I wanted to show up on Hacker News. I wanted the podcast to show up on Hacker News. So I always put an essay inside of there because Hacker News people like to read and not listen.

[00:52:58] Alessio: So episode essays,

[00:52:59] swyx: I remember [00:53:00] purchasing them separately. You say Lanchain Llama Index is still growing.

[00:53:03] Alessio: Yeah, so I looked at the PyPy stats, you know. I don't care about stars. On PyPy you see Do you want to share your screen? Yes. I prefer to look at actual downloads, not at stars on GitHub. So if you look at, you know, Lanchain still growing.

[00:53:20] Alessio: These are the last six months. Llama Index still growing. What I've basically seen is like things that, One, obviously these things have A commercial product. So there's like people buying this and sticking with it versus kind of hopping in between things versus, you know, for example, crew AI, not really growing as much.

[00:53:38] Alessio: The stars are growing. If you look on GitHub, like the stars are growing, but kind of like the usage is kind of like flat. In the last six months, have they done some

[00:53:46] swyx: kind of a reorg where they did like a split of packages? And now it's like a bundle of packages. Sometimes that happens, you know, I didn't see that.

[00:53:54] swyx: I can see both. I can, I can see both happening. The crew AI is, is very loud, but, but not used. [00:54:00] And then,

[00:54:00] Alessio: yeah. But anyway, to me, it's just like, yeah, there's no split. I mean, auto similar with LGBT is like, they're still a wait list. For auto GPT to be used. Yeah, they're

[00:54:12] swyx: still kicking. They announced some stuff recently.

[00:54:14] swyx: But I think

[00:54:14] Alessio: that's another one. It's the fastest growing project in the history of GitHub. But I think, you know, when you maybe like run the numbers on like the value of the stars and like the value of the hype. I think in AI you see this a lot, which is like a lot of stars, a lot of interest at a rate that you didn't really see in the past in open source, where nobody's running to start.

[00:54:33] Alessio: Uh, you know, a NoSQL database. It's kind of like just to be able to actually use it. Yeah.

[00:54:37] swyx: I think one thing that's interesting here, one obviously is that in AI, you kind of get paid to promise things and then you, to deliver them, you know, people have a lot of patience. I think that patience has come down over time.

[00:54:49] swyx: One example here is Devin, right this year, where a lot of promise in March and then, and then it took nine months to get to GA. Uh, but I think people are still coming around now and Devin, Devin's [00:55:00] product has improved a little bit, hasn't he? Even you're going to be a paying customer. So I think something Devon like will work.

[00:55:05] swyx: I don't know if it's Devon itself. The Auto GPT has an interesting second layer in terms of what I think is the dynamics going on here, which is a very AI specific layer. Over promising under delivering applies to any startup, but for AI specifically, there's this promise of generality that I can do anything, right?

[00:55:24] swyx: So Auto GPT's initial problem was making money, like increase my net worth. And I think. That means that there's a lot of broad interest from a lot of different people who are trying to do all different things on this one project. So that's why this concentrates a lot of stars. And then obviously, because it does too much, maybe, or it's not focused enough, then it fails to deploy.

[00:55:44] swyx: So that would be my explanation for why the interest to usage ratio is so low. And the second one is obviously pure execution, like the team needs to have a vision and execute, like half the core team left right after AI Engineer Summit last year. [00:56:00] That will be my explanation as to why, like this promise of generality works basically only for ChatGPT and maybe for this year's Notebook LM.

[00:56:09] swyx: Like, sticking anything in there, it'll mostly be direct. And then for basically everyone else, it's like, you know, we will help you complete code, we will help you with your PR reviews. Like, small things.

[00:56:21] Alessio: Alright, code interpreting, we talked about a bunch of times. We soft announced the E2B fundraising on this podcast.

[00:56:29] Alessio: Code sandbox got acquired by Together AI. Last week, um, which are now also going to offer as an API. So, uh, more and more activity, which is great. Yeah. And then, uh, in the last step, two episodes ago with Bolt, we talked about the web container stuff that we've been working on. I think like there's maybe the spectrum of code interpreting, which is like, You know, dedicated SDK.

[00:56:53] Alessio: There's like, yeah, the models of the world, which is like, Hey, we got a sandbox. Now you just kind of run the commands and orchestrate all of that. [00:57:00] I think this is one of the, I mean, it'd be screwed. That's just been crazy just because, I mean. Everybody needs to run code, right? And I think now all the products and the everybody's graduating to like, okay, it's not enough to just do chat.

[00:57:13] Alessio: So perplexity, which is a easy to be customers, they do all these nice charts for like finance and all these different things. It's like the products are maturing and I think this is becoming more and more of kind of like a hair on fire. problem, so to speak. So yeah, excited to see more. And this was one that really wasn't on the radar when we first wrote

[00:57:32] swyx: the four wars.

[00:57:33] swyx: Yeah, I think mostly because I was trying to limit it to Ragnops. But I think now that the frontier has expanded in terms of the core set of tools, core set of tools would include Code interpreting, like, like tools that every agent needs, right? And Graham in his state of agents talk had this as well, which is kind of interesting for me.

[00:57:55] swyx: Cause like everyone finds the same set of things. So it's basically like someone, [00:58:00] everyone needs web browsing. Everyone needs. Code interpreting, and then everyone needs some kind of memory or planning or whatever that is. We'll discover this more over time, but I think this is what we've discovered so far.

[00:58:12] swyx: I will also call out Morphlabs for launching a time travel VM. I think that basically the statefulness of these things needs to be locked down. A lot. Basically, you can't just spin up a VM, run code on it, and then kill it. It's because sometimes you might need to time travel back, like unwind, or fork, to explore different paths for sort of like a tree search approach to your agent development.

[00:58:38] swyx: I would call out the newer ones, the new implementations as The emerging frontier in terms of like what people kind of are going to need for agents to do very fan out approaches to all this sort of code execution. And then I'll also call out that I think chat2bt canvas with what they launched in the 12 days of shipmas that they announced has surprisingly superseded Code Interpreter.

[00:58:59] swyx: Like [00:59:00] Code Interpreter was last year's thing. And now canvas can also write code and also run code. And do more than Code Interpreter used to do. So right now it has not killed it. So there's, there's a toggle box for Canvas and for Code Interpreter when you create a new custom GPTs. You know, my, my old thesis that custom GPTs is your roadmap for investing because it's, it's what everyone needs.

[00:59:17] swyx: So now there's a new box called Canvas that everyone has access to, but basically there's no reason why you should use Code Interpreter over Canvas. Like Canvas has incorporated the diff mode that both Anthropic and OpenAI and Fireworks has now shipped that I is going to be the norm for next year. Uh, that everyone needs some kind of diff mode code interpreter thing.

[00:59:38] swyx: Like Aitor was also very early to this. Like the Aitor benchmarks were also all based on diffs and Coursera as well.

[00:59:45] Alessio: You want to talk about memory? Memory? Uh, you think it's not real? Yeah, I just don't. I think most memory product today, just like a summarization and extraction. I don't think they're very immature.

[00:59:58] Alessio: Yeah, there's no implicit [01:00:00] memory, you know, it's not explicit memory of what you've written. There's no implicit extraction of like, Oh, use a node to this, use a node to this 10 times, so you don't like going on hikes at 6am. Like it doesn't, none of the memory products do that. They'll summarize what you say explicitly.

[01:00:18] Alessio: When you say

[01:00:18] swyx: memory products, you mean that the startups that are more offering memory as a service?

[01:00:22] Alessio: Yeah, or even like, you know, it's like memories, you know, it's like based on what I say, it remembers it. So it's less about making an actual memory of my preference, it's more about what I explicitly said, um, and I'm trying to figure out at what level that gets solved, you know, like, is it, do these memory products, like the MGPTs of the world, create a better way to implicitly extract preference or can that be done very well, you know, I think that's why I don't think, it's not that I don't think memory is real, I just don't think that like,

[01:00:57] swyx: I would actually agree with that, but I [01:01:00] would just point it to it being immature rather than not needed. It's clearly something that we will want at some point. And so the people developing it now are trying You know, I'm not very good at it, and I would definitely predict that next year will be better, and the year after that will be better than that.

[01:01:17] swyx: I definitely think that last time we had the shouldn't you pod with Harrison as a guest host, I over focused on LangMem as a separate product. He has now rolled it into LangGraph as a memory service with the same API. And I think that Everyone will need some kind of memory, and I think that this is, has distinguished itself now as a separate need from a normal rag vector database.

[01:01:38] swyx: Like, you will need a memory layer, whether it's on top of a vector database or not, it's up to you. A memory database and a vector database are kind of two different things. Like, I've had to justify this so much, actually, that I have a draft post in the, in Latentspace dashboard that, Uh, basically says like, what is the difference between memory and knowledge?

[01:01:53] swyx: And to me, it's very clear. It's like, knowledge is about the world around you, and like, there's knowledge that you have, which is the rag [01:02:00] corpus that you're, maybe your company docs or whatever. And then there's external knowledge, which is the stuff that you Google. So you use something like Exa, whatever.

[01:02:07] swyx: And then there's memory, which is my interactions with you over time. Both can be represented by vector databases or knowledge graphs, doesn't really matter. Time is a specifically important one in memory because you need a decay function, and then you also need like a review function. A lot of people are implementing this as sleep.

[01:02:24] swyx: Like when you sleep, you like literally you sort of process the day's memories, and you come up with new insights that you then persist and bring into context in the future. So I feel like this is being developed. Langrath has a version of this. ZEP is another one that's based on Neo4j's knowledge graph that has a version of this.

[01:02:40] swyx: Um, MGPT used to have this, but I think, I feel like Leda, since it was funded by Quiet Capital has broadened out into more of a sort of general LLMOS type startup, which I feel like there's a bunch of those now, there's this all hands and all this.

[01:02:55] Alessio: Do you think this is a LLMOS product or should it be a consumer product?

[01:02:59] swyx: I think it's a [01:03:00] building block. I think every, I mean, there should be, just like every consumer product is going to have a, going to eventually want a gateway, you know, for, for managing their requests and ops tool, you know, that kind of stuff, um, code interpreter for maybe not exposing the code, but executing code under the hood for sure.

[01:03:18] swyx: So it's going to want memory. So as a consumer, let's say you are a new doc computer who, um, you know, they've, they've launched their own, uh, little agents or if you're a friend. com, you're going to want to invest in memory at some point. Maybe it's not today. Maybe you can push it off a lot further with like a million token context, but at some point you need to compress your memory and to selectively retrieve it.

[01:03:43] swyx: And. Then what are you going to do? You have to reinvent the whole memory stack, and these guys have been doing it for a year now.

[01:03:49] Alessio: Yeah, to me, it's more like I want to bring the memories. It's almost like they're my memories, right? So why do you

[01:03:56] swyx: selectively choose the memories to bring in? Yeah,

[01:03:57] Alessio: why does every time that I go to a new product, [01:04:00] it needs to relearn everything about me?

[01:04:01] Alessio: Okay, you want portable memories. Yeah, is it like a protocol? Like, how does that work?

[01:04:06] swyx: Speaking of protocols, Anthropic's model context protocol that they launched has a 300 line of code memory implementation. Very simple. Very bad news for all the memory startups. But that's all you need. And yeah, it would be nice to have a portable memory of you to ship to everyone else.

[01:04:23] swyx: Simple answer is there's no standardization for a while because everyone will experiment with their own stuff. And I think, Anthropic success with MCP suggests that basically no one else but the big labs can do it because no one else has the sway to do this, then that's, that's how it's going to be, like, unless you have something silly, like, okay, some one form of standardization basically came from Georgie Griganov with Llama CPP, right?

[01:04:50] swyx: And that was completely open source, completely bottoms up. And that's because there's just a significant amount of work that needed to be done there. And then people build up from there. Another form of standardization is Confit UI from Confit Anonymous. [01:05:00] So like, that kind of standardization can be done.

[01:05:03] swyx: So someone basically has to Create that for the roleplay community, because those are the people with the longest memories right now, the roleplay community, as far as I understand it, I've looked at Soli Tavern, I've looked at Cobalt, they only share character cards, and there's like four or five different standardized standard versions of these character cards.

[01:05:22] swyx: But nobody has exportable memory yet. If there was anyone that developed memory first that became a standard, it would be those guys.

[01:05:28] Alessio: Cool. Excited to see. Thank you. What people built.

[01:05:31] The Future of AI Benchmarks

[01:05:31] Alessio: Benchmarks. Okay. One of our favorite pet topics.

[01:05:34] swyx: Uh, yeah, yeah. Um, so basically I just wanted to mention this briefly. Like, um, I think that in a year, end of year review, it's useful to remind everybody where we were.

[01:05:44] swyx: So we talked about how in LMS's ELO, everyone has gone up and it's a very close race. And I think benchmarks as well. I was looking at the OpenAI live stream today. When they introduced O1API with structured output and everything. And the benchmarks [01:06:00] they're talking about are like completely different than the benchmarks that we were talking about this time last year.

[01:06:07] swyx: This time last year, we were still talking about MMLU, a little bit of, there's still like GSMAK. There's stuff that's basically in V, One of the hugging face open models leaderboard, right? We talked to Clementine about the decisions that she made to upgrade to V2. I will also say LM Sys, now LM Arena also has emerged this year as, as a, as the leading like battlegrounds between the big frontier labs, but also we have also seen like the emergence of SuiBench, LiveBench, MMU Pro, and Amy, Amy specifically for one, it will be interesting to see that, you know, Top most cited benchmarks of the year from 2020 to 2021, 2, 3, 4, and then going to 5.

[01:06:50] swyx: And you can see what has been saturated and solved and what people care about now. And so now people care a lot about frontier math coding, right? There's literally a benchmark called frontier [01:07:00] math, which I spent a bit of time talking about at NeurIPS. There's Amy, there's Livebench, there's MMORPG Pro, and there's SweetBench.

[01:07:07] swyx: I feel like this is good. And then, um, there's another one. This time last year, it was GPQA. I'll put math and GPQA here as sort of top benchmarks of last year. At NeurIPS, GPQA was declared dead, which is very sad. People are still talking about GPQA Diamond. So, literally, the name of GPQA is called Google Proof Question Answering.

[01:07:28] swyx: So it's supposed to be resistant to saturation for a while. Bye. Uh, and Noam Brown said that GPQ was dead. So now we only care about SuiteBench, LiveBench, MMORPG Pro, AME. And even SuiteBench, we don't care about SuiteBench proper. We care about SuiteBench verified. Uh, we, we care about the SuiteBench multi modal.

[01:07:44] swyx: And then we also care about the new Kowinski prize from Andy Kowinski, which is the guy that we talked to yesterday, who has launched a similar sort of Arc AGI attempt on a SuiteBench type metric, which Arguably, it's a bit more useful. OpenAI also has [01:08:00] MLEbench, which is more tracking sort of ML research and bootstrapping, which arguably like this is the key metric that is most relevant for the Frontier Labs, which is when the researchers can automate their own jobs.

[01:08:11] swyx: So that is a kink in the acceleration curve, if we were ever to reach that.

[01:08:15] Alessio: Yeah, that makes sense. I mean, I'm curious, I think Dylan, At the debate he said SweetBench 80 percent was like a soap for end of next year as a kind of like, you know, watermark that the moms are still improving. And keeping

[01:08:28] swyx: when we started the year at 13%.

[01:08:30] Alessio: Yeah, exactly.

[01:08:31] swyx: And so now we're about 50, um, open hands is around there. And yeah, 80 sounds fine. Uh, Kowinski prize is 90.

[01:08:38] Alessio: And then as we get to a hundred,

[01:08:39] swyx: then the open source catches up. Oh yeah, magically going to close the gap between the closed source and open source. So basically I think my advice to people is keep track of the slow cooking of benchmark language because the labs that are not that frontier will keep measuring themselves on last year's benchmarks and then the labs that are actually frontier will Tell you about [01:09:00] benchmarks you've never heard of and you'll be like, Oh, like, okay, there's, there's new, there's new territory to, to, to go on.

[01:09:05] swyx: That would be the quick tip there. Yeah. And maybe, maybe I won't, uh, belabor this point too much. I was also saying maybe Veo has introduced some new video benchmarks, right? Like basically every new frontier capabilities and this, the next section that we're going to go into introduces new benchmarks.

[01:09:18] swyx: We'll also briefly talk about Ruler as like the, the new setup. Uh, you know, last year we was like needle in a haystack and Ruler is basically a multidimensional needle in a haystack.

[01:09:26] Alessio: Yeah, we'll link on the episodes. Yeah, this is like a review of all

[01:09:30] swyx: the episodes that we've done, which I have in my head.

[01:09:32] swyx: This is one of the slides that I did on my Dev Day talk. So we're moving on from benchmarks to capabilities. And I think I have a useful categorization that I've been kind of selling. I'd be curious on your feedback or edits. I think there's basically like, I kind of like the thought spot. MMLU is a model of what's mature, what's emerging, what's frontier, what's niche.

[01:09:51] swyx: So mature is stuff that you can just rely on in production, it's solved, everyone has it. So what's solved is general knowledge, MMLU. And what's solved is kind of long context, everyone [01:10:00] has 128K. Today O1 announced 200K, which is Very expensive. I don't know what the price is. What's solved? Kind of solved is RAG.

[01:10:09] swyx: There's like 18 different kinds of RAG, but it's mostly solved. Bash transcription, I would say Whisper, is something that you should be using on a as much as possible. And then code generation, kind of solved. There's different tiers of code generation, and I really need to split out single line autocomplete versus multi file generation.

[01:10:27] swyx: I think that is definitely emerging. So on the emerging side, tool use, I would still kind of solve. Consider emerging, maybe, maybe more mature already. But they only launched for short output this year. Yeah, yeah, yeah. I think emerging

[01:10:37] Alessio: is fine.

[01:10:38] swyx: Vision language models, everyone has vision now, I think. Yeah, including Owen.

[01:10:42] swyx: So this is clear. A subset of vision is PDF parsing. And I think the community is very excited about the work being done with CodePoly and CodeQuin. What's for you the breakpoint for vision to go to mature? I think it's basically now. This is maybe two months old. Yeah, yeah, yeah. [01:11:00] NVIDIA, most valuable company in the world.

[01:11:02] swyx: Also, I think, this was in June, then also they surprised a lot on the upside for their Q3 earnings. I think the quote that I highlighted in AI News was that it is the best, like Blackwell is the best selling series. The in, in the history of the company and they're sold. I mean, obviously they're always sold out, but for him to make that statement, I think it's a, it's another indication that the transition from the H to the B series is gonna go very well.

[01:11:30] Alessio: Yeah, the, I mean, if you had just bought N Video and charge your BT game out,

[01:11:33] swyx: that would be, yeah. Insane. Uh, you know, which one more, you know, Nvidia Bitcoin, I think, I think Nvidia,

[01:11:40] Alessio: I think in gains. Yeah.

[01:11:41] swyx: Well, I think the question is like, people ask me like, is there, what's the reason to not invest in Nvidia?

[01:11:45] swyx: I think it's really just like the. They have committed to this. They went for a two year cycle to one year cycle, right? And so, it takes one misstep to delay. You know, like, there have been delays in the past. And, like, when delays happen, they're typically very good buying opportunities. Anyway. [01:12:00] Hey, this is Swyx from the editing room.

[01:12:03] swyx: I actually just realized that we lost about 15 minutes of audio and video that was in the episode that we shipped, and I'm just cutting it back in and re recording. We don't have time to re record before the end of the year. At least I'm a 31st already, so I'm just going to do my best to re cover what we have and then sort of segue you in nicely to the end.

[01:12:26] swyx: Uh, so our plan was basically to cover like what we felt was emerging capabilities, frontier capabilities, and niche capabilities. So emerging would be tool use, visual language models, which you just heard, real time transcription, which I have on one of our upcoming episodes, The Bee, as well as you can try it in Whisper Web GPU, which is amazing.

[01:12:46] swyx: Uh, I think diarization capabilities are also maturing as well, but still way too hard to do properly. Like we, we had to do a lot of stuff for the latent space transcripts to, to come out right. Um, I think [01:13:00] maybe, you know, Dwarkesh recently has been talking about how he's using Gemini 2. 0 flash to do it.

[01:13:04] swyx: And I think that might be a good effort, a good way to do it. And especially if there's crosstalk involved, that might be really good. But, uh, there might be other reasons to use normal diarization models as well.

[01:13:17] Pionote and Frontier Models

[01:13:17] swyx: Specifically, pionote. Text and image, we talked about a lot, so I'm just going to skip. And then we go to Frontier, which I think, like, basically, I would say, is on the horizon, but not quite ready for broad usage.

[01:13:28] swyx: Like, it's, you know, interesting to show off to people, but, like, we haven't really figured out how, like, the daily use, the large amount of money is going to be made on long inference, on real time, interruptive, Sort of real time API voice mode things on on device models, as well as all the other modalities.

[01:13:47] Niche Models and Base Models

[01:13:47] swyx: And then niche models, uh, niche capabilities. I always say, like, base models are very underrated. People always love talking to base models as well, um, and we're increasingly getting less access to them. Uh, it's quite [01:14:00] possible, I think, you know, Sam Altman for 2025 was like, asking about what he should, what people want him to ship, or what people want him to open source, and people really want GPT 3 base.

[01:14:10] swyx: Uh,

[01:14:10] swyx: we may get it. We may get it. It's just for historical interest. Um, but, uh, you know, at this point, but we may get it. Like, it's definitely not a significant IP anymore for him. So, we'll see. Um, you know, I think OpenAI has a lot more things to worry about than shipping based models, but it would be very, very nice things to do for the community.

[01:14:30] State Space Models and RWKB

[01:14:30] swyx: Um, state space models as well. I would say, like, the hype for state space models this year, even though, um, you know, the post transformers talk at Linspace Live was extremely hyped, uh, and very well attended and watched. Um, I would say, like, it feels like a step down this year. I don't know why. Um, It seems like things are scaling out in states based models and RWKBs.

[01:14:53] swyx: So Cartesia, I think, is doing extremely well. We use them for a bunch of stuff, especially for Smalltalks and some of our [01:15:00] sort of Notebook LN podcast clones. I think they're a real challenger to 11 labs as well. And RWKB, of course, is rolling out on Windows. So, um, I, I, I'll still, I'll still say these, these are niches.

[01:15:12] swyx: We've been talking about them as the future for a long time. And, I mean, we live technically in a year in the future from last year, and we're still saying the exact same things as we were saying last year. So, what's changed? I don't know. Um, I do think the xLSTM paper, which we will cover when we cover the, sort of, NeurIPS papers, um, is worth a look.

[01:15:31] swyx: Um, I, I, I think they, they are very clear eyed as to, um, How do you want to fix LSTM? Okay, so, and then we also want to cover a little bit, uh, like the major themes of the year. Um, and then we wanted to go month by month. So I'll bridge you into, back to the recording, which, uh, we still have the audio of.

[01:15:48] Inference Race and Price Wars

[01:15:48] swyx: So, the main, one of the major themes is sort of the inference race at the bottom.

[01:15:51] swyx: We started this, uh, last year, this time last year with the misdrawl price war of 2023. Um, with a mixed trial going [01:16:00] from 1. 80 per token down to 1. 27, uh, in the span of like a couple of weeks. And, um, you know, I think this, uh, a lot of people are also interested in the price war, sort of the price intelligence curve for this year as well.

[01:16:15] swyx: Um, I started tracking it, I think, roundabout in March of 2024 with, uh, Haiku's launch. And so this is, uh, if you're watching the YouTube, this is. What I initially charted out as like, here's the frontier, like everyone's kind of like in a pretty tight range of LMS's ELO versus the model pricing, you can pay more for more intelligence, and you and it'll be cheaper to get less intelligence, but roughly it correlates to aligned, and it's a trend line.

[01:16:43] swyx: And then I could update it again in July and see that everything had kind of shifted right. So for the same amount of ELO, let's say GPT 4, 2023. Cloud 3 would be about sort of 11. 75 in ELO, and you used to get that for [01:17:00] like 40 per token, per million tokens. And now you get Cloud 3 Haiku, which is about the same ELO, for 0.

[01:17:07] swyx: 50. And so that's a two orders of magnitude improvement in about two years. Sorry, in about a year. Um, but more, more importantly, I think, uh, you can see the more recent launches like Cloud3 Opus, which launched in March this year. Um, now basically superseded, completely, completely dominated by Gemini 1. 5 Pro, which is both cheaper, 5 a month, uh, 5 per million, as well as smarter.

[01:17:31] swyx: Uh, so it's about slightly higher than Elo. Um, so, the March frontier. And shift to the July frontier is roughly one order of magnitude improvement per, uh, sort of ISO ELO. Um, and I think what you're starting to see now, uh, in July is the emergence of 4. 0 Mini and DeepSeq v2 as outliers to the July frontier, where July frontier used to be maintained by 4.

[01:17:54] swyx: 0. Llama405, Gemini 1. 5 Flash, and Mistral and Nemo. These things kind of break the [01:18:00] frontier. And then if you update it like a month later, I think if I go back a month here, You update it, you can see more items start to appear. Uh, here as well with the August frontier, with Gemini 1. 5 Flash coming out, uh, with an August update as, as compared to the June update, um, being a lot cheaper, uh, and roughly the same ELO.

[01:18:20] swyx: And then, uh, we update for September, um, and that, this is one of those things where, um, it really started to, to, we really started to understand the pricing curves being real instead of something that some random person on the internet drew, uh, Who drew on a chart? Because Gemini 1. 5 cut their prices and cut their prices exactly in line with where everyone else is in terms of their Elo price charts If you plot by September we had a O1 preview in pricing and costs and Elos um, so the frontier was O1 preview GPC 4.

[01:18:53] swyx: 0. 0. 1 mini, 4. 0. 0. 0 mini, and then Gemini Flash at the low end. That was the [01:19:00] frontier as of September. Gemini 1. 5 Pro was not on that frontier. Then they cut their prices, uh, they halved their prices, and suddenly they were on the frontier. Um, and so it's a very, very tight and predictive line, which I thought it was really interesting and entertaining as well.

[01:19:15] swyx: Um, and I thought that was kind of cool. In November, we had 3. 5 haiku new. Um, and obviously we had sonnet as well, uh, sonnet as, uh, as not, I don't know where there's sonnet on this chart, but, Um, haiku new, uh, basically, uh, was 4x the price of old haiku. Or, uh, sorry, 3. 5 haiku was 4x the price of 3 haiku. And people were kind of unhappy about that.

[01:19:42] swyx: Um, there's a reasonable, uh, Assumption, to be honest, that it's not a price hike, it's just a bigger model, so it costs more. But we just don't know that. There was no transparency on that, so we are left to draw our own conclusions on what that means. That's just is what it is. So, [01:20:00] yeah, that would be the sort of Price ELO chart.

[01:20:03] swyx: I would say that the main update for this one, if you go to my LLM pricing chart, which is public, you can ask me for it, or I've shared it online as well. The most recent one is Amazon Nova, which we briefly, briefly talked about on the pod, where, um, they've really sort of come in and, you know, You know, basically offered Amazon basics LLM, uh, where Amazon Pro, Nova Pro, Nova Lite, and Nova Micro are the efficient frontier for, uh, their intelligence levels of 1, 200 to 1, 300.

[01:20:30] swyx: Um, you want to get beyond 1, 300, you have to pay up for the O1s of the world and the 4Os of the world and the Gemini 1. 5 Pros of the world. Um, but, uh, 2Flash is not on here. And it is probably a good deal higher. Flash thinking is not on here, as well as all the other QWQs, R1s, and all the other sort of thinking models.

[01:20:49] swyx: So, I'm going to have to update this chart. It's always a struggle to keep up to date. But I want to give you the idea that basically for, uh, through the month through the, through the [01:21:00] Through 2024 for the same amount of elo, what you used to pay at the start of 2024. Um, you know, let's say, you know, 54, 40 to $50 per million tokens, uh, now is available, uh, approximately at, with Amazon Nova, uh, approximately at, I don't know, 0.075.

[01:21:22] swyx: dollars per token, so like 7. 5 cents. Um, so that is a couple orders of magnitude at least, uh, actually almost three orders of magnitude improvement in a year. And I used to say that intelligence, the cost intelligence was coming down, uh, one order of magnitude per year, like 10x. Um, you know, that is already faster than Moore's law, but coming down three times this year, um, is something that I think not enough people are talking about.

[01:21:50] swyx: And so. Even though people understand that intelligence has become cheaper, I don't think people are appreciating how much more accelerated this year has been. [01:22:00] And obviously I think a lot of people are speculating how much more next year will be with H200s becoming commodity, Blackwell's coming out. We, it's very hard to predict.

[01:22:09] swyx: And obviously there are a lot of factors beyond just the GPUs. So that is the sort of thematic overview.

[01:22:16] Major AI Themes of the Year

[01:22:16] swyx: And then we went into sort of the, the annual overview. This is basically, um, us going through the AI news, uh, releases of the, of, uh, of the year and just picking out favorites. Um, I had Will, our new research assistant, uh, help out with the research, but you can go on to AI News and check out, um, all the, all the sort of top news of the day.

[01:22:41] swyx: Uh, but we had a little bit of an AI Rewind thing, which I'll briefly bridge you in back to the recording that we had.

[01:22:48] AI Rewind: January to March

[01:22:48] swyx: So January, we had the first round of the year for Perfect City. Um, and for me, it was notable that Jeff Bezos backed it. Um, Jeff doesn't invest in a whole lot of companies, but when he does, [01:23:00] um, you know, he backed Google.

[01:23:02] swyx: And now he's backing the new Google, which is kind of cool. Perplexity is now worth 9 billion. I think they have four rounds this year.

[01:23:10] swyx: Will also picked out that Sam was talking about GPT 5 soon. This was back when he was, I think, at one of the sort of summit type things, Davos. And, um, yeah, no GPT 5. It's actually, we got O1 and O3. Thinking about last year's Dev Day, and this is three months on from Dev Day, people were kind of losing confidence in GPTs, and I feel like that hasn't super recovered yet.

[01:23:44] swyx: I hear from people that there are still stuff in the works, and you should not give up on them, and they're actually underrated now. Um, which is good. So, I think people are taking a stab at the problem. I think it's a thing that should exist. And we just need to keep iterating on them. Honestly, [01:24:00] any marketplace is hard.

[01:24:01] swyx: It's very hard to judge, given all the other stuff that you've shipped. Um, chatgtp also released memory in February, which we talked about a little bit. We also had Gemini's diversity drama, which we don't tend to talk a ton about in this podcast because we try to keep it technical. But we also started seeing context window size blow out.

[01:24:22] swyx: So we, this year, I mean, it was, it was Gemini with one million tokens. Um, But also, I think there's two million tokens talked about. We had a podcast with Gradients talking about how to fine tune for one million tokens. It's not just like what you declare to be your token context, but you also have to use it well.

[01:24:40] swyx: And increasingly, I think people are looking at not just Ruler, which is sort of multi needle in a haystack we talked about, but also Muser and like reasoning over long context, not just being able to retrieve over long context. And so that's what I would. Call out there, uh, specifically I think magic. dev as well, made a lot of waves for the 100 [01:25:00] million token model, which was kind of teased last year, but whatever it was, they made some noise about it, um, still not released, so we don't know, but we'll try to get them on, on the podcast.

[01:25:09] swyx: In March, Cloud 3 came out. Which, huge, huge, huge for Enthropic. This basically started to mark the shift of market share that we talked about earlier in the pod, where most production traffic was on OpenAI, and now Enthropic, um, had a decent frontier model family that people could shift to, and obviously now we know that Sonnet is, is kind of the workhorse, um, just like 4.

[01:25:31] swyx: 0 is the workhorse of, of OpenAI. Devon, um, came out in March, and that was a very, very big launch. It was probably one of the most well executed PR campaigns, um, maybe in tech, maybe this decade. Um, and, and then I think, you know, there was a lot of backlash as to, like, what specifically was real in the, in the videos that they launched with.

[01:25:55] swyx: And then they took 9 months to ship to GA, and now you can buy it [01:26:00] for 500 a month and form your own opinion. I think some people are happy, some people less so, but it's very hard to live up to the promises that they made. And the fact that some of them, for some of them, they do, which is interesting. I think the main thing I would caution out for Devon, and I think people call me a Devon show sometimes, because I say nice things, like one nice thing doesn't mean I'm a show.

[01:26:22] swyx: Um, Basically, it is that like a lot of the ideas can be copied and this is the always the threat of Quote unquote GPT wrappers that you achieve product market fit with one feature It's gonna be copied by a hundred other people So, of course you gotta compete with branding and better products and better engineering and all that sort of stuff Which Devin has in spades, so we'll see.

[01:26:42] AI Rewind: April to June

[01:26:42] swyx: April, we actually talked to Yurio and Suno Um, we talked to Suno specifically, but UDL I also got a beta access to, and like, um, AI music generation. We, we played with that on the podcast. I loved it. Some of our friends at the pod like play in their [01:27:00] cars, like I rode in their cars while they played our Suno intro songs and I freaking loved using O1 to craft the lyrics and Suno to, and Yudioh to make the songs.

[01:27:10] swyx: But ultimately, like a lot of people, you know, some people were skipping them. I don't know what, Exact percentages, but those, you know, 10 percent of you that skipped it, you're, you're the reason why we cut the intro songs. Um, we also had Lama 3 released. So, you know, I think people always want to see, uh, you know, like a, a good frontier, uh, open source model.

[01:27:29] swyx: And Lama 3 obviously delivered on that with the 8B and 70B. The 400B came later. Then, um, May, GPC 4. 0 released, um, we, uh, and it was like kind of a model efficiency thing, but also I think just a really good demo of all the, uh, the things that 4. 0 was capable of. Like, this is where the messaging of OmniModel really started kicking in.

[01:27:51] swyx: You know, previously, 4 and 4. 0 Turbo were all text. Um, and not natively, uh, sort of vision. I mean, they had vision, but not [01:28:00] natively voice. And, you know, that, uh, I think everyone was, fell in love immediately with the SkyVoice and SkyVoice got taken away, um, before the public release, and, um, I think it's probably self inflicted.

[01:28:13] swyx: Um, I think that the, the version of events that has Sam Altman basically putting a foot in his mouth with a three letter tweet, you know, Um, causing decent grounds for a lawsuit where there was no grounds to be had because they actually just used a voice actress that sounded like Scarlett Johansson. Um, uh, is unfortunate because we could have had it and we, we don't.

[01:28:36] swyx: So that's what it is and that's what the consensus seems to be from the people I talk to. Uh, people be pining for the Scarlett Johansson voice. In June, Apple announced Apple Intelligence at WWDC. Um, and, um, we haven't, most of us, if you update your phones, have it now if you're on an iPhone. And I would say it's, like, decent.

[01:28:57] swyx: You know, like, I think it wasn't the game [01:29:00] changer thing that caused the Apple stock to rise, like, 20%. And just because everyone was, like, going to upgrade their iPhones just to get Apple Intelligence, it did not become that. But, um, Um, it, it is the, uh, probably the largest scale rollout of transformers yet, um, after Google rolled out BERT for search and, um, and people are using it and it's a 3B, you know, foundation model that's running locally on your phone with Loras that are hot swaps and we have papers for it.

[01:29:29] swyx: Honestly, Apple did a fantastic job of doing the best that they can. They're not the most transparent company in the world and nobody expects them to be, but, um, they gave us. More than I think we normally get for Apple tech, and that's very nice for the research community as well. NVIDIA, I think we continue to talk about, I think I was at the Taiwanese trade show, Comtex, and saw him signing, you know, You know, women body [01:30:00] parts.

[01:30:00] swyx: And I think that was maybe a sign of the times, maybe a sign that things have peaked, but things are clearly not peaked because they continued going. Ilya, and then, and then that bridges us back into the episode recording. I'm going to stop now and stop yapping. But, uh, Yeah, we, you know, we recorded a whole bunch of stuff.

[01:30:18] swyx: We lost it and we're scrambling to re record it for you, but also we're trying to close the chapter on 2024. So, uh, now I'm going to cut back to the recording where we talk about the rest of June, July, August, September, and the second half of 2024 is news. And we'll end the episode there. Ilya came out from the woodwork, raised a billion dollars.

[01:30:45] swyx: Dan Gross seems to have now become full time CEO of the company, which is interesting. I thought he was going to be an investor for life, but now he's operating. He was an investor for a short amount of time. What else can we say about Ilya? I think [01:31:00] this idea that you only ship one product and it's a straight shot at superintelligence seems like a really good focusing mission, but then it runs counter to basically both Tesla and OpenAI in terms of the ship intermediate products that get you to that vision.

[01:31:17] Alessio: OpenAI now needs then more money because they need to support those products and I think maybe their bet is like 1 billion we can get to the thing. Like we don't want to have to have intermediate steps, like we're just making it clear that like this is what

[01:31:30] swyx: it's about. Yeah, but then like where do you get your data?

[01:31:33] swyx: Yeah, totally. Um, so, so I think that's the question. I think we can also use this as part of a general theme of the safety wing of OpenAI leaving. It's fair to say that, you know, Yann Leclerc also left and, like, basically the entire super alignment team left.

[01:31:52] Alessio: Yeah, then there was artifacts, kind of like the Chajupiti canvas equivalent that came out.

[01:31:57] swyx: I think more code oriented. Yeah. [01:32:00] Canvas clone yet, apart from

[01:32:03] swyx: OpenAI.

[01:32:04] swyx: Interestingly, I think the same person responsible for artifacts and canvas, Karina, officially left Anthropic after this to join OpenAI on the rare reverse moves.

[01:32:16] Alessio: In June, I was over 2, 000 people, not including us. I would love to attend the next one. If only we could get

[01:32:25] swyx: tickets. We now have it deployed for everybody. Gemini actually kind of beat them to the GA release, which is kind of interesting. Everyone should basically always have this on. As long as you're comfortable with the privacy settings because then you have a second person looking over your shoulder.

[01:32:43] swyx: And, like, this time next year, I would be willing to bet that I would just have this running on my machine. And, you know, I think that assistance always on, that you can talk to with vision, that sees what you're seeing. I think that is where, uh, At least one hour of software experience to go, then it will be another few years [01:33:00] for that to happen in real life outside of the screen.

[01:33:03] swyx: But for screen experiences, I think it's basically here but not evenly distributed. And you know, we've just seen the GA of this capability that was demoed in June.

[01:33:12] AI Rewind: July to September

[01:33:12] Alessio: And then July was Lama 3. 1, which, you know, we've done a whole podcast on. But that was, that was great. July and August were kind of quiet.

[01:33:19] Alessio: Yeah, structure uploads. We also did a full podcast on that. And then September we got O1. Yes. Strawberry, a. k. a. Qstar, a. k. a. We had a nice party with strawberry glasses. Yes.

[01:33:31] swyx: I think very underrated. Like this is basically from the first internal demo of Q of strawberry was, let's say, November 2023. So between November to September, Like, the whole red teaming and everything.

[01:33:46] swyx: Honestly, a very good ship rate. Like, I don't know if people are giving OpenAI enough credit for, like, this all being available in ChajGBT and then shortly after in API. I think maybe in the same day, I don't know. I don't remember the exact sequence [01:34:00] already. But like, This is like the frontier model that was like rolled out very, very quickly to the whole world.

[01:34:05] swyx: And then we immediately got used to it, immediately said it was s**t because we're still using Sonnet or whatever. But like still very good. And then obviously now we have O1 Pro and O1 Full. I think like in terms of like biggest ships of the year, I think this is it, right?

[01:34:18] Alessio: Yeah. Yeah, totally. Yeah. And I think it now opens a whole new Pandora's box for like the inference time compute and all that.

[01:34:25] Alessio: Yeah.

[01:34:26] swyx: Yeah. It's funny because like it could have been done by anyone else before.

[01:34:29] swyx: Yeah,

[01:34:30] swyx: literally, this is an open secret. They were working on it ever since they hired Gnome. Um, but no one else did.

[01:34:35] swyx: Yeah.

[01:34:36] swyx: Another discovery, I think, um, Ilya actually worked on a previous version called GPT 0 in 2021. Same exact idea.

[01:34:43] swyx: And it failed. Yeah. Whatever that means. Yeah.

[01:34:47] Alessio: Timing. Voice mode also. Voice mode, yeah. I think most people have tried it by now. Because it's generally available. I think your wife also likes it. Yeah, she talks to it all the time. Okay.

[01:34:59] AI Rewind: October to December

[01:34:59] Alessio: [01:35:00] Canvas in October. Another big release. Have you used it much? Not really, honestly.

[01:35:06] swyx: I use it a lot. What do you use it for mostly? Drafting anything. I think that people don't see where all this is heading. Like OpenAI is really competing with Google in everything. Canvas is Google Docs. Canvas is Google Docs. It's a full document editing environment with an auto assister thing at the side that is arguably better than Google Docs, at least for some editing use cases, right?

[01:35:26] swyx: Because it has a much better AI integration than Google Docs. Google Docs with Gemini on the side. And so OpenAI is taking on Google and Google Docs. It's also taking on, taking it on in search. And they, you know, they launched their, their little, uh, Chrome extension thing to, to be the default search. And I think like piece by piece, it's, it's kind of really.

[01:35:44] swyx: Tackling on Google in a very smart way that I think is additive to workflow and people should start using it as intended, because this is a peek into the future. Maybe they're not successful, but at least they're trying. And I think Google has gone without competition for so long that anyone trying will be, [01:36:00] will be, will at least receive some attention from me.

[01:36:03] Alessio: And then yeah, computer use also came out. Um, yeah, that was, yeah, that was a busy, it's been a busy couple months.

[01:36:10] swyx: Busy couple months. I would say that computer use was one of the most upvoted demos on Hacker News of the year. But then comparatively, I don't see people using it as much. This is how you feel the difference between a mature capability and an emerging capability.

[01:36:25] swyx: Maybe this is why Vision is emerging. Because I launched computer use, you're not using it today. But you use everything else in the mature category. And it's mostly because it's not precise enough, or it's too slow, or it's too expensive. And those would be the main criticisms.

[01:36:39] Alessio: Yeah, that makes sense. It's also just like overall uneasiness about just letting it go crazy on your computer.

[01:36:46] Alessio: Yeah, no, no, totally. But I think a lot of people do. November. R1, so that was kind of like the open source, so one

[01:36:52] swyx: competitor. This was a surprise. Yeah, nobody knew it was coming. Yeah. Everyone knew, like, F1 we had a preview at the Fireworks HQ, and then [01:37:00] I think some other labs did it, but I think R1 and QWQ, Quill, from the Quent team, Both Alibaba affiliated, I think, are the leading contenders on that front end.

[01:37:12] swyx: We'll see. We'll see.

[01:37:14] Alessio: What else to highlight? I think the Stripe agent toolkit. It's a small thing, but it's just like people are like agents are not real. It's like when you have, you know, companies like Stripe and like start to build things to support it. It might not be real today, but obviously. They don't have to do it because they don't, they're not an AI company, but the fact that they do it shows that there's one demand and so there's belief

[01:37:35] swyx: on their end.

[01:37:35] swyx: This is a broader thing about, a broader thesis for me that I'm exploring around, do we need special SDKs for agents? Why can't normal SDKs for humans do the same thing? Stripe agent toolkits happens to be a wrapper on the Stripe SDK. It's fine. It's just like a nice little DX layer. But like, it's still unclear to me.

[01:37:53] swyx: Uh, I think, um, I have been asked my opinion on this before, and I said, I think I said it on a podcast, which is like, the main layer that you need is [01:38:00] the separate off roles, so that you don't assume it's a human, um, doing these things. And you can lock things down much quicker. You can identify whether it is an agent acting on your behalf or actually you.

[01:38:12] Alessio: Do.

[01:38:12] swyx: Um, and that, that is something that you need. Um, I had my 11 labs key pwned because I lost my laptop and, uh, I saw a whole bunch of API calls and I was like, Oh, is that me? Or is that, is that someone? And it turned out to be a key that had that committed, uh, onto GitHub and that didn't scrape. And so sourcing of where API usage is coming from, I think, um, you know, you should attribute it to agents and build for that world.

[01:38:36] swyx: But other than that, I think SDKs, I would see it as a failure of Dev tech and AI that we need every single thing needs to be reinvented for agents.

[01:38:48] Alessio: I agree in some ways. I think in other ways we've also like not always made things super explicit. There's kind of like a lot of defaults that people do when they design APIs but like Um, I think if you were to [01:39:00] redesign them in a world in which the person or the agent using them as like all the most infinite memory and context, like you will maybe do things differently, but I don't know.

[01:39:09] Alessio: I think to me that the most interesting is like rest and GraphQL is almost more interesting in the world of agents because agents could come up with so many different things to query versus like before I always thought GraphQL was kind of like not really necessary because like, you know what you need, just build the rest end point for it.

[01:39:24] Alessio: So, yeah, I'm curious to see what else. Changes. And then they had the search wars. I think that was, you know, search GPD perplexity, Dropbox, Dropbox dash. Yeah, we had Drew on the pod and then we added the Pioneer Summit. The fact that Dropbox has a Google Drive integration, it's just like if you told somebody five years ago, it's like,

[01:39:44] swyx: oh,

[01:39:44] Alessio: Dropbox doesn't really care about your files.

[01:39:47] Alessio: You know, it's like that doesn't compute. So, yeah, I'm curious to see where. And that

[01:39:53] Year-End Reflections and Predictions

[01:39:53] swyx: brings us up to December, still developing, I'm curious what the last day of OpenAI shipments will be, I think everyone [01:40:00] is expecting something big there. I think so far it has been a very eventful year, definitely has grown a lot, we were asked by Will actually whether we made predictions, I don't think we did, but Not really, I

[01:40:11] Alessio: think we definitely talked about agents.

[01:40:14] Alessio: Yes. And I don't know if we said it was the year of the agents, but we said next

[01:40:19] swyx: year

[01:40:19] Alessio: is the year. No, no, but well, you know, the anatomy of autonomy that was April 2023, you know, so obviously there's been belief for a while. But I think now the models are, I would say maybe the last, yeah. Two months. I made a big push in like capability for like 3.

[01:40:35] Alessio: 6, 4. 1.

[01:40:36] swyx: Ilya saying the word agentic on stage at Eurips, it's a big deal. Satya, I think also saying that a lot these days. I mean, Sam has been saying that for a while now. So DeepMind, when they announced Gemini 2. 0, they announced Deep Research, but also Project Mariner, which is a browser agent, which is their computer use type thing, as well as Jules, which is their code agent.

[01:40:56] swyx: And I think. That basically complements with whatever OpenAI is shipping [01:41:00] next year, which is codename operator, which is their agent thing. It makes sense that if it actually replaces a junior employee, they will charge 2, 000 for it.

[01:41:09] Alessio: Yeah, I think that's my whole, I did this post, it's pinned on my Twitter, so you can find it easily, but about skill floor and skill ceiling in jobs.

[01:41:17] Alessio: And I think the skill floor more and more, I think 2025 will be the first year where the AI sets the skill floor. Overall, you know, I don't think that has been true in the past, but yeah, I think now really, like, you know, if Devon works, if all these customer support agents are working. So now to be a customer support person, you need to be better than an agent because the economics just don't work.

[01:41:38] Alessio: I think the same is going to happen to in software engineering, which I think the skill floor is very low. You know, like there's a lot of people doing software engineering that are really not that good. So I'm curious to see it. And the next year of the recap, what other jobs are going to have that change?

[01:41:52] swyx: Yeah. Every NeurIPS that I go, I have some chats with researchers and I'll just highlight the best prediction from that group. And then we'll move on [01:42:00] to end of year recap in terms of, we'll just go down the list of top five podcasts and then we'll end it. So the best prediction was that there will be a foreign spy caught at one of the major labs.

[01:42:14] swyx: So this is part of the consciousness already that, uh, you know, like, you know, whenever you see someone who is like too attractive in a San Francisco party, where it's like the ratio is like 100 guys to one girl, and like suddenly the girl is like super interested in you, like, you know, it may not be your looks.

[01:42:29] swyx: Um, so, um, There's a lot of like state level secrets that are kept in these labs and not that much security. I think if anything, the situational awareness essay did to raise awareness of it, I think it was directionally correct, even if not precisely correct. We should start caring a lot about this.

[01:42:45] swyx: OpenAI has hired a CISO this year. And I think like the security space in general. Oh, I remember what I was going to say about Apple Foundation Model before we cut for a break. They announced Apple Secure Cloud, Cloud Compute. And I think, um, We are also interested in investing in areas [01:43:00] that are basically secure cloud LLM inference for everybody.

[01:43:03] swyx: I think like what we have today is not secure enough because it's like normal security when like this is literally a state level interest.

[01:43:10] Alessio: Agreed. Top episodes? Yeah. So I'm just going through the sub stack. Number one, the David one. That's the most popular 2024. Why Google failed to make GPT 3?

[01:43:21] swyx: I will take a little bit of credit for the naming of that one because I think that was the Hacker News thing.

[01:43:26] swyx: It's very funny because, like, actually, obviously he wants to talk about Adept, but then he spent half the episode talking about his time at OpenAI. But I think it was a very useful insight that I'm still using today. Even in, like, the earlier post, I was still referring to what he said. And when we do podcast episodes, I try to look for that.

[01:43:42] swyx: I try to look for things that we'll still be referencing in the future. And that concentrated badness, David talked about the Brain Compute Marketplace, and then Ilya in his emails that I covered in the What Ilya Saw essay, had the opening eyesight of this, where they were like, [01:44:00] One big training run is much, much more valuable than the hundred equivalent small training runs.

[01:44:05] swyx: So we need to go big. And we need to concentrate better, not spread them.

[01:44:08] Alessio: Number two, how notebook. clan was made. Yeah, um, that was fun. Yeah, and everybody, I mean, I think that's like a great example of like, Just timeliness. You know, I think it was top of mind for everybody. There were great guests. Um, it just made the rounds on social media.

[01:44:24] swyx: Yeah. Um, and that one, I would say Risa is obviously a star, but she's been on every episode, every podcast, but Isamah, I think, you know, actually being the guy who worked on the audio model, being able to talk to him, I think was, was a great gift for us. And I think people should listen back to how they trained the model.

[01:44:41] swyx: Cause I think you put that level of attention on any model. You will make it SOTA. Yeah, that's true. And it's specifically like, uh, they didn't have evals. They just, they had vibes. They had a group session with vibes.

[01:44:55] Alessio: The ultimate got to prompting. Yeah, that was number three. I think all these episodes that are like [01:45:00] summarizing things that people care about, but they're disparate.

[01:45:03] Alessio: I think always do very well. This helps us

[01:45:05] swyx: save on a lot of smaller prompting episodes, right? Yeah. If we interviewed individual paper authors with like a 10 page paper that is just a different prompt, like not as useful as like an overview survey thing. Yeah, I think. The question is what to do from here.

[01:45:19] swyx: People have actually, I would, I would say I've been surprised by how well received that was. Should we do ultimate guide to other things? And then should we do prompting 201? Right? Those are the two lessons that we can learn from the success of this one. I think

[01:45:32] Alessio: if somebody does the work for us, that was the good thing about Sander.

[01:45:35] Alessio: Like he had done all the work for us. Yeah, Sander is very, very

[01:45:38] swyx: fastidious about this. So he did a lot of work on that. And you know, I'm definitely keen to have him on next year to talk more prompting. Okay, then the next one is the not safe for work one. Okay.

[01:45:48] Alessio: No.

[01:45:48] swyx: Or structured outputs. The next one is brain trust.

[01:45:52] swyx: Really? Yeah. Okay. We have a different list then. But yeah.

[01:45:55] Alessio: I'm just going on the sub

[01:45:57] swyx: stack. I see. I see. So that includes the number of [01:46:00] likes, but, uh, I was, I was going by downloads. Hmm. It's

[01:46:03] Alessio: fine. I would say this is almost recency bias in the way that like the audience keeps growing and then like the most recent episodes get more views.

[01:46:12] Alessio: I see. So I would say definitely like the. NSFW1 was very popular, what people were telling me they really liked, because it was something people don't cover. Um, yeah, structural outputs, I think people like that one. I mean, the same one, yeah, I think that's like something I refer to all the time. I think that's one of the most interesting areas for the new year.

[01:46:34] Alessio: the simulation. Oh, WebSim, Wolsim, really? Yeah, not that use case. But like, how do you use that for like model training and like agents learning and all of that?

[01:46:44] swyx: Yeah, so I would definitely point to our newest 7 hour long episode on Simulative Environments because it is the, let's say the scaled up, very serious AGI lab version of WebSim and MobileSim.

[01:46:58] swyx: If you take it very, very [01:47:00] seriously, you get Genie 2, which is exactly what you need to then build Sora and everything else. Um, so yeah, I think, uh, Simulative AI, still in summer. Still in summer. Still, still coming. And I was actually reflecting on this, like, would you, would you say that the AI winter has, like, coming on?

[01:47:15] swyx: Or, like, was it never even here? Because we did AI Winter episode, and I, you know, I was, like, trying to look for signs. I think that's kind of gone now.

[01:47:23] Alessio: Yeah. I would say. It was here in the vibes, but not really in the reality. You know, when you look back at the yearly recap, it's like every month there was like progress.

[01:47:32] Alessio: There wasn't really a winter. There was maybe like a hype winter, but I don't know if that counts as a real winter. I

[01:47:38] swyx: think the scaling has hit a wall thing has been a big driving discussion for 2024.

[01:47:43] swyx: Yeah.

[01:47:43] swyx: And, you know, with some amount of conclusion on, in Europe's that we were also kind of pointing to in the winter episode, but like, it's not a winter by any means.

[01:47:54] swyx: Yeah, we know what winter feels like. It is not winter. So I think things are, things are going well. [01:48:00] I think every time that people think that there's like, Not much happening in AI, just think back to this time last year,

[01:48:05] swyx: right?

[01:48:06] swyx: And understand how much has changed from benchmarks to frontier models to market share between OpenAI and the rest.

[01:48:11] swyx: And then also cover like, you know, the, the various coverage areas that we've marked out, how the discussion has, has evolved a lot and what we take for granted now versus what we did not have a year ago.

[01:48:21] Alessio: Yeah. And then just to like throw that out there, there've been 133 funding rounds, over a hundred million in AI.

[01:48:28] Alessio: This year.

[01:48:29] swyx: Does that include Databricks, the largest venture around in

[01:48:31] Alessio: history? 10 billion dollars. Sheesh. Well, that Mosaic now has been bought for two something billion because it was mostly stock, you know, so price goes up. I see. Theoretically. I see. So you just bought at a valuation

[01:48:46] swyx: of 40, right? Yeah. It was like 43 or something like that.

[01:48:49] swyx: At the time, I remember at the time there was a question about whether or not the evaluation was real.

[01:48:53] Alessio: Yeah, well, that's why everybody

[01:48:55] swyx: was down. And like Databricks was a private valuation that was like two years old. [01:49:00] It's like, who knows what this thing's worth. Now it's worth 60 billion.

[01:49:03] Alessio: It's worth more.

[01:49:03] Alessio: That's what it's worth. It's worth more than what you thought. Yeah, it's been a crazy year, but I'm excited for next year. I feel like this is almost like, you know, Now the agent thing needs to happen. And I think that's really the unlock.

[01:49:16] swyx: I have to agree with you. Next year is the year of the agent in production.

[01:49:21] swyx: Yeah.

[01:49:23] Alessio: It's almost like, I'm not 100 percent sure it will happen, but it needs to happen. Otherwise, it's definitely the winter next year. Any other questions? Parting, thoughts.

[01:49:33] swyx: I'm very grateful for you. Uh, I think that, I think you've been, uh, the, the, a dream partner to, to build Lanespace with. And, uh, and also the Discord community, the paper club people have been beyond my wildest dreams, like, uh, so supportive and, and successful.

[01:49:47] swyx: Like, it's amazing that, you know, the, the community has, you know, grown so much and like the, the vibe has not changed.

[01:49:53] Alessio: Yeah. Yeah, that's true. We're almost at 5, 000 people.

[01:49:56] swyx: Yeah, we started this discord like four years ago. And still, like, people [01:50:00] get it when they join. Like, you post news here, and then you discuss it in threads.

[01:50:03] swyx: And, you know, you try not to self promote too much. And mostly people obey the rules. And sometimes you smack them down a little bit, but that's okay.

[01:50:11] Alessio: We rarely have to ban people, which is great. But yeah, man, it's been awesome, man. I think we both started not knowing where this was going to go. And now we've done 100 episodes.

[01:50:21] Alessio: It's easy to see how we're going to get to 200. I think maybe when we started, it wasn't easy to see how we would get to 100, you know. Yeah, excited for more. Subscribe on YouTube, because we're doing so much work to make that work. It's very expensive

[01:50:35] swyx: for an unclear payoff as to like what we're actually going to get out of it.

[01:50:39] swyx: But hopefully people discover us more there. I do believe in YouTube as a podcasting platform much more so than Spotify.

[01:50:46] Alessio: Yeah,

[01:50:47] swyx: totally.

[01:50:48] Alessio: Thank you all for listening. See you in the new year.

[01:50:51] swyx: Bye [01:51:00] bye.

Happy holidays! We’ll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all all our LS supporters who helped fund the gorgeous venue and A/V production!

For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver.

Our next keynote covers The State of LLM Agents, with the triumphant return of Professor Graham Neubig’s return to the pod (his ICLR episode here!). OpenDevin is now a startup known as AllHands! The renamed OpenHands has done extremely well this year, as they end the year sitting comfortably at number 1 on the hardest SWE-Bench Full leaderboard at 29%, though on the smaller SWE-Bench Verified, they are at 53%, behind Amazon Q, devlo, and OpenAI's self reported o3 results at 71.7%.

Many are saying that 2025 is going to be the year of agents, with OpenAI, DeepMind and Anthropic setting their sights on consumer and coding agents, vision based computer-using agents and multi agent systems. There has been so much progress on the practical reliability and applications of agents in all domains, from the huge launch of Cognition AI's Devin this year, to the sleeper hit of Cursor Composer and Codeium's Windsurf Cascade in the IDE arena, to the explosive revenue growth of Stackblitz's Bolt, Lovable, and Vercel's v0, and the unicorn rounds and high profile movements of customer support agents like Sierra (now worth $4 billion) and search agents like Perplexity (now worth $9 billion). We wanted to take a little step back to understand the most notable papers of the year in Agents, and Graham indulged with his list of 8 perennial problems in building agents in 2024.

Must-Read Papers for the 8 Problems of Agents

* The agent-computer interface: CodeAct: Executable Code Actions Elicit Better LLM Agents. Minimial viable tools: Execution Sandbox, File Editor, Web Browsing

* The human-agent interface: Chat UI, GitHub Plugin, Remote runtime, …?

* Choosing an LLM: See Evaluation of LLMs as Coding Agents on SWE-Bench at 30x - must understand instructions, tools, code, environment, error recovery

* Planning: Single Agent Systems vs Multi Agent (CoAct: A Global-Local Hierarchy for Autonomous Agent Collaboration) - Explicit vs Implicit, Curated vs Generated

* Reusable common workflows: SteP: Stacked LLM Policies for Web Actions and Agent Workflow Memory - Manual prompting vs Learning from Experience

* Exploration: Agentless: Demystifying LLM-based Software Engineering Agents and BAGEL: Bootstrapping Agents by Guiding Exploration with Language

* Search: Tree Search for Language Model Agents - explore paths and rewind

* Evaluation: Fast Sanity Checks (miniWoB and Aider) and Highly Realistic (WebArena, SWE-Bench) and SWE-Gym: An Open Environment for Training Software Engineering Agents & Verifiers

Full Talk on YouTube

Please like and subscribe!

Timestamps

* 00:00 Welcome to Latent Space Live at NeurIPS 2024

* 00:29 State of LLM Agents in 2024

* 02:20 Professor Graham Newbig's Insights on Agents

* 03:57 Live Demo: Coding Agents in Action

* 08:20 Designing Effective Agents

* 14:13 Choosing the Right Language Model for Agents

* 16:24 Planning and Workflow for Agents

* 22:21 Evaluation and Future Predictions for Agents

* 25:31 Future of Agent Development

* 25:56 Human-Agent Interaction Challenges

* 26:48 Expanding Agent Use Beyond Programming

* 27:25 Redesigning Systems for Agent Efficiency

* 28:03 Accelerating Progress with Agent Technology

* 28:28 Call to Action for Open Source Contributions

* 30:36 Q&A: Agent Performance and Benchmarks

* 33:23 Q&A: Web Agents and Interaction Methods

* 37:16 Q&A: Agent Architectures and Improvements

* 43:09 Q&A: Self-Improving Agents and Authentication

* 47:31 Live Demonstration and Closing Remarks

Transcript

[00:00:29] State of LLM Agents in 2024

[00:00:29] Speaker 9: Our next keynote covers the state of LLM agents. With the triumphant return of Professor Graham Newbig of CMU and OpenDevon, now a startup known as AllHands. The renamed OpenHands has done extremely well this year, as they end the year sitting comfortably at number one on the hardest SWE Benchful leaderboard at 29%.

[00:00:53] Speaker 9: Though, on the smaller SWE bench verified, they are at 53 percent behind Amazon Q [00:01:00] Devlo and OpenAI's self reported O3 results at 71. 7%. Many are saying that 2025 is going to be the year of agents, with OpenAI, DeepMind, and Anthropic setting their sights on consumer and coding agents. Vision based computer using agents and multi agent systems.

[00:01:22] Speaker 9: There has been so much progress on the practical reliability and applications of agents in all domains, from the huge launch of Cognition AI's Devon this year, to the sleeper hit of Cursor Composer and recent guest Codium's Windsurf Cascade in the IDE arena. To the explosive revenue growth of recent guests StackBlitz's Bolt, Lovable, and Vercel's vZero.

[00:01:44] Speaker 9: And the unicorn rounds and high profile movements of customer support agents like Sierra, now worth 4 billion, and search agents like Perplexity, now worth 9 billion. We wanted to take a little step back to understand the most notable papers of the year in [00:02:00] agents, and Graham indulged with his list of eight perennial problems in building agents.

[00:02:06] Speaker 9: As always, don't forget to check our show notes for all the selected best papers of 2024, and for the YouTube link to their talk. Graham's slides were especially popular online, and we are honoured to have him. Watch out and take care!

[00:02:20] Professor Graham Newbig's Insights on Agents

[00:02:20] Speaker: Okay hi everyone. So I was given the task of talking about agents in 2024, and this is An impossible task because there are so many agents, so many agents in 2024. So this is going to be strongly covered by like my personal experience and what I think is interesting and important, but I think it's an important topic.

[00:02:41] Speaker: So let's go ahead. So the first thing I'd like to think about is let's say I gave you you know, a highly competent human, some tools. Let's say I gave you a web browser and a terminal or a file system. And the ability to [00:03:00] edit text or code. What could you do with that? Everything. Yeah.

[00:03:07] Speaker: Probably a lot of things. This is like 99 percent of my, you know, daily daily life, I guess. When I'm, when I'm working. So, I think this is a pretty powerful tool set, and I am trying to do, and what I think some other people are trying to do, is come up with agents that are able to, you know, manipulate these things.

[00:03:26] Speaker: Web browsing, coding, running code in successful ways. So there was a little bit about my profile. I'm a professor at CMU, chief scientist at All Hands AI, building open source coding agents. I'm maintainer of OpenHands, which is an open source coding agent framework. And I'm also a software developer and I, I like doing lots of coding and, and, you know, shipping new features and stuff like this.

[00:03:51] Speaker: So building agents that help me to do this, you know, is kind of an interesting thing, very close to me.

[00:03:57] Live Demo: Coding Agents in Action

[00:03:57] Speaker: So the first thing I'd like to do is I'd like to try [00:04:00] some things that I haven't actually tried before. If anybody has, you know, tried to give a live demo, you know, this is, you know very, very scary whenever you do it and it might not work.

[00:04:09] Speaker: So it might not work this time either. But I want to show you like three things that I typically do with coding agents in my everyday work. I use coding agents maybe five to 10 times a day to help me solve my own problems. And so this is a first one. This is a data science task. Which says I want to create scatter plots that show the increase of the SWE bench score over time.

[00:04:34] Speaker: And so I, I wrote a kind of concrete prompt about this. Agents work better with like somewhat concrete prompts. And I'm gonna throw this into open hands and let it work. And I'll, I'll go back to that in a second. Another thing that I do is I create new software. And I, I've been using a [00:05:00] service a particular service.

[00:05:01] Speaker: I won't name it for sending emails and I'm not very happy with it. So I want to switch over to this new service called resend. com, which makes it easier to send emails. And so I'm going to ask it to read the docs for the resend. com API and come up with a script that allows me to send emails. The input to the script should be a CSV file and the subject and body should be provided in Jinja2 templates.

[00:05:24] Speaker: So I'll start another agent and and try to get it to do that for me.

[00:05:35] Speaker: And let's go with the last one. The last one I do is. This is improving existing software and in order, you know, once you write software, you usually don't throw it away. You go in and, like, actually improve it iteratively. This software that I have is something I created without writing any code.

[00:05:52] Speaker: It's basically software to monitor how much our our agents are contributing to the OpenHance repository. [00:06:00] And on the, let me make that a little bit bigger, on the left side, I have the number of issues where it like sent a pull request. I have the number of issues where it like sent a pull request, whether it was merged in purple, closed in red, or is still open in green. And so these are like, you know, it's helping us monitor, but one thing it doesn't tell me is the total number. And I kind of want that feature added to this software.

[00:06:33] Speaker: So I'm going to try to add that too. So. I'll take this, I'll take this prompt,

[00:06:46] Speaker: and here I want to open up specifically that GitHub repo. So I'll open up that repo and paste in the prompt asking it. I asked it to make a pie chart for each of these and give me the total over the entire time period that I'm [00:07:00] monitoring. So we'll do that. And so now I have let's see, I have some agents.

[00:07:05] Speaker: Oh, this one already finished. Let's see. So this one already finished. You can see it finished analyzing the Swebench repository. It wrote a demonstration of, yeah, I'm trying to do that now, actually.

[00:07:30] Speaker: It wrote a demonstration of how much each of the systems have improved over time. And I asked it to label the top three for each of the data sets. And so it labeled OpenHands as being the best one for SWE Bench Normal. For SWE Bench Verified, it has like the Amazon QAgent and OpenHands. For the SWE Bench Lite, it has three here over three over here.

[00:07:53] Speaker: So you can see like. That's pretty useful, right? If you're a researcher, you do data analysis all the time. I did it while I was talking to all [00:08:00] of you and making a presentation. So that's, that's pretty nice. I, I doubt the other two are finished yet. That would be impressive if the, yeah. So I think they're still working.

[00:08:09] Speaker: So maybe we'll get back to them at the end of the presentation. But so these are the kinds of the, these are the kinds of things that I do every day with coding agents now. And it's or software development agents. It's pretty impressive.

[00:08:20] Designing Effective Agents

[00:08:20] Speaker: The next thing I'd like to talk about a little bit is things I worry about when designing agents.

[00:08:24] Speaker: So we're designing agents to, you know, do a very difficult task of like navigating websites writing code, other things like this. And within 2024, there's been like a huge improvement in the methodology that we use to do this. But there's a bunch of things we think about. There's a bunch of interesting papers, and I'd like to introduce a few of them.

[00:08:46] Speaker: So the first thing I worry about is the agent computer interface. Like, how do we get an agent to interact with computers? And, How do we provide agents with the tools to do the job? And [00:09:00] within OpenHands we are doing the thing on the right, but there's also a lot of agents that do the thing on the left.

[00:09:05] Speaker: So the thing on the left is you give like agents kind of granular tools. You give them tools like or let's say your instruction is I want to determine the most cost effective country to purchase the smartphone model, Kodak one the countries to consider are the USA, Japan, Germany, and India. And you have a bunch of available APIs.

[00:09:26] Speaker: And. So what you do for some agents is you provide them all of these tools APIs as tools that they can call. And so in this particular case in order to solve this problem, you'd have to make about like 30 tool calls, right? You'd have to call lookup rates for Germany, you'd have to look it up for the US, Japan, and India.

[00:09:44] Speaker: That's four tool goals. And then you go through and do all of these things separately. And the method that we adopt in OpenHands instead is we provide these tools, but we provide them by just giving a coding agent, the ability to call [00:10:00] arbitrary Python code. And. In the arbitrary Python code, it can call these tools.

[00:10:05] Speaker: We expose these tools as APIs that the model can call. And what that allows us to do is instead of writing 20 tool calls, making 20 LLM calls, you write a program that runs all of these all at once, and it gets the result. And of course it can execute that program. It can, you know, make a mistake. It can get errors back and fix things.

[00:10:23] Speaker: But that makes our job a lot easier. And this has been really like instrumental to our success, I think. Another part of this is what tools does the agent need? And I, I think this depends on your use case, we're kind of extreme and we're only giving the agent five tools or maybe six tools.

[00:10:40] Speaker: And what, what are they? The first one is program execution. So it can execute bash programs, and it can execute Jupyter notebooks. It can execute cells in Jupyter notebooks. So that, those are two tools. Another one is a file editing tool. And the file editing tool allows you to browse parts of files.[00:11:00]

[00:11:00] Speaker: And kind of read them, overwrite them, other stuff like this. And then we have another global search and replace tool. So it's actually two tools for file editing. And then a final one is web browsing, web browsing. I'm kind of cheating when I call it only one tool. You actually have like scroll and text input and click and other stuff like that.

[00:11:18] Speaker: But these are basically the only things we allow the agent to do. What, then the question is, like, what if we wanted to allow it to do something else? And the answer is, well, you know, human programmers already have a bunch of things that they use. They have the requests PyPy library, they have the PDF to text PyPy library, they have, like, all these other libraries in the Python ecosystem that they could use.

[00:11:41] Speaker: And so if we provide a coding agent with all these libraries, it can do things like data visualization and other stuff that I just showed you. So it can also get clone repositories and, and other things like this. The agents are super good at using the GitHub API also. So they can do, you know, things on GitHub, like finding all of the, you know, [00:12:00] comments on your issues or checking GitHub actions and stuff.

[00:12:02] Speaker: The second thing I think about is the human agent interface. So this is like how do we get humans to interact with agents? Bye. I already showed you one variety of our human agent interface. It's basically a chat window where you can browse through the agent's results and things like this. This is very, very difficult.

[00:12:18] Speaker: I, I don't think anybody has a good answer to this, and I don't think we have a good answer to this, but the, the guiding principles that I'm trying to follow are we want to present enough info to the user. So we want to present them with, you know, what the agent is doing in the form of a kind of.

[00:12:36] Speaker: English descriptions. So you can see here you can see here every time it takes an action, it says like, I will help you create a script for sending emails. When it runs a bash command. Sorry, that's a little small. When it runs a bash command, it will say ran a bash command. It won't actually show you the whole bash command or the whole Jupyter notebook because it can be really large, but you can open it up and see if you [00:13:00] want to, by clicking on this.

[00:13:01] Speaker: So like if you want to explore more, you can click over to the Jupyter notebook and see what's displayed in the Jupyter notebook. And you get like lots and lots of information. So that's one thing.

[00:13:16] Speaker: Another thing is go where the user is. So like if the user's already interacting in a particular setting then I'd like to, you know, integrate into that setting, but only to a point. So at OpenHands, we have a chat UI for interaction. We have a GitHub plugin for tagging and resolving issues. So basically what you do is you Do at open hands agent and the open hands agent will like see that comment and be able to go in and fix things.

[00:13:42] Speaker: So if you say at open hands agent tests are failing on this PR, please fix the tests. It will go in and fix the test for you and stuff like this. Another thing we have is a remote runtime for launching headless jobs. So if you want to launch like a fleet of agents to solve, you know five different problems at once, you can also do [00:14:00] that through an API.

[00:14:00] Speaker: So we have we have these interfaces and this probably depends on the use case. So like, depending if you're a coding agent, you want to do things one way. If you're a like insurance auditing agent, you'll want to do things other ways, obviously.

[00:14:13] Choosing the Right Language Model for Agents

[00:14:13] Speaker: Another thing I think about a lot is choosing a language model.

[00:14:16] Speaker: And for agentic LMs we have to have a bunch of things work really well. The first thing is really, really good instruction following ability. And if you have really good instruction following ability, it opens up like a ton of possible applications for you. Tool use and coding ability. So if you provide tools, it needs to be able to use them well.

[00:14:38] Speaker: Environment understanding. So it needs, like, if you're building a web agent, it needs to be able to understand web pages either through vision or through text. And error awareness and recovery ability. So, if it makes a mistake, it needs to be able to, you know, figure out why it made a mistake, come up with alternative strategies, and other things like this.

[00:14:58] Speaker: [00:15:00] Under the hood, in all of the demos that I did now Cloud, we're using Cloud. Cloud has all of these abilities very good, not perfect, but very good. Most others don't have these abilities quite as much. So like GPT 4. 0 doesn't have very good error recovery ability. And so because of this, it will go into loops and do the same thing over and over and over again.

[00:15:22] Speaker: Whereas Claude does not do this. Claude, if you, if you use the agents enough, you get used to their kind of like personality. And Claude says, Hmm, let me try a different approach a lot. So, you know, obviously it's been trained in some way to, you know, elicit this ability. We did an evaluation. This is old.

[00:15:40] Speaker: And we need to update this basically, but we evaluated CLOD, mini LLAMA 405B, DeepSeq 2. 5 on being a good code agent within our framework. And CLOD was kind of head and shoulders above the rest. GPT 40 was kind of okay. The best open source model was LLAMA [00:16:00] 3. 1 405B. This needs to be updated because this is like a few months old by now and, you know, things are moving really, really fast.

[00:16:05] Speaker: But I still am under the impression that Claude is the best. The other closed models are, you know, not quite as good. And then the open models are a little bit behind that. Grok, I, we haven't tried Grok at all, actually. So, it's a good question. If you want to try it I'd be happy to help.

[00:16:24] Speaker: Cool.

[00:16:24] Planning and Workflow for Agents

[00:16:24] Speaker: Another thing is planning. And so there's a few considerations for planning. The first one is whether you have a curated plan or you have it generated on the fly. And so for solving GitHub issues, you can kind of have an overall plan. Like the plan is first reproduce. If there's an issue, first write tests to reproduce the issue or to demonstrate the issue.

[00:16:50] Speaker: After that, run the tests and make sure they fail. Then go in and fix the tests. Run the tests again to make sure they pass and then you're done. So that's like a pretty good workflow [00:17:00] for like solving coding issues. And you could curate that ahead of time. Another option is to let the language model basically generate its own plan.

[00:17:10] Speaker: And both of these are perfectly valid. Another one is explicit structure versus implicit structure. So let's say you generate a plan. If you have explicit structure, you could like write a multi agent system, and the multi agent system would have your reproducer agent, and then it would have your your bug your test writer agent, and your bug fixer agent, and lots of different agents, and you would explicitly write this all out in code, and then then use it that way.

[00:17:38] Speaker: On the other hand, you could just provide a prompt that says, please do all of these things in order. So in OpenHands, we do very light planning. We have a single prompt. We don't have any multi agent systems. But we do provide, like, instructions about, like, what to do first, what to do next, and other things like this.

[00:17:56] Speaker: I'm not against doing it the other way. But I laid [00:18:00] out some kind of justification for this in this blog called Don't Sleep on Single Agent Systems. And the basic idea behind this is if you have a really, really good instruction following agent it will follow the instructions as long as things are working according to your plan.

[00:18:14] Speaker: But let's say you need to deviate from your plan, you still have the flexibility to do this. And if you do explicit structure through a multi agent system, it becomes a lot harder to do that. Like, you get stuck when things deviate from your plan. There's also some other examples, and I wanted to introduce a few papers.

[00:18:30] Speaker: So one paper I liked recently is this paper called CoAct where you generate plans and then go in and fix them. And so the basic idea is like, if you need to deviate from your plan, you can You know, figure out that your plan was not working and go back and deviate from it.

[00:18:49] Speaker: Another thing I think about a lot is specifying common workflows. So we're trying to tackle a software development and I already showed like three use cases where we do [00:19:00] software development and when we. We do software development, we do a ton of different things, but we do them over and over and over again.

[00:19:08] Speaker: So just to give an example we fix GitHub actions when GitHub actions are failing. And we do that over and over and over again. That's not the number one thing that software engineers do, but it's a, you know, high up on the list. So how can we get a list of all of, like, the workflows that people are working on?

[00:19:26] Speaker: And there's a few research works that people have done in this direction. One example is manual prompting. So there's this nice paper called STEP that got state of the art on the WebArena Web Navigation Benchmark where they came up with a bunch of manual workflows for solving different web navigation tasks.

[00:19:43] Speaker: And we also have a paper recently called Agent Workflow Memory where the basic idea behind this is we want to create self improving agents that learn from their past successes. And the way it works is is we have a memory that has an example of lots of the previous [00:20:00] workflows that people have used. And every time the agent finishes a task and it self judges that it did a good job at that task, you take that task, you break it down into individual workflows included in that, and then you put it back in the prompt for the agent to work next time.

[00:20:16] Speaker: And this we demonstrated that this leads to a 22. 5 percent increase on WebArena after 40 examples. So that's a pretty, you know, huge increase by kind of self learning and self improvement.

[00:20:31] Speaker: Another thing is exploration. Oops. And one thing I think about is like, how can agents learn more about their environment before acting? And I work on coding and web agents, and there's, you know, a few good examples of this in, in both areas. Within coding, I view this as like repository understanding, understanding the code base that you're dealing with.

[00:20:55] Speaker: And there's an example of this, or a couple examples of this, one example being AgentList. [00:21:00] Where they basically create a map of the repo and based on the map of the repo, they feed that into the agent so the agent can then navigate the repo and and better know where things are. And for web agents there's an example of a paper called Bagel, and basically what they do is they have the agent just do random tasks on a website, explore the website, better understand the structure of the website, and then after that they they feed that in as part of the product.

[00:21:27] Speaker: Part seven is search. Right now in open hands, we just let the agent go on a linear search path. So it's just solving the problem once. We're using a good agent that can kind of like recover from errors and try alternative things when things are not working properly, but still we only have a linear search path.

[00:21:45] Speaker: But there's also some nice work in 2024 that is about exploring multiple paths. So one example of this is there's a paper called Tree Search for Language Agents. And they basically expand multiple paths check whether the paths are going well, [00:22:00] and if they aren't going well, you rewind back. And on the web, this is kind of tricky, because, like, how do you rewind when you accidentally ordered something you don't want on Amazon?

[00:22:09] Speaker: It's kind of, you know, not, not the easiest thing to do. For code, it's a little bit easier, because you can just revert any changes that you made. But I, I think that's an interesting topic, too.

[00:22:21] Evaluation and Future Predictions for Agents

[00:22:21] Speaker: And then finally evaluation. So within our development for evaluation, we want to do a number of things. The first one is fast sanity checks.

[00:22:30] Speaker: And in order to do this, we want things we can run really fast, really really cheaply. So for web, we have something called mini world of bits, which is basically these trivial kind of web navigation things. We have something called the Adder Code Editing Benchmark, where it's just about editing individual files that we use.

[00:22:48] Speaker: But we also want highly realistic evaluation. So for the web, we have something called WebArena that we created at CMU. This is web navigation on real real open source websites. So it's open source [00:23:00] websites that are actually used to serve shops or like bulletin boards or other things like this.

[00:23:07] Speaker: And for code, we use Swebench, which I think a lot of people may have heard of. It's basically a coding benchmark that comes from real world pull requests on GitHub. So if you can solve those, you can also probably solve other real world pull requests. I would say we still don't have benchmarks for the fur full versatility of agents.

[00:23:25] Speaker: So, for example We don't have benchmarks that test whether agents can code and do web navigation. But we're working on that and hoping to release something in the next week or two. So if that sounds interesting to you, come talk to me and I, I will tell you more about it.

[00:23:42] Speaker: Cool. So I don't like making predictions, but I was told that I should be somewhat controversial, I guess, so I will, I will try to do it try to do it anyway, although maybe none of these will be very controversial. Um, the first thing is agent oriented LLMs like large language models for [00:24:00] agents.

[00:24:00] Speaker: My, my prediction is every large LM trainer will be focusing on training models as agents. So every large language model will be a better agent model by mid 2025. Competition will increase, prices will go down, smaller models will become competitive as agents. So right now, actually agents are somewhat expensive to run in some cases, but I expect that that won't last six months.

[00:24:23] Speaker: I, I bet we'll have much better agent models in six months. Another thing is instruction following ability, specifically in agentic contexts, will increase. And what that means is we'll have to do less manual engineering of agentic workflows and be able to do more by just prompting agents in more complex ways.

[00:24:44] Speaker: Cloud is already really good at this. It's not perfect, but it's already really, really good. And I expect the other models will catch up to Cloud pretty soon. Error correction ability will increase, less getting stuck in loops. Again, this is something that Cloud's already pretty good at and I expect the others will, will follow.[00:25:00]

[00:25:01] Speaker: Agent benchmarks. Agent benchmarks will start saturating.

[00:25:05] Speaker: And Swebench I think WebArena is already too easy. It, it is, it's not super easy, but it's already a bit too easy because the tasks we do in there are ones that take like two minutes for a human. So not, not too hard. And kind of historically in 2023 our benchmarks were too easy. So we built harder benchmarks like WebArena and Swebench were both built in 2023.

[00:25:31] Future of Agent Development

[00:25:31] Speaker: In 2024, our agents were too bad, so we built agents and now we're building better agents. In 2025, our benchmarks will be too easy, so we'll build better benchmarks, I'm, I'm guessing. So, I would expect to see much more challenging agent benchmarks come out, and we're already seeing some of them.

[00:25:49] Speaker: In 2026, I don't know. I didn't write AGI, but we'll, we'll, we'll see.

[00:25:56] Human-Agent Interaction Challenges

[00:25:56] Speaker: Then the human agent computer interface. I think one thing that [00:26:00] we'll want to think about is what do we do at 75 percent success rate at things that we like actually care about? Right now we have 53 percent or 55 percent on Swebench verified, which is real world GitHub PRs.

[00:26:16] Speaker: My impression is that the actual. Actual ability of models is maybe closer to 30 to 40%. So 30 to 40 percent of the things that I want an agent to solve on my own repos, it just solves without any human intervention. 80 to 90 percent it can solve without me opening an IDE. But I need to give it feedback.

[00:26:36] Speaker: So how do we, how do we make that interaction smooth so that humans can audit? The work of agents that are really, really good, but not perfect is going to be a big challenge.

[00:26:48] Expanding Agent Use Beyond Programming

[00:26:48] Speaker: How can we expose the power of programming agents to other industries? So like as programmers, I think not all of us are using agents every day in our programming, although we probably will be [00:27:00] in in months or maybe a year.

[00:27:02] Speaker: But I, I think it will come very naturally to us as programmers because we know code. We know, you know. Like how to architect software and stuff like that. So I think the question is how do we put this in the hands of like a lawyer or a chemist or somebody else and have them also be able to, you know, interact with it as naturally as we can.

[00:27:25] Redesigning Systems for Agent Efficiency

[00:27:25] Speaker: Another interesting thing is how can we redesign our existing systems for agents? So we had a paper on API based web agents, and basically what we showed is If you take a web agent and the agent interacts not with a website, but with APIs, the accuracy goes way up just because APIs are way easier to interact with.

[00:27:42] Speaker: And in fact, like when I ask the, well, our agent, our agent is able to browse websites, but whenever I want it to interact with GitHub, I tell it do not browse the GitHub website. Use the GitHub API because it's way more successful at doing that. So maybe, you know, every website is going to need to have [00:28:00] an API because we're going to be having agents interact with them.

[00:28:03] Accelerating Progress with Agent Technology

[00:28:03] Speaker: About progress, I think progress will get faster. It's already fast. A lot of people are already overwhelmed, but I think it will continue. The reason why is agents are building agents. And better agents will build better agents faster. So I expect that you know, if you haven't interacted with a coding agent yet, it's pretty magical, like the stuff that it can do.

[00:28:24] Speaker: So yeah.

[00:28:28] Call to Action for Open Source Contributions

[00:28:28] Speaker: And I have a call to action. I'm honestly, like I've been working on, you know, natural language processing and, and Language models for what, 15 years now. And even for me, it's pretty impressive what like AI agents powered by strong language models can do. On the other hand, I believe that we should really make these powerful tools accessible.

[00:28:49] Speaker: And what I mean by this is I don't think like, you know, We, we should have these be opaque or limited to only a set, a certain set of people. I feel like they should be [00:29:00] affordable. They shouldn't be increasing the, you know, difference in the amount of power that people have. If anything, I'd really like them to kind of make it It's possible for people who weren't able to do things before to be able to do them well.

[00:29:13] Speaker: Open source is one way to do that. That's why I'm working on open source. There are other ways to do that. You know, make things cheap, make things you know, so you can serve them to people who aren't able to afford them. Easily, like Duolingo is one example where they get all the people in the US to pay them 20 a month so that they can give all the people in South America free, you know, language education, so they can learn English and become, you know like, and become, you know, More attractive on the job market, for instance.

[00:29:41] Speaker: And so I think we can all think of ways that we can do that sort of thing. And if that resonates with you, please contribute. Of course, I'd be happy if you contribute to OpenHands and use it. But another way you can do that is just use open source solutions, contribute to them, research with them, and train strong open source [00:30:00] models.

[00:30:00] Speaker: So I see, you know, Some people in the room who are already training models. It'd be great if you could train models for coding agents and make them cheap. And yeah yeah, please. I, I was thinking about you among others. So yeah, that's all I have. Thanks.

[00:30:20] Speaker 2: Slight, slightly controversial. Tick is probably the nicest way to say hot ticks. Any hot ticks questions, actual hot ticks?

[00:30:31] Speaker: Oh, I can also show the other agents that were working, if anybody's interested, but yeah, sorry, go ahead.

[00:30:36] Q&A: Agent Performance and Benchmarks

[00:30:36] Speaker 3: Yeah, I have a couple of questions. So they're kind of paired, maybe. The first thing is that you said that You're estimating that your your agent is successfully resolving like something like 30 to 40 percent of your issues, but that's like below what you saw in Swebench.

[00:30:52] Speaker 3: So I guess I'm wondering where that discrepancy is coming from. And then I guess my other second question, which is maybe broader in scope is that [00:31:00] like, if, if you think of an agent as like a junior developer, and I say, go do something, then I expect maybe tomorrow to get a Slack message being like, Hey, I ran into this issue.

[00:31:10] Speaker 3: How can I resolve it? And, and, like you said, your agent is, like, successfully solving, like, 90 percent of issues where you give it direct feedback. So, are you thinking about how to get the agent to reach out to, like, for, for planning when it's, when it's stuck or something like that? Or, like, identify when it runs into a hole like that?

[00:31:30] Speaker: Yeah, so great. These are great questions. Oh,

[00:31:32] Speaker 3: sorry. The third question, which is a good, so this is the first two. And if so, are you going to add a benchmark for that second question?

[00:31:40] Speaker: Okay. Great. Yeah. Great questions. Okay. So the first question was why do I think it's resolving less than 50 percent of the issues on Swebench?

[00:31:48] Speaker: So first Swebench is on popular open source repos, and all of these popular open source repos were included in the training data for all of the language models. And so the language [00:32:00] models already know these repos. In some cases, the language models already know the individual issues in Swebench.

[00:32:06] Speaker: So basically, like, some of the training data has leaked. And so it, it definitely will overestimate with respect to that. I don't think it's like, you know, Horribly, horribly off but I think, you know, it's boosting the accuracy by a little bit. So, maybe that's the biggest reason why. In terms of asking for help, and whether we're benchmarking asking for help yes we are.

[00:32:29] Speaker: So one one thing we're working on now, which we're hoping to put out soon, is we we basically made SuperVig. Sweep edge issues. Like I'm having a, I'm having a problem with the matrix multiply. Please help. Because these are like, if anybody's run a popular open source, like framework, these are what half your issues are.

[00:32:49] Speaker: You're like users show up and say like, my screen doesn't work. What, what's wrong or something. And so then you need to ask them questions and how to reproduce. So yeah, we're, we're, we're working on [00:33:00] that. I think. It, my impression is that agents are not very good at asking for help, even Claude. So like when, when they ask for help, they'll ask for help when they don't need it.

[00:33:11] Speaker: And then won't ask for help when they do need it. So this is definitely like an issue, I think.

[00:33:20] Speaker 4: Thanks for the great talk. I also have two questions.

[00:33:23] Q&A: Web Agents and Interaction Methods

[00:33:23] Speaker 4: It's first one can you talk a bit more about how the web agent interacts with So is there a VLM that looks at the web page layout and then you parse the HTML and select which buttons to click on? And if so do you think there's a future where there's like, so I work at Bing Microsoft AI.

[00:33:41] Speaker 4: Do you think there's a future where the same web index, but there's an agent friendly web index where all the processing is done offline so that you don't need to spend time. Cleaning up, like, cleaning up these TML and figuring out what to click online. And any thoughts on, thoughts on that?

[00:33:57] Speaker: Yeah, so great question. There's a lot of work on web [00:34:00] agents. I didn't go into, like, all of the details, but I think there's There's three main ways that agents interact with websites. The first way is the simplest way and the newest way, but it doesn't work very well, which is you take a screenshot of the website and then you click on a particular pixel value on the website.

[00:34:23] Speaker: And Like models are not very good at that at the moment. Like they'll misclick. There was this thing about how like clawed computer use started like looking at pictures of Yellowstone national park or something like this. I don't know if you heard about this anecdote, but like people were like, oh, it's so human, it's looking for vacation.

[00:34:40] Speaker: And it was like, no, it probably just misclicked on the wrong pixels and accidentally clicked on an ad. So like this is the simplest way. The second simplest way. You take the HTML and you basically identify elements in the HTML. You don't use any vision whatsoever. And then you say, okay, I want to click on this element.

[00:34:59] Speaker: I want to enter text [00:35:00] in this element or something like that. But HTML is too huge. So it actually, it usually gets condensed down into something called an accessibility tree, which was made for screen readers for visually impaired people. And So that's another way. And then the third way is kind of a hybrid where you present the screenshot, but you also present like a textual summary of the output.

[00:35:18] Speaker: And that's the one that I think will probably work best. What we're using is we're just using text at the moment. And that's just an implementation issue that we haven't implemented the. Visual stuff yet, but that's kind of like we're working on it now. Another thing that I should point out is we actually have two modalities for web browsing.

[00:35:35] Speaker: Very recently we implemented this. And the reason why is because if you want to interact with full websites you will need to click on all of the elements or have the ability to click on all of the elements. But most of our work that we need websites for is just web browsing and like gathering information.

[00:35:50] Speaker: So we have another modality where we convert all of it to markdown because that's like way more concise and easier for the agent to deal with. And then [00:36:00] can we create an index specifically for agents, maybe a markdown index or something like that would be, you know, would make sense. Oh, how would I make a successor to Swebench?

[00:36:10] Speaker: So I mean, the first thing is there's like live code bench, which live code bench is basically continuously updating to make sure it doesn't leak into language model training data. That's easy to do for Swebench because it comes from real websites and those real websites are getting new issues all the time.

[00:36:27] Speaker: So you could just do it on the same benchmarks that they have there. There's also like a pretty large number of things covering various coding tasks. So like, for example, Swebunch is mainly fixing issues, but there's also like documentation, there's generating tests that actually test the functionality that you want.

[00:36:47] Speaker: And there there was a paper by a student at CMU on generating tests and stuff like that. So I feel like. Swebench is one piece of the puzzle, but you could also have like 10 different other tasks and then you could have like a composite [00:37:00] benchmark where you test all of these abilities, not just that particular one.

[00:37:04] Speaker: Well, lots, lots of other things too, but

[00:37:11] Speaker 2: Question from across. Use your mic, it will help. Um,

[00:37:15] Speaker 5: Great talk. Thank you.

[00:37:16] Q&A: Agent Architectures and Improvements

[00:37:16] Speaker 5: My question is about your experience designing agent architectures. Specifically how much do you have to separate concerns in terms of tasks specific agents versus having one agent to do three or five things with a gigantic prompt with conditional paths and so on.

[00:37:35] Speaker: Yeah, so that's a great question. So we have a basic coding and browsing agent. And I won't say basic, like it's a good, you know, it's a good agent, but it does coding and browsing. And it has instructions about how to do coding and browsing. That is enough for most things. Especially given a strong language model that has a lot of background knowledge about how to solve different types of tasks and how to use different APIs and stuff like that.

[00:37:58] Speaker: We do have [00:38:00] a mechanism for something called micro agents. And micro agents are basically something that gets added to the prompt when a trigger is triggered. Right now it's very, very rudimentary. It's like if you detect the word GitHub anywhere, you get instructions about how to interact with GitHub, like use the API and don't browse.

[00:38:17] Speaker: Also another one that I just added is for NPM, the like JavaScript package manager. And NPM, when it runs and it hits a failure, it Like hits in interactive terminals where it says, would you like to quit? Yep. Enter yes. And if that does it, it like stalls our agent for the time out until like two minutes.

[00:38:36] Speaker: So like I added a new microagent whenever it started using NPM, it would Like get instructions about how to not use interactive terminal and stuff like that. So that's our current solution. Honestly, I like it a lot. It's simple. It's easy to maintain. It works really well and stuff like that. But I think there is a world where you would want something more complex than that.

[00:38:55] Speaker 5: Got it. Thank you.

[00:38:59] Speaker 6: I got a [00:39:00] question about MCP. I feel like this is the Anthropic Model Context Protocol. It seems like the most successful type of this, like, standardization of interactions between computers and agents. Are you guys adopting it? Is there any other competing standard?

[00:39:16] Speaker 6: Anything, anything thought about it?

[00:39:17] Speaker: Yeah, I think the Anth, so the Anthropic MCP is like, a way to It, it's essentially a collection of APIs that you can use to interact with different things on the internet. I, I think it's not a bad idea, but it, it's like, there's a few things that bug me a little bit about it.

[00:39:40] Speaker: It's like we already have an API for GitHub, so why do we need an MCP for GitHub? Right. You know, like GitHub has an API, the GitHub API is evolving. We can look up the GitHub API documentation. So it seems like kind of duplicated a little bit. And also they have a setting where [00:40:00] it's like you have to spin up a server to serve your GitHub stuff.

[00:40:04] Speaker: And you have to spin up a server to serve your like, you know, other stuff. And so I think it makes, it makes sense if you really care about like separation of concerns and security and like other things like this, but right now we haven't seen, we haven't seen that. To have a lot more value than interacting directly with the tools that are already provided.

[00:40:26] Speaker: And that kind of goes into my general philosophy, which is we're already developing things for programmers. You know,

[00:40:36] Speaker: how is an agent different than from a programmer? And it is different, obviously, you know, like agents are different from programmers, but they're not that different at this point. So we can kind of interact with the interfaces we create for, for programmers. Yeah. I might change my mind later though.

[00:40:51] Speaker: So we'll see.

[00:40:54] Speaker 7: Yeah. Hi. Thanks. Very interesting talk. You were saying that the agents you have right now [00:41:00] solve like maybe 30 percent of your, your issues out of the gate. I'm curious of the things that it doesn't do. Is there like a pattern that you observe? Like, Oh, like these are the sorts of things that it just seems to really struggle with, or is it just seemingly random?

[00:41:15] Speaker: It's definitely not random. It's like, if you think it's more complex than it's. Like, just intuitively, it's more likely to fail. I've gotten a bit better at prompting also, so like, just to give an example it, it will sometimes fail to fix a GitHub workflow because it will not look at the GitHub workflow and understand what the GitHub workflow is doing before it solves the problem.

[00:41:43] Speaker: So I, I think actually probably the biggest thing that it fails at is, um, er, that our, our agent plus Claude fails at is insufficient information gathering before trying to solve the task. And so if you provide all, if you provide instructions that it should do information [00:42:00] gathering beforehand, it tends to do well.

[00:42:01] Speaker: If you don't provide sufficient instructions, it will try to solve the task without, like, fully understanding the task first, and then fail, and then you need to go back and give feedback. You know, additional feedback. Another example, like, I, I love this example. While I was developing the the monitor website that I, I showed here, we hit a really tricky bug where it was writing out a cache file to a different directory than it was reading the cache file from.

[00:42:26] Speaker: And I had no idea what to do. I had no idea what was going on. I, I thought the bug was in a different part of the code, but what I asked it to do was come up with five possible reasons why this could be failing and decreasing order of likelihood and examine all of them. And that worked and it could just go in and like do that.

[00:42:44] Speaker: So like I think a certain level of like scaffolding about like how it should sufficiently Gather all the information that's necessary in order to solve a task is like, if that's missing, then that's probably the biggest failure point at the moment. [00:43:00]

[00:43:01] Speaker 7: Thanks.

[00:43:01] Speaker 6: Yeah.

[00:43:06] Speaker 6: I'm just, I'm just using this as a chance to ask you all my questions.

[00:43:09] Q&A: Self-Improving Agents and Authentication

[00:43:09] Speaker 6: You had a, you had a slide on here about like self improving agents or something like that with memory. It's like a really throwaway slide for like a super powerful idea. It got me thinking about how I would do it. I have no idea how.

[00:43:21] Speaker 6: So I just wanted you to chain a thought more on this.

[00:43:25] Speaker: Yeah, self, self improving. So I think the biggest reason, like the simplest possible way to create a self improving agent. The problem with that is to have a really, really strong language model that with infinite context, and it can just go back and look at like all of its past experiences and, you know, learn from them.

[00:43:46] Speaker: You might also want to remove the bad stuff just so it doesn't over index on it's like failed past experiences. But the problem is a really powerful language model is large. Infinite context is expensive. We don't have a good way to [00:44:00] index into it because like rag, Okay. At least in my experience, RAG from language to code doesn't work super well.

[00:44:08] Speaker: So I think in the end, it's like, that's the way I would like to solve this problem. I'd like to have an infinite context and somehow be able to index into it appropriately. And I think that would mostly solve it. Another thing you can do is fine tuning. So I think like RAG is one way to get information into your model.

[00:44:23] Speaker: Fine tuning is another way to get information into your model. So. That might be another way of continuously improving. Like you identify when you did a good job and then just add all of the good examples into your model.

[00:44:34] Speaker 6: Yeah. So, you know, how like Voyager tries to write code into a skill library and then you reuse as a skill library, right?

[00:44:40] Speaker 6: So that it improves in the sense that it just builds up the skill library over time.

[00:44:44] Speaker: Yep.

[00:44:44] Speaker 6: One thing I was like thinking about and there's this idea of, from, from Devin, your, your arch nemesis of playbooks. I don't know if you've seen them.

[00:44:52] Speaker: Yeah, I mean, we're calling them workflows, but they're simpler.

[00:44:55] Speaker 6: Yeah, so like, basically, like, you should, like, once a workflow works, you can kind of, [00:45:00] like, persist them as a skill library. Yeah. Right? Like I, I feel like that there's a, that's like some in between, like you said, you know, it's hard to do rag between language and code, but I feel like that is ragged for, like, I've done this before, last time I did it, this, this worked.

[00:45:14] Speaker 6: So I'm just going to shortcut. All the stuff that failed before.

[00:45:18] Speaker: Yeah, I totally, I think it's possible. It's just, you know, not, not trivial at the same time. I'll explain the two curves. So basically, the base, the baseline is just an agent that does it from scratch every time. And this curve up here is agent workflow memory where it's like adding the successful experiences back into the prompt.

[00:45:39] Speaker: Why is this improving? The reason why is because just it failed on the first few examples and for the average to catch up it, it took a little bit of time. So it's not like this is actually improving it. You could just basically view the this one is constant and then this one is like improving.

[00:45:56] Speaker: Like this, basically you can see it's continuing to go [00:46:00] up.

[00:46:01] Speaker 8: How do you think we're going to solve the authentication problem for agents right now?

[00:46:05] Speaker: When you say authentication, you mean like credentials, like, yeah.

[00:46:09] Speaker 8: Yeah. Cause I've seen a few like startup solutions today, but it seems like it's limited to the amount of like websites or actual like authentication methods that it's capable of performing today.

[00:46:19] Speaker: Yeah. Great questions. So. My preferred solution to this at the moment is GitHub like fine grained authentication tokens and GitHub fine grained authentication tokens allow you to specify like very free. On a very granular basis on this repo, you have permission to do this, on this repo, you have permission to do this.

[00:46:41] Speaker: You also can prevent people from pushing to the main branch unless they get approved. You can do all of these other things. And I think these were all developed for human developers. Or like, the branch protection rules were developed for human developers. The fine grained authentication tokens were developed for GitHub apps.

[00:46:56] Speaker: I think for GitHub, maybe [00:47:00] just pushing this like a little bit more is the way to do this. For other things, they're totally not prepared to give that sort of fine grained control. Like most APIs don't have something like a fine grained authentication token. And that goes into my like comment that we're going to need to prepare the world for agents, I think.

[00:47:17] Speaker: But I think like the GitHub authentication tokens are like a good template for how you could start doing that maybe, but yeah, I don't, I don't, I don't have an answer.

[00:47:25] Speaker 8: I'll let you know if I find one.

[00:47:26] Speaker: Okay. Yeah.

[00:47:31] Live Demonstration and Closing Remarks

[00:47:31] Speaker: I'm going to finish up. Let, let me just see.

[00:47:37] Speaker: Okay. So this one this one did write a script. I'm not going to actually read it for you. And then the other one, let's see.

[00:47:51] Speaker: Yeah. So it sent a PR, sorry. What is, what is the PR URL?[00:48:00]

[00:48:02] Speaker: So I don't, I don't know if this sorry, that's taking way longer than it should. Okay, cool. Yeah. So this one sent a PR. I'll, I'll tell you later if this actually like successfully Oh, no, it's deployed on Vercel, so I can actually show you, but let's, let me try this real quick. Sorry. I know I don't have time.

[00:48:24] Speaker: Yeah, there you go. I have pie charts now. So it's so fun. It's so fun to play with these things. Cause you could just do that while I'm giving a, you know, talk and things like that. So, yeah, thanks.

Happy holidays! We’ll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all all our LS supporters who helped fund the gorgeous venue and A/V production!

For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver.

Today, we’re proud to share Loubna’s highly anticipated talk (slides here)!

Synthetic Data

We called out the Synthetic Data debate at last year’s NeurIPS, and no surprise that 2024 was dominated by the rise of synthetic data everywhere:

* Apple’s Rephrasing the Web, Microsoft’s Phi 2-4 and Orca/AgentInstruct, Tencent’s Billion Persona dataset, DCLM, and HuggingFace’s FineWeb-Edu, and Loubna’s own Cosmopedia extended the ideas of synthetic textbook and agent generation to improve raw web scrape dataset quality

* This year we also talked to the IDEFICS/OBELICS team at HuggingFace who released WebSight this year, the first work on code-vs-images synthetic data.

* We called Llama 3.1 the Synthetic Data Model for its extensive use (and documentation!) of synthetic data in its pipeline, as well as its permissive license.

* Nemotron CC and Nemotron-4-340B also made a big splash this year for how they used 20k items of human data to synthesize over 98% of the data used for SFT/PFT.

* Cohere introduced Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress observing gains of up to 56.5% improvement in win rates comparing multiple teachers vs the single best teacher model

* In post training, AI2’s Tülu3 (discussed by Luca in our Open Models talk) and Loubna’s Smol Talk were also notable open releases this year.

This comes in the face of a lot of scrutiny and criticism, with Scale AI as one of the leading voices publishing AI models collapse when trained on recursively generated data in Nature magazine bringing mainstream concerns to the potential downsides of poor quality syndata:

Part of the concerns we highlighted last year on low-background tokens are coming to bear: ChatGPT contaminated data is spiking in every possible metric:

But perhaps, if Sakana’s AI Scientist pans out this year, we will have mostly-AI AI researchers publishing AI research anyway so do we really care as long as the ideas can be verified to be correct?

Smol Models

Meta surprised many folks this year by not just aggressively updating Llama 3 and adding multimodality, but also adding a new series of “small” 1B and 3B “on device” models this year, even working on quantized numerics collaborations with Qualcomm, Mediatek, and Arm. It is near unbelievable that a 1B model today can qualitatively match a 13B model of last year:

and the minimum size to hit a given MMLU bar has come down roughly 10x in the last year. We have been tracking this proxied by Lmsys Elo and inference price:

The key reads this year are:

* MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

* Apple Intelligence Foundation Language Models

* Hymba: A Hybrid-head Architecture for Small Language Models

* Loubna’s SmolLM and SmolLM2: a family of state-of-the-art small models with 135M, 360M, and 1.7B parameters on the pareto efficiency frontier.

* and Moondream, which we already covered in the 2024 in Vision talk

Full Talk on YouTube

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Timestamps

* [00:00:05] Loubna Intro

* [00:00:33] The Rise of Synthetic Data Everywhere

* [00:02:57] Model Collapse

* [00:05:14] Phi, FineWeb, Cosmopedia - Synthetic Textbooks

* [00:12:36] DCLM, Nemotron-CC

* [00:13:28] Post Training - AI2 Tulu, Smol Talk, Cohere Multilingual Arbitrage

* [00:16:17] Smol Models

* [00:18:24] On Device Models

* [00:22:45] Smol Vision Models

* [00:25:14] What's Next

Transcript

2024 in Synthetic Data and Smol Models

[00:00:00] ​

[00:00:05] Loubna Intro

[00:00:05] Speaker: ​I'm very happy to be here. Thank you for the invitation. So I'm going to be talking about synthetic data in 2024. And then I'm going to be talking about small on device models. So I think the most interesting thing about synthetic data this year is that like now we have it everywhere in the large language models pipeline.

[00:00:33] The Rise of Synthetic Data Everywhere

[00:00:33] Speaker: I think initially, synthetic data was mainly used just for post training, because naturally that's the part where we needed human annotators. And then after that, we realized that we don't really have good benchmarks to [00:01:00] measure if models follow instructions well, if they are creative enough, or if they are chatty enough, so we also started using LLMs as judges.

[00:01:08] Speaker: Thank you. And I think this year and towards the end of last year, we also went to the pre training parts and we started generating synthetic data for pre training to kind of replace some parts of the web. And the motivation behind that is that you have a lot of control over synthetic data. You can control your prompt and basically also the kind of data that you generate.

[00:01:28] Speaker: So instead of just trying to filter the web, you could try to get the LLM to generate what you think the best web pages could look like and then train your models on that. So this is how we went from not having synthetic data at all in the LLM pipeline to having it everywhere. And so the cool thing is like today you can train an LLM with like an entirely synthetic pipeline.

[00:01:49] Speaker: For example, you can use our Cosmopedia datasets and you can train a 1B model on like 150 billion tokens that are 100 percent synthetic. And those are also of good quality. And then you can [00:02:00] instruction tune the model on a synthetic SFT dataset. You can also do DPO on a synthetic dataset. And then to evaluate if the model is good, you can use.

[00:02:07] Speaker: A benchmark that uses LLMs as a judge, for example, MTBench or AlpacaEvil. So I think this is like a really mind blowing because like just a few years ago, we wouldn't think this is possible. And I think there's a lot of concerns about model collapse, and I'm going to talk about that later. But we'll see that like, if we use synthetic data properly and we curate it carefully, that shouldn't happen.

[00:02:29] Speaker: And the reason synthetic data is very popular right now is that we have really strong models, both open and closed. It is really cheap and fast to use compared to human annotations, which cost a lot and take a lot of time. And also for open models right now, we have some really good inference frameworks.

[00:02:47] Speaker: So if you have enough GPUs, it's really easy to spawn these GPUs and generate like a lot of synthetic data. Some examples are VLM, TGI, and TensorRT.

[00:02:57] Model Collapse

[00:02:57] Speaker: Now let's talk about the elephant in the room, model [00:03:00] collapse. Is this the end? If you look at the media and all of like, for example, some papers in nature, it's really scary because there's a lot of synthetic data out there in the web.

[00:03:09] Speaker: And naturally we train on the web. So we're going to be training a lot of synthetic data. And if model collapse is going to happen, we should really try to take that seriously. And the other issue is that, as I said, we think, a lot of people think the web is polluted because there's a lot of synthetic data.

[00:03:24] Speaker: And for example, when we're building fine web datasets here at Guillerm and Hinek, we're interested in like, how much synthetic data is there in the web? So there isn't really a method to properly measure the amount of synthetic data or to save a webpage synthetic or not. But one thing we can do is to try to look for like proxy words, for example, expressions like as a large language model or words like delve that we know are actually generated by chat GPT.

[00:03:49] Speaker: We could try to measure the amount of these words in our data system and compare them to the previous years. For example, here, we measured like a, these words ratio in different dumps of common crawl. [00:04:00] And we can see that like the ratio really increased after chat GPT's release. So if we were to say that synthetic data amount didn't change, you would expect this ratio to stay constant, which is not the case.

[00:04:11] Speaker: So there's a lot of synthetic data probably on the web, but does this really make models worse? So what we did is we trained different models on these different dumps. And we then computed their performance on popular, like, NLP benchmarks, and then we computed the aggregated score. And surprisingly, you can see that the latest DOMs are actually even better than the DOMs that are before.

[00:04:31] Speaker: So if there's some synthetic data there, at least it did not make the model's worse. Yeah, which is really encouraging. So personally, I wouldn't say the web is positive with Synthetic Data. Maybe it's even making it more rich. And the issue with like model collapse is that, for example, those studies, they were done at like a small scale, and you would ask the model to complete, for example, a Wikipedia paragraph, and then you would train it on these new generations, and you would do that every day.

[00:04:56] Speaker: iteratively. I think if you do that approach, it's normal to [00:05:00] observe this kind of behavior because the quality is going to be worse because the model is already small. And then if you train it just on its generations, you shouldn't expect it to become better. But what we're really doing here is that we take a model that is very large and we try to distill its knowledge into a model that is smaller.

[00:05:14] Phi, FineWeb, Cosmopedia - Synthetic Textbooks

[00:05:14] Speaker: And in this way, you can expect to get like a better performance for your small model. And using synthetic data for pre-training has become really popular. After the textbooks are all you need papers where Microsoft basically trained a series of small models on textbooks that were using a large LLM.

[00:05:32] Speaker: And then they found that these models were actually better than models that are much larger. So this was really interesting. It was like first of its time, but it was also met with a lot of skepticism, which is a good thing in research. It pushes you to question things because the dataset that they trained on was not public, so people were not really sure if these models are really good or maybe there's just some data contamination.

[00:05:55] Speaker: So it was really hard to check if you just have the weights of the models. [00:06:00] And as Hugging Face, because we like open source, we tried to reproduce what they did. So this is our Cosmopedia dataset. We basically tried to follow a similar approach to what they documented in the paper. And we created a synthetic dataset of textbooks and blog posts and stories that had almost 30 billion tokens.

[00:06:16] Speaker: And we tried to train some models on that. And we found that like the key ingredient to getting a good data set that is synthetic is trying as much as possible to keep it diverse. Because if you just throw the same prompts as your model, like generate like a textbook about linear algebra, and even if you change the temperature, the textbooks are going to look alike.

[00:06:35] Speaker: So there's no way you could scale to like millions of samples. And the way you do that is by creating prompts that have some seeds that make them diverse. In our case, the prompt, we would ask the model to generate a textbook, but make it related to an extract from a webpage. And also we try to frame it within, to stay within topic.

[00:06:55] Speaker: For example, here, we put like an extract about cardiovascular bioimaging, [00:07:00] and then we ask the model to generate a textbook related to medicine that is also related to this webpage. And this is a really nice approach because there's so many webpages out there. So you can. Be sure that your generation is not going to be diverse when you change the seed example.

[00:07:16] Speaker: One thing that's challenging with this is that you want the seed samples to be related to your topics. So we use like a search tool to try to go all of fine web datasets. And then we also do a lot of experiments with the type of generations we want the model to generate. For example, we ask it for textbooks for middle school students or textbook for college.

[00:07:40] Speaker: And we found that like some generation styles help on some specific benchmarks, while others help on other benchmarks. For example, college textbooks are really good for MMLU, while middle school textbooks are good for benchmarks like OpenBookQA and Pico. This is like a sample from like our search tool.

[00:07:56] Speaker: For example, you have a top category, which is a topic, and then you have some [00:08:00] subtopics, and then you have the topic hits, which are basically the web pages in fine web does belong to these topics. And here you can see the comparison between Cosmopedia. We had two versions V1 and V2 in blue and red, and you can see the comparison to fine web, and as you can see throughout the training training on Cosmopedia was consistently better.

[00:08:20] Speaker: So we managed to get a data set that was actually good to train these models on. It's of course so much smaller than FineWeb, it's only 30 billion tokens, but that's the scale that Microsoft data sets was, so we kind of managed to reproduce a bit what they did. And the data set is public, so everyone can go there, check if everything is all right.

[00:08:38] Speaker: And now this is a recent paper from NVIDIA, Neumatron CC. They took things a bit further, and they generated not a few billion tokens, but 1. 9 trillion tokens, which is huge. And we can see later how they did that. It's more of, like, rephrasing the web. So we can see today that there's, like, some really huge synthetic datasets out there, and they're public, so, [00:09:00] like, you can try to filter them even further if you want to get, like, more high quality corpses.

[00:09:04] Speaker: So for this, rephrasing the web this approach was suggested in this paper by Pratyush, where basically in this paper, they take some samples from C4 datasets, and then they use an LLM to rewrite these samples into a better format. For example, they ask an LLM to rewrite the sample into a Wikipedia passage or into a Q& A page.

[00:09:25] Speaker: And the interesting thing in this approach is that you can use a model that is Small because it doesn't, rewriting doesn't require knowledge. It's just rewriting a page into a different style. So the model doesn't need to have like knowledge that is like extensive of what is rewriting compared to just asking a model to generate a new textbook and not giving it like ground truth.

[00:09:45] Speaker: So here they rewrite some samples from C4 into Q& A, into Wikipedia, and they find that doing this works better than training just on C4. And so what they did in Nemo Trans CC is a similar approach. [00:10:00] They rewrite some pages from Common Crawl for two reasons. One is to, like improve Pages that are low quality, so they rewrite them into, for example, Wikipedia page, so they look better.

[00:10:11] Speaker: And another reason is to create more diverse datasets. So they have a dataset that they already heavily filtered, and then they take these pages that are already high quality, and they ask the model to rewrite them in Question and Answer format. into like open ended questions or like multi choice questions.

[00:10:27] Speaker: So this way they can reuse the same page multiple times without fearing like having multiple duplicates, because it's the same information, but it's going to be written differently. So I think that's also a really interesting approach for like generating synthetic data just by rephrasing the pages that you already have.

[00:10:44] Speaker: There's also this approach called Prox where they try to start from a web page and then they generate a program which finds how to write that page to make it better and less noisy. For example, here you can see that there's some leftover metadata in the web page and you don't necessarily want to keep that for training [00:11:00] your model.

[00:11:00] Speaker: So So they train a model that can generate programs that can like normalize and remove lines that are extra. So I think this approach is also interesting, but it's maybe less scalable than the approaches that I presented before. So that was it for like rephrasing and generating new textbooks.

[00:11:17] Speaker: Another approach that I think is really good and becoming really popular for using synthetic data for pre training is basically building a better classifiers. For filtering the web for example, here we release the data sets called fine web edu. And the way we built it is by taking Llama3 and asking it to rate the educational content of web pages from zero to five.

[00:11:39] Speaker: So for example, if a page is like a really good textbook that could be useful in a school setting, it would get a really high score. And if a page is just like an advertisement or promotional material, it would get a lower score. And then after that, we take these synthetic annotations and we train a classifier on them.

[00:11:57] Speaker: It's a classifier like a BERT model. [00:12:00] And then we run this classifier on all of FineWeb, which is a 15 trillion tokens dataset. And then we only keep the pages that have like a score that's higher than 3. So for example, in our case, we went from 15 trillion tokens to 3. to just 1. 5 trillion tokens. Those are really highly educational.

[00:12:16] Speaker: And as you can see here, a fine web EDU outperforms all the other public web datasets by a larger margin on a couple of benchmarks here, I show the aggregated score and you can see that this approach is really effective for filtering web datasets to get like better corpuses for training your LLMs.

[00:12:36] DCLM, Nemotron-CC

[00:12:36] Speaker: Others also try to do this approach. There's, for example, the DCLM datasets where they also train the classifier, but not to detect educational content. Instead, they trained it on OpenHermes dataset, which is a dataset for instruction tuning. And also they explain like IAM5 subreddits, and then they also get really high quality dataset which is like very information dense and can help [00:13:00] you train some really good LLMs.

[00:13:01] Speaker: And then Nemotron Common Crawl, they also did this approach, but instead of using one classifier, they used an ensemble of classifiers. So they used, for example, the DCLM classifier, and also classifiers like the ones we used in FineWebEducational, and then they combined these two. Scores into a, with an ensemble method to only retain the best high quality pages, and they get a data set that works even better than the ones we develop.

[00:13:25] Speaker: So that was it for like synthetic data for pre-training.

[00:13:28] Post Training - AI2 Tulu, Smol Talk, Cohere Multilingual Arbitrage

[00:13:28] Speaker: Now we can go back to post training. I think there's a lot of interesting post training data sets out there. One that was released recently, the agent instructs by Microsoft where they basically try to target some specific skills. And improve the performance of models on them.

[00:13:43] Speaker: For example, here, you can see code, brain teasers, open domain QA, and they managed to get a dataset that outperforms that's when fine tuning Mistral 7b on it, it outperforms the original instruct model that was released by Mistral. And as I said, to get good synthetic data, you really [00:14:00] have to have a framework to make sure that your data is diverse.

[00:14:03] Speaker: So for example, for them, they always. And then they see the generations on either source code or raw text documents, and then they rewrite them to make sure they're easier to generate instructions from, and then they use that for their like instruction data generation. There's also the Tool3SFT mixture, which was released recently by Allen AI.

[00:14:23] Speaker: It's also really good quality and it covers a wide range of tasks. And the way they make sure that this dataset is diverse is by using personas from the persona hub datasets. Which is basically a data set of like I think over a million personas. And for example, in the tool mixture to generate like a new code snippet, they would give like the model persona, for example, a machine learning researcher interested in neural networks, and then ask it to generate like a coding problem.

[00:14:49] Speaker: This way you make sure that your data set is really diverse, and then you can further filter the data sets, for example, using the reward models. We also released a dataset called Smalltalk, [00:15:00] and we also tried to cover the wide range of tasks, and as you can see here, for example, when fine tuning Mistral 7b on the dataset, we also outperformed the original Mistral instructs on a number of benchmarks, notably on mathematics and instruction following with ifevil.

[00:15:18] Speaker: Another paper that's really interesting I wanted to mention is this one called Multilingual Data Arbitrage by Cohere. And basically they want to generate a data set for post training that is multilingual. And they have a really interesting problem. It's the fact that there isn't like one model that's really good at all the languages they wanted.

[00:15:36] Speaker: So what they do is that like they use not just one teacher model, but multiple teachers. And then they have a router which basically sends the prompts they have to all these models. And then they get the completions and they have a reward model that traces all these generations and only keeps the best one.

[00:15:52] Speaker: And this is like arbitrage and finance. So well, I think what's interesting in this, it shows that like synthetic data, it doesn't have to come from a single model. [00:16:00] And because we have so many good models now, you could like pull these models together and get like a dataset that's really high quality and that's diverse and that's covers all your needs.

[00:16:12] Speaker: I was supposed to put a meme there, but. Yeah, so that was it for like a synthetic data.

[00:16:17] Smol Models

[00:16:17] Speaker: Now we can go to see what's happening in the small models field in 2024. I don't know if you know, but like now we have some really good small models. For example, Lama 3. 2 1B is. It matches Lama 2. 13b from, that was released last year on the LMSYS arena, which is basically the default go to leaderboard for evaluating models using human evaluation.

[00:16:39] Speaker: And as you can see here, the scores of the models are really close. So I think we've made like hugely forward in terms of small models. Of course, that's one, just one data point, but there's more. For example, if you look at this chart from the Quint 2. 5 blog post, it shows that today we have some really good models that are only like 3 billion parameters [00:17:00] and 4 billion that score really high on MMLU.

[00:17:03] Speaker: Which is a really popular benchmark for evaluating models. And you can see here that the red, the blue dots have more than 65 on MMLU. And the grey ones have less. And for example, Llama33b had less. So now we have a 3b model that outperforms a 33b model that was released earlier. So I think now people are starting to realize that like, we shouldn't just scale and scale models, but we should try to make them more efficient.

[00:17:33] Speaker: I don't know if you knew, but you can also chat with a 3B plus model on your iPhone. For example, here, this is an app called PocketPal, where you can go and select a model from Hugging Face. It has a large choice. For example, here we loaded the 5. 3. 5, which is 3. 8 billion parameters on this iPhone. And we can chat with this and you can see that even the latency is also acceptable.

[00:17:57] Speaker: For example, here, I asked it to give me a joke about [00:18:00] NeurIPS. So let's see what it has to say.

[00:18:06] Speaker: Okay, why did the neural network attend NeurIPS? Because it heard there would be a lot of layers and fun and it wanted to train its sense of humor. So not very funny, but at least it can run on device. Yeah, so I think now we have good small models, but we also have like good frameworks and tools to use these small models.

[00:18:24] On Device Models

[00:18:24] Speaker: So I think we're really close to having like really on edge and on device models that are really good. And I think for a while we've had this narrative. But just training larger models is better. Of course, this is supported by science scaling laws. As you can see here, for example, when we scale the model size, the loss is lower and obviously you get a better model.

[00:18:46] Speaker: But and we can see this, for example, in the GPT family of models, how we went from just a hundred million parameters to more than a trillion. parameters. And of course, we all observed the performance improvement when using the latest model. But [00:19:00] one thing that we shouldn't forget is that when we scale the model, we also scale the inference costs and time.

[00:19:05] Speaker: And so the largest models were are going to cost so much more. So I think now instead of just building larger models, we should be focusing on building more efficient models. It's no longer a race for the largest models since these models are really expensive to run and they require like a really good infrastructure to do that and they cannot run on, for example, consumer hardware.

[00:19:27] Speaker: And when you try to build more efficient models that match larger models, that's when you can really unlock some really interesting on device use cases. And I think a trend that we're noticing now is the trend of training smaller models longer. For example, if you compare how much, how long LLAMA was trained compared to LLAMA3, there is a huge increase in the pre training length.

[00:19:50] Speaker: LLAMA was trained on 1 trillion tokens, but LLAMA3 8b was trained on 15 trillion tokens. So Meta managed to get a model that's the same size, but But it performs so much [00:20:00] better by choosing to like spend the sacrifice during training, because as we know, training is a one time cost, but inference is something that's ongoing.

[00:20:08] Speaker: If we want to see what are like the small models reads in 2024, I think this mobile LLM paper by Meta is interesting. They try to study different models that are like have the less than 1 billion parameters and find which architecture makes most sense for these models. For example, they find that depth is more important than width.

[00:20:29] Speaker: So it's more important to have models that have like more layers than just one. making them more wide. They also find that GQA helps, that tying the embedding helps. So I think it's a nice study overall for models that are just a few hundred million parameters. There's also the Apple intelligence tech report, which is interesting.

[00:20:48] Speaker: So for Apple intelligence, they had two models, one that was like on server and another model that was on device. It had 3 billion parameters. And I think the interesting part is that they trained this model using [00:21:00] pruning. And then distillation. And for example, they have this table where they show that, like, using pruning and distillation works much better than training from scratch.

[00:21:08] Speaker: And they also have some interesting insights about, like, how they specialize their models on specific tasks, like, for example, summarization and rewriting. There's also this paper by NVIDIA that was released recently. I think you've already had a talk about, like, hybrid models that was all interesting.

[00:21:23] Speaker: And this model, they used, like, a hybrid architecture between state space models and transformers. And they managed to train a 1B model that's really performant without needing to train it on a lot of tokens. And regarding our work, we just recently released SmallM2, so it's a series of three models, which are the best in class in each model size.

[00:21:46] Speaker: For example, our 1. 7b model outperforms Lama 1b and also Qt 2. 5. And how we managed to train this model is the following. That's where you spent a lot of time trying to curate the pre training datasets. We did a lot of [00:22:00] ablations, trying to find which datasets are good and also how to mix them. We also created some new math and code datasets that we're releasing soon.

[00:22:08] Speaker: But you basically really spent a lot of time trying to find what's the best mixture that you can train these models on. And then we spent some time trying to like we also trained these models for very long. For example, small M1 was trained only on 1 trillion tokens, but this model is trained on 11 trillion tokens.

[00:22:24] Speaker: And we saw that the performance kept improving. The models didn't really plateau mid training, which I think is really interesting. It shows that you can train such small models for very long and keep getting performance gains. What's interesting about SmallLM2 is that it's fully open. We also released, like the pre training code base, the fine tuning code, the datasets, and also evaluation in this repository.

[00:22:45] Smol Vision Models

[00:22:45] Speaker: Also there's, like, really interesting small models for text, but also for vision. For example, here you can see SmallVLM, which is a 2B model that's really efficient. It doesn't consume a lot of RAM, and it also has a good performance. There's also Moondream 0. [00:23:00] 5b, which was released recently. It's like the smallest visual language model.

[00:23:04] Speaker: And as you can see, there isn't like a big trade off compared to Moondream 2b. So now I showed you that we have some really good small models. We also have the tools to use them, but why should you consider using small models and when? I think, like, small models are really interesting because of the on device feature.

[00:23:23] Speaker: Because these models are small and they can run fast, you can basically run them on your laptop, but also on your mobile phone. And this means that your dataset stays locally. You don't have to send your queries to third parties. And this really enhances privacy. That was, for example, one of the big selling points for Apple Intelligence.

[00:23:42] Speaker: Also, right now, we really have a lot of work to do. So many frameworks to do on device inference. For example, there's MLX, MLC, Llama, CPP, Transformers, JS. So we have a lot of options and each of them have like great features. So you have so many options for doing that. Small models are also really powerful if you choose to specialize them.[00:24:00]

[00:24:00] Speaker: For example, here there's a startup called Numind, which took small LM and then they fine tuned it on text extraction datasets. And they managed to get a model that's not very far from models that are much larger. So I think text extraction is like one use case where small models can be really performant and it makes sense to use them instead of just using larger models.

[00:24:19] Speaker: You can also chat with these models in browser. For example, here, you can go there, you can load the model, you can even turn off your internet and just start chatting with the model locally. Speaking of text extraction, if you don't want to fine tune the models, there's a really good method of structure generation.

[00:24:36] Speaker: We can basically force the models to follow a JSON schema that you defined. For example, here, we try to force the model to follow a schema for extracting key information from GitHub issues. So you can input free text, which is a complaint about a GitHub repository, something not working. And then you can run it there and the model can extract anything that is relevant for your GitHub issue creation.

[00:24:58] Speaker: For example, the [00:25:00] priority, for example, here, priority is high, the type of the issue bug, and then a title and the estimation of how long this will take to fix. And you can just like do this in the browser, you can transform your text into a GitHub issue that's properly formatted.

[00:25:14] What's Next

[00:25:14] Speaker: So what's next for synthetic data and small models?

[00:25:18] Speaker: I think that domain specific synthetic data is going to be, it's already important, it's going to be even more important. For example, generating synthetic data for math. I think this really would help improve the reasoning of a lot of models. And a lot of people are doing it, for example, Quint 2. 12 math, everyone's trying to reproduce a one.

[00:25:37] Speaker: And so I think for synthetic data, trying to specialize it on some domains is going to be really important. And then for small models, I think specializing them through fine tuning, it's also going to be really important because I think a lot of companies are just trying to use these large models because they are better.

[00:25:53] Speaker: But on some tasks, I think you can already get decent performance with small models. So you don't need to Pay like a [00:26:00] cost that's much larger just to make your model better at your task by a few percent. And this is not just for text. And I think it also applies for other modalities like vision and audio.

[00:26:11] Speaker: And I think you should also watch out for on device frameworks and applications. For example, like the app I showed, or lama, all these frameworks are becoming really popular and I'm pretty sure that we're gonna get like more of them in 2025. And users really like that. Maybe for other, I should also say hot take.

[00:26:28] Speaker: I think that like in AI, we just started like with fine tuning, for example, trying to make BERT work on some specific use cases, and really struggling to do that. And then we had some models that are much larger. So we just switched to like prompt engineering to get the models And I think we're going back to fine tuning where we realize these models are really costly.

[00:26:47] Speaker: It's better to use just a small model or try to specialize it. So I think it's a little bit of a cycle and we're going to start to see like more fine tuning and less of just like a prompt engineering the models. So that was my talk. Thank you for following. And if you have [00:27:00] any questions, we can take them now.

Happy holidays! We’ll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all all our LS supporters who helped fund the gorgeous venue and A/V production!

For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver.

Of perennial interest, particularly at academic conferences, is scaled-up architecture research as people hunt for the next Attention Is All You Need. We have many names for them: “efficient models”, “retentive networks”, “subquadratic attention” or “linear attention” but some of them don’t even have any lineage with attention - one of the best papers of this NeurIPS was Sepp Hochreiter’s xLSTM, which has a particularly poetic significance as one of the creators of the LSTM returning to update and challenge the OG language model architecture:

So, for lack of a better term, we decided to call this segment “the State of Post-Transformers” and fortunately everyone rolled with it.

We are fortunate to have two powerful friends of the pod to give us an update here:

* Together AI: with CEO Vipul Ved Prakash and CTO Ce Zhang joining us to talk about how they are building Together together as a quote unquote full stack AI startup, from the lowest level kernel and systems programming to the highest level mathematical abstractions driving new model architectures and inference algorithms, with notable industry contributions from RedPajama v2, Flash Attention 3, Mamba 2, Mixture of Agents, BASED, Sequoia, Evo, Dragonfly, Dan Fu's ThunderKittens and many more research projects this year

* Recursal AI: with CEO Eugene Cheah who has helped lead the independent RWKV project while also running Featherless AI. This year, the team has shipped RWKV v5, codenamed Eagle, to 1.5 billion Windows 10 and Windows 11 machines worldwide, to support Microsoft's on-device, energy-usage-sensitive Windows Copilot usecases, and has launched the first updates on RWKV v6, codenamed Finch and GoldFinch. On the morning of Latent Space Live, they also announced QRWKV6, a Qwen 32B model modified with RWKV linear attention layers.

We were looking to host a debate between our speakers, but given that both of them were working on post-transformers alternatives

Full Talk on Youtube

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Links

All the models and papers they picked:

* Earlier Cited Work

* Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

* Hungry hungry hippos: Towards language modeling with state space models

* Hyena hierarchy: Towards larger convolutional language models

* Mamba: Linear-Time Sequence Modeling with Selective State Spaces

* S4: Efficiently Modeling Long Sequences with Structured State Spaces

* Just Read Twice (Arora et al)

* Recurrent large language models that compete with Transformers in language modeling perplexity are emerging at a rapid rate (e.g., Mamba, RWKV). Excitingly, these architectures use a constant amount of memory during inference. However, due to the limited memory, recurrent LMs cannot recall and use all the information in long contexts leading to brittle in-context learning (ICL) quality. A key challenge for efficient LMs is selecting what information to store versus discard. In this work, we observe the order in which information is shown to the LM impacts the selection difficulty.

* To formalize this, we show that the hardness of information recall reduces to the hardness of a problem called set disjointness (SD), a quintessential problem in communication complexity that requires a streaming algorithm (e.g., recurrent model) to decide whether inputted sets are disjoint. We empirically and theoretically show that the recurrent memory required to solve SD changes with set order, i.e., whether the smaller set appears first in-context.

* Our analysis suggests, to mitigate the reliance on data order, we can put information in the right order in-context or process prompts non-causally. Towards that end, we propose: (1) JRT-Prompt, where context gets repeated multiple times in the prompt, effectively showing the model all data orders. This gives 11.0±1.3 points of improvement, averaged across 16 recurrent LMs and the 6 ICL tasks, with 11.9× higher throughput than FlashAttention-2 for generation prefill (length 32k, batch size 16, NVidia H100). We then propose (2) JRT-RNN, which uses non-causal prefix-linear-attention to process prompts and provides 99% of Transformer quality at 360M params., 30B tokens and 96% at 1.3B params., 50B tokens on average across the tasks, with 19.2× higher throughput for prefill than FA2.

* Jamba: A 52B Hybrid Transformer-Mamba Language Model

* We present Jamba, a new base large language model based on a novel hybrid Transformer-Mamba mixture-of-experts (MoE) architecture.

* Specifically, Jamba interleaves blocks of Transformer and Mamba layers, enjoying the benefits of both model families. MoE is added in some of these layers to increase model capacity while keeping active parameter usage manageable.

* This flexible architecture allows resource- and objective-specific configurations. In the particular configuration we have implemented, we end up with a powerful model that fits in a single 80GB GPU.

* Built at large scale, Jamba provides high throughput and small memory footprint compared to vanilla Transformers, and at the same time state-of-the-art performance on standard language model benchmarks and long-context evaluations. Remarkably, the model presents strong results for up to 256K tokens context length.

* We study various architectural decisions, such as how to combine Transformer and Mamba layers, and how to mix experts, and show that some of them are crucial in large scale modeling. We also describe several interesting properties of these architectures which the training and evaluation of Jamba have revealed, and plan to release checkpoints from various ablation runs, to encourage further exploration of this novel architecture. We make the weights of our implementation of Jamba publicly available under a permissive license.

* SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

* We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096×4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU. Core designs include:

* (1) Deep compression autoencoder: unlike traditional AEs, which compress images only 8×, we trained an AE that can compress images 32×, effectively reducing the number of latent tokens.

* (2) Linear DiT: we replace all vanilla attention in DiT with linear attention, which is more efficient at high resolutions without sacrificing quality.

* (3) Decoder-only text encoder: we replaced T5 with modern decoder-only small LLM as the text encoder and designed complex human instruction with in-context learning to enhance the image-text alignment.

* (4) Efficient training and sampling: we propose Flow-DPM-Solver to reduce sampling steps, with efficient caption labeling and selection to accelerate convergence.

* As a result, Sana-0.6B is very competitive with modern giant diffusion model (e.g. Flux-12B), being 20 times smaller and 100+ times faster in measured throughput. Moreover, Sana-0.6B can be deployed on a 16GB laptop GPU, taking less than 1 second to generate a 1024×1024 resolution image. Sana enables content creation at low cost.

* RWKV: Reinventing RNNs for the Transformer Era

* Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability.

* We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of transformers with the efficient inference of RNNs.

* Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference.

* We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers, suggesting future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.

* LoLCATs: On Low-Rank Linearizing of Large Language Models

* Recent works show we can linearize large language models (LLMs) -- swapping the quadratic attentions of popular Transformer-based LLMs with subquadratic analogs, such as linear attention -- avoiding the expensive pretraining costs. However, linearizing LLMs often significantly degrades model quality, still requires training over billions of tokens, and remains limited to smaller 1.3B to 7B LLMs.

* We thus propose Low-rank Linear Conversion via Attention Transfer (LoLCATs), a simple two-step method that improves LLM linearizing quality with orders of magnitudes less memory and compute.

* We base these steps on two findings.

* First, we can replace an LLM's softmax attentions with closely-approximating linear attentions, simply by training the linear attentions to match their softmax counterparts with an output MSE loss ("attention transfer").

* Then, this enables adjusting for approximation errors and recovering LLM quality simply with low-rank adaptation (LoRA).

* LoLCATs significantly improves linearizing quality, training efficiency, and scalability. We significantly reduce the linearizing quality gap and produce state-of-the-art subquadratic LLMs from Llama 3 8B and Mistral 7B v0.1, leading to 20+ points of improvement on 5-shot MMLU.

* Furthermore, LoLCATs does so with only 0.2% of past methods' model parameters and 0.4% of their training tokens.

* Finally, we apply LoLCATs to create the first linearized 70B and 405B LLMs (50x larger than prior work).

* When compared with prior approaches under the same compute budgets, LoLCATs significantly improves linearizing quality, closing the gap between linearized and original Llama 3.1 70B and 405B LLMs by 77.8% and 78.1% on 5-shot MMLU.

Timestamps

* [00:02:27] Intros

* [00:03:16] Why Scale Context Lengths? or work on Efficient Models

* [00:06:07] The Story of SSMs

* [00:09:33] Idea 1: Approximation -> Principled Modeling

* [00:12:14] Idea 3: Selection

* [00:15:07] Just Read Twice

* [00:16:51] Idea 4: Test Time Compute

* [00:17:32] Idea 2: Hardware & Kernel Support

* [00:19:49] RWKV vs SSMs

* [00:24:24] RWKV Arch

* [00:26:15] QWRKWv6 launch

* [00:30:00] What's next

* [00:33:21] Hot Takes - does anyone really need long context?

Transcript

[00:00:00] AI Charlie: We're back at Latent Space Live, our first mini conference held at NeurIPS 2024 in Vancouver. This is Charlie, your AI co host. As a special treat this week, we're recapping the best of 2024 going domain by domain. We sent out a survey to the over 900 of you who told us what you wanted, and then invited the best speakers in the Latent Space Network to cover each field.

[00:00:24] AI Charlie: 200 of you joined us in person throughout the day, with over 2200 watching live online. Thanks Our next keynote covers the State of Transformers alternative architectures, with a special joint presentation with Dan Fu of Together AI and Eugene Chia of Recursal AI and Featherless AI. We've featured both Together and Recursal on the pod before, with CEO Veepal Vedprakash introducing them.

[00:00:49] AI Charlie: And CTO CE Zhang joining us to talk about how they are building together together as a quote unquote full stack AI startup from the lowest level kernel and systems [00:01:00] programming to the highest level mathematical abstractions driving new model architectures and inference algorithms with notable industry contributions from Red Pajama V2, Flash Attention 3, Mamba 2, Mixture of Agents.

[00:01:15] AI Charlie: Based, Sequoia, Evo, Dragonfly, Danfoo's Thunder Kittens, and many more research projects this year. As for Recursal and Featherless, we were the first podcast to feature RWKV last year, and this year the team has shipped RWKV v5, codenamed Eagle, to 1. 5 billion Windows 10 and Windows 11 machines worldwide to support Microsoft's on device, end Energy Usage Sensitive Windows Copilot Use Cases and has launched the first updates on RWKV v6, codenamed Finch and Goldfinch.

[00:01:53] AI Charlie: On the morning of Latent Space Live, they also announced QRdata UKv6, a QEN32B model [00:02:00] modified with RDWKV linear attention layers. Eugene has also written the most single most popular guest post on the Latent Space blog this year. Yes, we do take guest posts on what he has discovered about the H100 GPU inference NeoCloud market since the successful launch of Featherless AI this year.

[00:02:20] AI Charlie: As always, don't forget to check the show notes for the YouTube link to their talk as well as their slides. Watch out and take care.

[00:02:27] Intros

[00:02:27] Dan Fu: Yeah, so thanks so much for having us. So this is going to be a little bit of a two part presentation. My name is Dan. I'm at Together AI, and I'll be joining UCSD as faculty in about a year. And Eugene, you want to introduce yourself?

[00:02:46] Eugene Cheah: Eugene, I lead the art activity team, and I, I'm CEO of Featherless, and we both work on this new post transformer architecture space.

[00:02:55] Dan Fu: Yeah, so yeah, so today we're really excited to talk to you a little bit [00:03:00] about that. So first I'm going to give a broad overview of kind of the last few years of progress in non post transformer architectures. And then afterwards Eugene will tell us a little bit about the latest and the greatest and the latest frontier models in this space.

[00:03:16] Why Scale Context Lengths? or work on Efficient Models

[00:03:16] Dan Fu: So, the story starts with Scaling. So this is probably a figure or something like this that you've seen very recently. Over the last five to six years, we've seen models really scale up in parameter size, and that's brought with it a bunch of new capabilities, like the ability to talk to you and tell you sometimes how to use your Colab screens.

[00:03:35] Dan Fu: But another place where we've seen scaling especially recently is scaling in context length. So this can mean Having more text inputs for your models, but it can also mean things like taking a lot of visual token inputs image inputs to your models or generating lots of outputs. And one thing that's been really exciting over the last few months or so is that we're, we're seeing scaling, not only during training time, but also [00:04:00] during test time.

[00:04:00] Dan Fu: So this is one of the, the, this is the iconic image from the OpenAI 01 release. Not only are we starting to scale train time compute, but we're also starting to scale test time compute. Now if you're familiar with our attention and our transformer architectures today, this graph on the right might look a little bit scary.

[00:04:19] Dan Fu: And one of the reasons is that the implications are a little bit Interesting. So what does it mean if we want to continue having smarter and smarter models? Do we just need to start building bigger, bigger data centers, spending more flops? Is this this little Dolly 3, we need more flops, guys? Is this going to be the future of all of AI?

[00:04:39] Dan Fu: Or is there a better way, another path forward? Maybe we can get the same capabilities that we've gotten used to, But for a lot less compute, a lot less flops. And one of the things that we're going to talk about today is specifically looking at that core attention operator in some of these models.

[00:04:57] Dan Fu: And the reason is that so this is just some, some [00:05:00] basic you know, scaling curves, but attention has compute that scales quadratically in the context length. So that means that if you're doing something like test time compute and you want to spend a bunch of tokens thinking about what comes next, the longer that that goes the, the, the more tokens you spend on that, that compute grows quadratically in that.

[00:05:19] Dan Fu: One of the questions that we're interested in is, can we take that basic sequence model, that basic sequence primitive at the bottom, and get it to scale better? Can we scale in, let's say, n to the 3 halves or n log n? So in, in the first part of the talk, so we just went over the introduction. What I'm gonna do over the next few slides is just talk about some of the key advances and ideas that have shown over the past few years since maybe early 2020 to, to now that shown promise that this might actually be possible.

[00:05:48] Dan Fu: That you can actually get potentially the same quality that we want while scale, while scaling better. So to do that, we're and, and basically the, the story that we're gonna look is we're gonna start to see [00:06:00] how. So this is a basic graph of just the past couple years of progress of perplexity where that blue line, that dotted blue line, is attention.

[00:06:07] The Story of SSMs

[00:06:07] Dan Fu: It's your basic transformer, full dense attention. And then the dots coming down are some of the methods that you'll see in this presentation today. We're going to turn the clock back all the way to 2020. So this, this, this question of can we make attention subquadratic? Basically, as soon as we said attention is all you need, People started asking this question.

[00:06:28] Dan Fu: So we have this quadratic attention operator. Can we do better? I'll briefly talk about why attention is quadratic. And the basic thing that happens, if you're not familiar, is that you have these inputs, these keys and queries. And what you do in this attention matrix, this S matrix over here, is that you're using, you're comparing every token in your input to every other token.

[00:06:49] Dan Fu: So when I try to do something like upload a whole book to Gemini, what happens beyond the Maybe not Gemini, because we don't necessarily know what architecture is. But let's say we upload it to LLAMA, what happens beyond [00:07:00] the scenes, behind the scenes, is that it's going to take every single word in that book and compare it to every other word.

[00:07:05] Dan Fu: And this has been a really, it's, it's led to some pretty impressive things. But it's kind of a brute forcing of the way that you would try to interpret a interpret something. And what attention does in particular is the, and then what attention, sorry, don't want to. Okay, no, no laser pointer. What, what attention does afterwards is that instead of always operating in this quadratic thing, it takes a row wise softmax over this matrix, and then multiplies it by this values matrix.

[00:07:32] Dan Fu: So, one of the key points to notice is that the output size is always going to be the same as the inputs, at least in standard self attention. So one of the first things that folks tried to do around 2020 is this thing called linear attention, which is just, just noticing that if we take out this softmax from here, if we take out this non linearity in the middle of the attention operation, and then if you compute the keys and the values operation first, you actually never hit this quadratic bottleneck.

[00:07:57] Dan Fu: So that, that's potentially a way [00:08:00] to get a lot more computationally efficient. And there are various ways to do this by basically using feature maps or try to approximate this overall attention computation. But some of this work sort of started to hit a wall in 2020. And the basic challenges were, were two.

[00:08:16] Dan Fu: So one was quality. It was back then, it was kind of hard to, to get good quality with these linear attention operators. The other one was actually hardware efficiency. So these, this feature map that was just shown by a simplify simplify here. Actually ends up being quite computationally expensive if you just implement it naively.

[00:08:34] Dan Fu: So you started having these operators that not only were you sure, you're not really sure if they have the same quality, but also they're actually just wall clock slower. So you kind of end up getting the worst of both worlds. So this was the the stage. So that kind of sets the stage for four years ago.

[00:08:49] Dan Fu: Keep this in mind because linear attention is actually going to come back in a few years once we have a better understanding. But one of the works that started kicking off this, this [00:09:00] mini revolution in post transformer architectures was this idea called states based model. So here the seminal work is, is one about our work queue in 2022.

[00:09:09] Dan Fu: And this, this piece of work really brought together a few ideas from, from some long running research research lines of work. The first one was, and this is really one of the keys to, to closing the gap in quality was just using things that, that if you talk to a, a, an electrical engineer off the street, they might know off, off the, like the back of their hand.

[00:09:33] Idea 1: Approximation -> Principled Modeling

[00:09:33] Dan Fu: But taking some of those properties with how we model dynamical systems in signal processing and then using those ideas to model the inputs, the, the text tokens in, for example a transformer like Next Token Prediction Architecture. So some of those early states-based model papers were looking at this relatively, relatively simple recurrent update model that comes from maybe chapter one of a signal processing class.

[00:09:59] Dan Fu: But then using [00:10:00] some principle theory about how you should do that recurrent update in order to really get the most that you can out of your hidden state, out of your out of your sequence. So that, that was one key idea for quality and. When this was eventually realized, you started to see a bunch of benchmarks that were pretty sticky for a few years.

[00:10:20] Dan Fu: Things like long range arena, some long sequence evaluation benchmarks, There was stuff in time series, time series analysis. They started to, you started to see the quality tick up in meaningful ways. But the other key thing that What's so influential about these states based models is that they also had a key idea about how you can compute these things efficiently.

[00:10:45] Dan Fu: So if you go back to your machine learning 101 class where you learned about RNNs, one thing that you may have learned is that they don't paralyze as well as detention, because if you just run them naively, you have to do this kind of sequential update to process new tokens, [00:11:00] whereas in attention, you can process all the tokens in parallel at one time.

[00:11:04] Dan Fu: One of the key insights behind the S4 paper was that these recurrent models, you could take them and you could also formulate them as a convolution. And in particular, with a convolution, you could, instead of using a PyTorch conv1d operation, you can compute that with the FFT. And that would give you n log n compute in the in the sequence length n with an operator that was relatively well optimized for modern hardware.

[00:11:28] Dan Fu: So those are really, I'd say, the two key ideas in 2022 that started allowing these breakthroughs to happen in these non transformer architectures. So, these ideas about how to principally model sorry, how to model the recurrent updates of a mo of, of a sequence in a principled way, and also these key ideas in how you can compute it efficiently by turning it into a convolution and then scaling it up with the FFT.

[00:11:53] Dan Fu: Along those same lines, so afterwards we started putting out some work on specialized kernels, so just [00:12:00] like we have flash attention for transformers, we also have works like flash fft conf, and if you look at these lines of work oftentimes when, whenever you see a new architecture, you see a new primitive one of the, one of the table stakes now is, do you have an efficient kernel so that you can actually get wall clock speed up?

[00:12:14] Idea 3: Selection

[00:12:14] Dan Fu: So by 2022, We are starting to have these models that had promising quality primitives, but and, and also promising wall clocks. So you could actually see regimes where they were better than transformers in meaningful ways. That being said, there were, there's still sometimes a quality gap, particularly for language modeling.

[00:12:33] Dan Fu: And because languages, It's so core to what we do in sequence modeling these days the, the next, the next key idea that I'm going to talk about is this idea of selection mechanisms. And this is basically an idea of, so you have this recurrent state that you're keeping around that just summarizes everything that, that came before.

[00:12:50] Dan Fu: And to get a good sequence model, one of the things that you really need to be able to do is have the model learn what's the best way to pick out pieces from that recurrent [00:13:00] state. So one of the, one of the major ideas here in a line of work called H3, Hungry Hungry Hippos, and also these hyena models were One way you can do this is by just adding some simple element wise gates.

[00:13:13] Dan Fu: So versions of these ideas have been around for decades. If you squint at the LSTM paper you, you can probably find, find this gating mechanism. But turns out you can take those old ideas, add them into these new. state space models, and then you can see quality start to pick up. If you've heard of the Mamba model, this also takes the selection to the next level by actually making some changes in that fundamental recurrent state space.

[00:13:40] Dan Fu: So, it's not only just this gating that happens around the SSM layer, but also you can actually make The ABCD matrices of your state space model, you can make them data dependent, which will allow you to even better select out different pieces from your hidden state depending on what you're seeing. I'll also point out if you look at the [00:14:00] bottom right of this figure, there's this little triangle with a GPU SRAM, GPU HBM, and this, this is just continuing that trend of when you have a new architecture you, you, you also release it with a kernel to, to, to show that it is hardware efficient, that it, that it can be hardware efficient on modern hardware.

[00:14:17] Dan Fu: The, the, one of the next cool things that happened is once we had this understanding of these are the basic pieces, these are the basic principles behind some of the sequence models linear attention actually started to come back. So in earlier this year, there was a model called BASED the, from Simran Arora and, and some other folks, that combined a more principled version of linear attention that basically the, the, the, the two second summary is that it used a Taylor approximation of the softmax attention, combined that with a simple sliding window attention and was starting to able, starting to be able to expand the Pareto frontier of how much data can you recall from your sequence, versus how small is your recurrent state size.

[00:14:58] Dan Fu: So those orange dots [00:15:00] are, at the top there, are just showing smaller sequences that can recall more memory.

[00:15:07] Just Read Twice

[00:15:07] Dan Fu: And the last major idea I think that has been influential in this line of work and is very relatively late breaking just a few months ago, is just the basic idea that when you have these models that are fundamentally more efficient in the sequence length, you maybe don't want to prompt them or use them in exactly the same way.

[00:15:26] Dan Fu: So this was a really cool paper called Just Read Twice, also from Simran. That basically said, hey, all these efficient models can process tokens so much more efficiently than transformers that they can sometimes have unfair advantages compared to a simple transformer token. So, or sorry, a simple transformer model.

[00:15:44] Dan Fu: So take, for example the standard, the standard use case of you have some long document, you're going to pass it in as input, and then you're going to ask some question about it. One problem you might imagine for a recurrent model where you have a fixed state size is, let's say that [00:16:00] you're. Article is very long, and you're trying to ask about some really niche thing.

[00:16:04] Dan Fu: You can imagine it might be hard for the model to know ahead of time what information to put into the hidden state. But these, these, these models are so much more efficient that you can do something really stupid, like, you can just put the document write down the document, write down the question, write down the document again, and then write down the question again, and then this time, the second time that you go over that document, you know exactly what to look for.

[00:16:25] Dan Fu: And the cool thing about this is, so this is, And this this results in better quality, especially on these recall intensive tasks. But the other interesting thing is it really takes advantage of the more efficient architectures that, that we're having here. So one of the other, I think, influential ideas in this line of work is if you change the fundamental compute capabilities of your model and the way that it scales, you can actually start to query it at test time differently.

[00:16:51] Idea 4: Test Time Compute

[00:16:51] Dan Fu: And this actually, of course, goes back to those slides on test time compute. So while everybody's looking at, say, test time compute for big transformer models, [00:17:00] I think potentially a really interesting research question is, how can you take those and how does it change with this new next generation of models?

[00:17:09] Dan Fu: So the, I'll just briefly summarize what some of those key ideas were and then talk and then show you briefly kind of what the state of the art is today. So, so the four key ideas are instead of just doing a simple linear attention approximation, instead take ideas that we know from other fields like signal processing, do a more principled approach to your modeling of the sequence.

[00:17:32] Idea 2: Hardware & Kernel Support

[00:17:32] Dan Fu: Another key idea throughout all these lines of work is you really want. Hardware and kernel support from day one. So, so even if your model is theoretically more efficient if somebody goes and runs it and it's two times slower one of the things that, that we've learned is that if, if you're in that situation, it's, it's just gonna be dead on arrival.

[00:17:49] Dan Fu: So you want to be designing your architectures one of the key, key machine learning ideas that has been important for the quality is just making sure that you encode different ways that you can [00:18:00] select from your hidden state and, and really focus on that as a key decider of quality. And finally, I think one of the, the, the emerging new, new things for, for this line of work and something that's quite interesting is, What are the right test time paradigms for these models?

[00:18:15] Dan Fu: How do they change relative to relative to what you might do for a standard transformer? I'll briefly end this section. So I've labeled this slide where we are yesterday because Eugene is going to talk about some new models that he released literally this morning. But as of yesterday, some of the really cool results out of the, these efficient alternative models were so AI2 trained this hybrid MOE called Jamba.

[00:18:40] Dan Fu: That, that, that seems, that is currently the state of the art for these non transformer architectures. There's this NVIDIA and MIT put out this new diffusion model called SANA recently that one of their key key observations is that you can take a standard diffusion transformer diffusion model, replace the layers with linear [00:19:00] attention, and then that lets you scale to much larger much larger images, much, much Much larger sequences more efficiently.

[00:19:07] Dan Fu: And and one thing that I don't think anybody would have called when a few years ago is that one of those gated SSM, gated states based models ended up on the cover of Science because a great group of folks went and trained some DNA models. So that's Michael Polley, Eric Yuen from from Stanford and the Arc Institute.

[00:19:26] Dan Fu: So it's, we're really at an exciting time in 2024 where these non transformer, post transformer architectures are showing promise across a wide range. Across a wide range of, of modalities, of applications, and, and of tasks. And with that, I'll pass it on to Eugene, who can tell you a little bit about the latest and greatest with RWKV.

[00:19:49] RWKV vs SSMs

[00:19:49] Eugene Cheah: So, that's useful? Yeah. You're talking to here. Oh, I'm talking to here. Okay. So, yeah, two streams. Yeah. So, I think one common questions that we tend to get asked, right, is what's the difference between [00:20:00] RWKV and state space? So I think one of the key things to really understand, right the difference between the two groups, right, is that we are actually more like an open source, random internet meets academia kind of situation.

[00:20:11] Eugene Cheah: Like, most of us never wrote any paper, but we, we basically look at RNNs and linear intention when intention is all you need came out, and then we decided to like, hey there is a quadratic scaling problem. Why don't we try fixing that instead? So, so, so we end up developing our own branch, but we end up sharing ideas back and forth.

[00:20:30] Eugene Cheah: So, and, and we do all this actively in Discord, GitHub, etc. This was so bad for a few years, right, that basically, the average group's H index was so close to zero, right, Illuter. ai actually came in and helped us write our first paper. Great, now our H index is now three, apparently. So, so, so, but, but the thing is, like, a lot of these experiments led to results, and, and, essentially, essentially, we we took the same ideas from linear attention, [00:21:00] and we built on it.

[00:21:01] Eugene Cheah: So, to take a step back into, like, how does RWKB handle its own attention mechanic and achieve the same goals of, like, O and compute, respectively, and in focus of our overall goal to make AI accessible to everyone, regardless of language, nation, or compute, that's our goal. We actually train our models primarily on over a hundred languages, which is another topic altogether.

[00:21:23] Eugene Cheah: And our goal is to train to even 200 languages to cover all languages in the world. But at the same time, we work on this architecture, To lower the compute cost so that people can run it on Raspberry Pis and on anything. So, how did RWKB break the dependency of LSTM token flow? Because I think to understand architecture, right, it's probably easier to understand it from the RNN lens.

[00:21:46] Eugene Cheah: Because that's where we built on. We all, we all state space kind of like try to, try to start anew and took lessons from that and say, So there's a little bit of divergence there. And AKA, this our version of linear attention. So to take step back [00:22:00] all foundation models, be it transformers or non transformers at a very high level, right?

[00:22:05] Eugene Cheah: Pumps in the token. I mean, text that things into embeddings and go through a lot of layers. Generate a lot of states where the QKV cache or be iron in states or RW KB states. And outputs and embedding, they are not the same thing. And we just take more layers and more embeddings. And somehow that magically works.

[00:22:23] Eugene Cheah: So, if you, if you remember your ancient RNN lessons which we, which we, which we we call best learning these days the general idea is that you have the embedding information flowing all the way up, and when, and you take that information and you flow it back down, and then you process it as part of your LSTM layers.

[00:22:41] Eugene Cheah: So, this is how it generally works. Kapati is quoted saying that RNNs are actually unreasonably effective. The problem is this is not scalable. To start doing work on the second token, you need to wait for the first token. And then you need to, and likewise for the third token and fourth token, yada yada.

[00:22:55] Eugene Cheah: That is CPU land, not GPU land. So, so, so, you [00:23:00] can have a H100 and you can't even use 1 percent of it. So, so that's kind of why RNNs didn't really take off in the direction that we wanted, like, billions of parameters when it comes to training. So, what did RDAP KV version 0 do? Boom. We just did the dumbest, lamest thing.

[00:23:13] Eugene Cheah: Sorry, this is the bottleneck for RNN. We did the dumb thing of removing that line. And it kind of worked. It trained. It sucked, but it kind of worked. Then we were like, hey, then no one cared because the loss was crap, but how do we improve that? And that's essentially where we move forward, because if you see this kind of flow, right, you can actually get your GPU saturated quickly, where it essentially cascades respectively.

[00:23:41] Eugene Cheah: So I'm just waiting for this to loop again. So it's like, once you get your first layer, your token to be computed finish. You start to cascade your compute all the way until you are, Hey, I'm using 100 percent of the GPU. So we, we worked on it, and we started going along the principle of that as long as we keep this general architecture [00:24:00] where, where we can cascade and, and be highly efficient with our architecture, nothing is sacred in our architecture.

[00:24:06] Eugene Cheah: And we have done some crazy ideas. In fact, you ask us, if you ask me to explain some things in the paper, right, officially in the paper, I'll say we had this idea and we wrote it this way. The reality is someone came with a code, we tested it, it worked, and then we rationalized later. So, so the general

[00:24:24] RWKV Arch

[00:24:24] Eugene Cheah: The idea behind rwkbr is that we generally have two major blocks that we do.

[00:24:30] Eugene Cheah: We call time mix and channel mix. And time mix generally handles handles long term memory states, where essentially, where essentially where we apply the matrix multiplication and Cilu activation functions into processing an input embedding and an output embedding. I'm oversimplifying it because this, This calculation changed every version and we have, like, version 7 right now.

[00:24:50] Eugene Cheah: ChannelMix is similar to Base in the sense that it does shorter term attention, where it just looks at the sister token, or the token before it, because [00:25:00] there's a shift in the token shift matrix. I don't really want to go too much into the papers itself, because, like, we do have three papers on this.

[00:25:09] Eugene Cheah: Basically, RWKB, RNN for the transformer, ERA, Ego and Pinch, RWKB, Matrix Value State. This is the updated version 5, version 6. And Goldfinch is our, is, is, is, is our hybrid model respectively. We are writing the paper already for V seven and which is, which is for R wk V seven. Called, named Goose, or architectures are named by Bird.

[00:25:30] Eugene Cheah: And, I'm going to cover as well, qrwkb, and mama100k, and rwkb, and Where did that lead to? Great! Because we are all GPU poor and to be clear, like, most of this research is done, like, only on a handful H100s, which I had one Google researcher told me that was, like, his experiment budget for a single researcher.

[00:25:48] Eugene Cheah: So, our entire organization has less compute than a single researcher in Google. So We, we, one of the things that we explored into was to how do we convert transformer models instead? Because [00:26:00] someone already paid that billion dollars, a million dollars onto training, so why don't we take advantage of those weights?

[00:26:05] Eugene Cheah: And, and to, I believe, together AI worked on the lockets for, for the Lambda side of things, and, and we took some ideas from there as well, and we essentially did that for RWKB.

[00:26:15] QWRKWv6 launch

[00:26:15] Eugene Cheah: And that led to, Q RWKB6, which we just dropped today, a 32 bit instruct preview model, where we took the Quen 32 bit instruct model, freeze the feedforward layer, remove the QKB attention layer, and replace it with RWKB linear layers.

[00:26:32] Eugene Cheah: So to be clear, this means we do not have the rwkv channel mix layer, we only have the time mix layer. But but once we do that, we train the rwkv layer. Important is that the feedforward layer needs to be frozen, so the new attention can be learned. And then we unfreeze the feedforward layer, and train all the layers together with a custom learning rate schedule, so that they can learn how to work together.

[00:26:54] Eugene Cheah: The end result, surprisingly, And, to be honest, to the frustration of the R. W. [00:27:00] KV MOE team, which ended up releasing the model on the same day, was that, with just a few hours of training on two nodes, we managed to get it to be on par, kind of, with the original QUAN32B model. So, in fact, when the first run, right, that completely confused us, it was like, and I was telling Daniel Goldstein, Smirky, who kind of leads most of our research coordination, When you pitched me this idea, you told me at best you'll get the same level of performance.

[00:27:26] Eugene Cheah: You didn't tell me the challenge and score and Winograd score will shoot up. I don't know what's happening there. But it did. MMLU score dropping, that was expected. Because if you think about it, when we were training all the layers, right, we were essentially Like, Frankenstein this thing, and we did brain damage to the feedforward network layer 2 with the new RWKB layers.

[00:27:47] Eugene Cheah: But, 76%, hey, somehow it's retained, and we can probably further train this. We didn't even spend more than 3 days training this, so there's a lot more that can be done, hence the preview. This brings up [00:28:00] a big question, because We are already now in the process of converting to 7TB. We are now, this is actually extremely compute efficient to test our attention mechanic.

[00:28:10] Eugene Cheah: It's like, it becomes a shortcut. We can, we are already planning to do our version 7 and our hybrid architecture for it. Because we don't need to train from scratch. And we get a really good model out of it. And the other thing that is uncomfortable to say is that because we are doing right now on the 70b is that if this scales correctly to 128k context length, I'm not even talking about a million 128, majority of enterprise workload today is just on 70b at under 32k context length.

[00:28:41] Eugene Cheah: That means if this works and the benchmark matches it, It means we can replace the vast majority of current AI workload, unless you want super long context. And then sorry, can someone give us more GPUs? Because we do need the VRAM for super long context, sadly. So yeah, that's what we are working on, and essentially, [00:29:00] we are excited about this to just push it further.

[00:29:02] Eugene Cheah: And this conversion process, to be clear, I don't think it's going to be exclusive to RWKB. It probably will work for Mamba as well, I don't see why not. And we will probably see more ideas, or more experiments, or more hybrids, or Yeah, like, one of the weirdest things that I wanted to say outright, and I confirmed this with the Black Mamba team and the Jamba team, which because we did the GoFinch hybrid model, is that none of us understand why a hard hybrid with a state based model to be R.

[00:29:28] Eugene Cheah: QA state space and transformer performs better when, than the baseline of both. It's like, it's like when you train one, you expect, and then you replace, you expect the same results. That's our pitch. That's our claim. But somehow when we jam both together, it outperforms both. And that's like one area of emulation that, like, we only have four experiments, plus four teams, that a lot more needs to be done.

[00:29:51] Eugene Cheah: But, but these are things that excite me, essentially, because that is what it's potentially we can move ahead for. Which brings us to what comes next.

[00:30:00] What's next

[00:30:00] [00:30:00]

[00:30:00] Dan Fu: So, this part is kind of just some, where we'll talk a little bit about stuff that, that we're excited about. Maybe have some wild speculation on, on what, what's, what's coming next.

[00:30:12] Dan Fu: And, of course this is also the part that will be more open to questions. So, a couple things that, that I'm excited about is continued hardware model co design for, for these models. So one of the things that we've put out recently is this library called ThunderKittens. It's a CUDA library.

[00:30:29] Dan Fu: And one of the things that, that we found frustrating is every time that we built one of these new architectures, and I'm sure you had the exact same experience, we'd have to go and spend two months in CUDA land, like writing these, these new efficient things. And. If we decided to change one thing in PyTorch, like one line of PyTorch code is like a week of CUDA code at least.

[00:30:47] Dan Fu: So one of our goals with, with a library like Thunderkitten, so we, we just broke down what are the key principles, what are the key hardware things what are the key, Compute pieces that you get from the hardware. So for example on [00:31:00] H100 everything is really revolves around a warp group matrix multiply operation.

[00:31:06] Dan Fu: So you really want your operation to be able to split into relatively small matrix, matrix multiply operations. So like multiplying two 64 by 64 matrices, for example. And so if you know that ahead of time when you're designing your model, that probably gives you you know, some information about how you set the state sizes, how you set the update, how you set the update function.

[00:31:27] Dan Fu: So with Thunderkittens we basically built a whole library just around this basic idea that all your basic compute primitives should not be a float, but it should be a matrix, and everything should just be matrix compute. And we've been using that to, to try to both re implement some existing architectures, and also start to design code.

[00:31:44] Dan Fu: Some new ones that are really designed with this core with a tensor core primitive in mind. Another thing that that we're, that at least I'm excited about is we, over the last four or five years, we've really been looking at language models as the next thing. But if you've been paying [00:32:00] attention to Twitter there's been a bunch of new next generation models that are coming out.

[00:32:04] Dan Fu: So there, there are. So, video generation models that can run real time, that are supported by your mouse and your keyboard, that I'm told if you play with them that, you know, that they only have a few seconds of memory. Can we take that model, can we give it a very long context length so that you could actually maybe generate an entire game state at a time?

[00:32:25] Dan Fu: What does that look like for the model? You're certainly not going to do a giant quadratic attention computation to try to run that. Maybe, maybe use some of these new models, or some of these new video generation models that came out. So Sora came out I don't know, two days ago now. But with super long queue times and super long generation times.

[00:32:43] Dan Fu: So that's probably a quadratic attention operation at the, at the bottom of it. What if we could remove that and get the same quality, but a lot faster generation time? Or some of the demos that we saw from Paige earlier today. You know, if I have a super long conversation with my [00:33:00] Gemini bot, what if I wanted to remember everything that it's seen in the last week?

[00:33:06] Dan Fu: I mean, maybe you don't for personal reasons, but what if I did, you know? What does that mean for the architecture? And I think, you know, that's certainly something I'm pretty excited about. I'm sure you're excited about it too. So, I think we were supposed to have some hot takes, but I honestly don't remember what our hot takes were.

[00:33:21] Hot Takes - does anyone really need long context?

[00:33:21] Eugene Cheah: Yeah, including the next slide. Hot takes, yes, these are our

[00:33:25] Dan Fu: hot takes.

[00:33:25] Eugene Cheah: I think the big one on Twitter that we saw, that we shared, was the question is like, is RAG relevant? In the case of, like, the future of, like, state based models?

[00:33:38] Dan Fu: Let's see, I haven't played too much with RAG. But when I have. I'll say I found it was a little bit challenging to do research on it because we had this experience over and over again, where you could have any, an embedding model of any quality, so you could have a really, really bad embedding model, or you could have a really, really [00:34:00] good one, By any measure of good.

[00:34:03] Dan Fu: And for the final RAG application, it kind of didn't matter. That's what I'll say about RAG while I'm being recorded. I know it doesn't actually answer the question, but

[00:34:13] Eugene Cheah: Yeah, so I think a lot of folks are like, extremely excited of the idea of RWKB or State Space potentially having infinite context.

[00:34:21] Eugene Cheah: But I think the reality is that when we say infinite context, we just mean a different kind of infinite context, or you, or as it's previously covered, you need to test the model differently. So, think of it more along the lines of the human. Like, I don't remember what I ate for breakfast yesterday.

[00:34:37] Eugene Cheah: Yeah, that's the statement that I'll say. And And we humans are not quadratic transformers. If we did, if let's say we increased our brain size for every second we live, we would have exploded by the time we are 5 years old or something like that. And, and I think, I think basically fundamentally for us, right, be it whether we, regardless of whether RWKB, statespace, XLSTM, [00:35:00] etc, our general idea is that instead of that expanding state, that increase in computational cost, what if we have a fixed state size?

[00:35:08] Eugene Cheah: And Information theory detects that that fixed state size will have a limit. Just how big of a limit is a question, like, we, like, RWKB is running at 40 megabytes for, for its state. Its future version might run into 400 megabytes. That is like millions of tokens in, if you're talking about mathematically, the maximum possibility.

[00:35:29] Eugene Cheah: It's just that I guess we were all more inefficient about it, so maybe we hit 100, 000. And that's kind of like the work we are doing, trying to like push it and maximize it. And that's where the models will start differing, because it will choose to forget things, it will choose to remember things. And that's why I think that there might be some element of right, but it may not be the same right.

[00:35:49] Eugene Cheah: It may be the model learn things, and it's like, hmm, I can't remember that, that article. Let me do a database search, to search. Just like us humans, when we can't remember the article in the company. We do a search on Notion. [00:36:00]

[00:36:00] Dan Fu: I think something that would be really interesting is if you could have facts that are, so right now, the one intuition about language models is that all those parameters are around just to store random facts about the world.

[00:36:14] Dan Fu: And this intuition comes from the observation that if you take a really small language model, it can do things like talk to you, or kind of has like the The style of conversation, it can learn that, but where it will usually fall over compared to a much larger one is it'll just be a lot less factual about things that it knows or that it can do.

[00:36:32] Dan Fu: But that points to all those weights that we're spending, all that SGD that we're spending to train these models are just being used to store facts. And we have things like databases that are pretty good at storing facts. So I think one thing that would be really interesting is if we could actually have some sort of outside data store that a language model can can look at that that maybe is you know, has has some sort of gradient descent in it, but but would be quite interesting.

[00:36:58] Dan Fu: And then maybe you could edit it, delete [00:37:00] facts, you know, change who's president so that it doesn't, it doesn't get lost.

[00:37:04] Vibhu: Can we open up Q& A and hot takes for the audience? I have a hot take Q& A. Do these scale? When, when 405B state space model, RAG exists, no one does long context, who's throwing in 2 million token questions, hot takes?

[00:37:24] Dan Fu: The, the who's throwing in 2 million token question, I think, is, is a really good question. So I actually, I was going to offer that as a hot take. I mean, my hot take was going to be that long context doesn't matter. I know I just gave a whole talk about it, but you know, what, what's the point of doing research if you can't, you know, play both sides.

[00:37:40] Dan Fu: But I think one of the, so I think for both of us, the reason that we first got into this was just from the first principled questions of there's this quadratic thing. Clearly intelligence doesn't need to be quadratic. What is going on? Can we understand it better? You know, since then it's kind of turned into a race, which has [00:38:00] been exciting to watch, like, how much context you can take in.

[00:38:03] Dan Fu: But I think it's right. Nobody is actually putting in a two million context prompt into these models. And, and, you know, if they are, maybe we can go, go You know, design a better model to do that particular thing. Yeah, what do you think about that? So you've also been working on this. Do you think long context matters?

[00:38:19] Eugene Cheah: So I'm going to burn a bit. How many of you remember the news of Google Gemini supporting 3 million contacts, right? Raise your hand.

[00:38:28] Vibhu: Yeah, 2 million.

[00:38:29] Eugene Cheah: Oh, it's 2 million.

[00:38:31] Eugene Cheah: Yeah, how many of you actually tried that? See?

[00:38:34] Vibhu: I use it a lot. You? You work for MindsTV. I use it a lot.

[00:38:41] Eugene Cheah: So, for some people that has used, and I think, I think that's the, that's might be, like, this is where my opinion starts to differ, because I think the big labs may have a bigger role in this, because Like, even for RWKB, even when we train non contacts, the reason why I say VRAM is a problem is that because when we did the, we need to backprop [00:39:00] against the states, we actually need to maintain the state in between the tokens by the token length.

[00:39:05] Eugene Cheah: So that means we need to actually roll out the whole 1 million contacts if we are actually training 1 million. Which is the same for transformers, actually, but it just means we don't magically reuse the VRAM consumption in the training time space. So that is one of the VRAM bottlenecks, and I'm neither OpenAI nor Google, so donate GPUs if you have too much of them.

[00:39:27] Eugene Cheah: But then, putting it back to another paradigm, right, is that I think O1 style reasoning might be actually pushing that direction downwards. In my opinion, this is my partial hot take is that if, let's say you have a super big model, And let's say you have a 70B model that may take double the tokens, but gets the same result.

[00:39:51] Eugene Cheah: Strictly speaking, a 70B, and this is even for transformer or non transformer, right? We we'll take less less resources than that 400 B [00:40:00] model, even if it did double the amount thinking. And if that's the case, and we are still all trying to figure this out, maybe the direction for us is really getting the sub 200 B to be as fast as efficient as possible.

[00:40:11] Eugene Cheah: We a very efficient architecture that some folks happen to be working on to, to just reason it out over larger and larger context thing.

[00:40:20] Question: Yeah. One thing I'm super interested in is. Models that can watch forever? Obviously you cannot train something on infinite context length. How are y'all thinking about that, where you run on a much longer context length than is possible to train on?

[00:40:38] Dan Fu: Yeah, it's a, it's a great question. So I think when I think you guys probably had tweets along these lines, too. When we first started doing these things, because these are all recurrent models in theory you could just run it forever. You could just run it forever. And at the very least it won't, it won't like error out on your crash.

[00:40:57] Dan Fu: There's another question of whether it can actually [00:41:00] use what it's seen in that infinite context. And I think there, so one place where probably the research and architectures ran faster Then another research is actually the benchmarks for long context. So you turn it on forever. You want to do everything or watch everything.

[00:41:16] Dan Fu: What is it that you actually wanted to do? Can we actually build some benchmarks for that? Then measure what's happening. And then ask the question, can the models do it? Is there something else that they need? Yeah, I think that if I were to turn back the clock to 2022, that's probably one of the things I would have done differently, which would have been actually get some long context benchmarks out at the same time as we started pushing context length on all these models.

[00:41:41] Eugene Cheah: I will also say the use case. So like, I think we both agree that there's no Infinite memory and the model needs to be able to learn and decide. I think what we have observed for, I think this also fits the state space model, is that one of the key advantages of this alternate attention mechanic that is not based on token position is that the model don't suddenly become crazy when you go past the [00:42:00] 8k training context tank, or a million context tank.

[00:42:03] Eugene Cheah: It's actually still stable. It's still able to run, it's still able to rationalize. It just starts forgetting things. But some of these things are still there in latent memory. Some of these things are still somewhat there. That's the whole point of why reading twice works. Things like that. And one of the biggest pushes in this direction is that I think both Statespace and RWKB have Separate papers by other researchers where they use this architecture for time series data.

[00:42:26] Eugene Cheah: Weather modeling. So, you are not asking what was the weather five days ago. You're asking what's the weather tomorrow based on the infinite length that we, as long as this Earth and the computer will keep running. So, so, and they found that it is like, better than existing, like, transformer or existing architecture in modeling this weather data.

[00:42:47] Eugene Cheah: Control for the param size and stuff. I'm quite sure there are people with larger models. So, so there are things that, that in this case, right, there is future applications if your question is just what's next and not what's 10 years ago.

[00:42:59] Dan Fu: Thanks so [00:43:00] much for having us.

Happy holidays! We’ll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all our LS supporters who helped fund the venue and A/V production!

For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver.

Since Nathan Lambert ( Interconnects ) joined us for the hit RLHF 201 episode at the start of this year, it is hard to overstate how much Open Models have exploded this past year. In 2023 only five names were playing in the top LLM ranks, Mistral, Mosaic's MPT, TII UAE's Falcon, Yi from Kai-Fu Lee's 01.ai, and of course Meta's Llama 1 and 2. This year a whole cast of new open models have burst on the scene, from Google's Gemma and Cohere's Command R, to Alibaba's Qwen and Deepseek models, to LLM 360 and DCLM and of course to the Allen Institute's OLMo, OL MOE, Pixmo, Molmo, and Olmo 2 models.

We were honored to host Luca Soldaini, one of the research leads on the Olmo series of models at AI2.

Pursuing Open Model research comes with a lot of challenges beyond just funding and access to GPUs and datasets, particularly the regulatory debates this year across Europe, California and the White House. We also were honored to hear from and Sophia Yang, head of devrel at Mistral, who also presented a great session at the AI Engineer World's Fair Open Models track!

Full Talk on YouTube

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Timestamps

* 00:00 Welcome to Latent Space Live

* 00:12 Recap of 2024: Best Moments and Keynotes

* 01:22 Explosive Growth of Open Models in 2024

* 02:04 Challenges in Open Model Research

* 02:38 Keynote by Luca Soldani: State of Open Models

* 07:23 Significance of Open Source AI Licenses

* 11:31 Research Constraints and Compute Challenges

* 13:46 Fully Open Models: A New Trend

* 27:46 Mistral's Journey and Innovations

* 32:57 Interactive Demo: Lachat Capabilities

* 36:50 Closing Remarks and Networking

Transcript

Session3Audio

[00:00:00] AI Charlie: Welcome to Latent Space Live, our first mini conference held at NeurIPS 2024 in Vancouver. This is Charlie, your AI co host. As a special treat this week, we're recapping the best of 2024 going domain by domain. We sent out a survey to the over 900 of you who told us what you wanted, and then invited the best speakers in the latent space network to cover each field.

[00:00:28] AI Charlie: 200 of you joined us in person throughout the day, with over 2, 200 watching live online. Our next keynote covers the state of open models in 2024, with Luca Soldani and Nathan Lambert of the Allen Institute for AI, with a special appearance from Dr. Sophia Yang of Mistral. Our first hit episode of 2024 was with Nathan Lambert on RLHF 201 back in January.

[00:00:57] AI Charlie: Where he discussed both reinforcement learning for language [00:01:00] models and the growing post training and mid training stack with hot takes on everything from constitutional AI to DPO to rejection sampling and also previewed the sea change coming to the Allen Institute. And to Interconnects, his incredible substack on the technical aspects of state of the art AI training.

[00:01:18] AI Charlie: We highly recommend subscribing to get access to his Discord as well. It is hard to overstate how much open models have exploded this past year. In 2023, only five names were playing in the top LLM ranks. Mistral, Mosaics MPT, and Gatsby. TII UAE's Falcon, Yi, from Kaifu Lee's 01. ai, And of course, Meta's Lama 1 and 2.

[00:01:43] AI Charlie: This year, a whole cast of new open models have burst on the scene. From Google's Jemma and Cohere's Command R, To Alibaba's Quen and DeepSeq models, to LLM360 and DCLM, and of course, to the Allen Institute's OLMO, [00:02:00] OLMOE, PIXMO, MOLMO, and OLMO2 models. Pursuing open model research comes with a lot of challenges beyond just funding and access to GPUs and datasets, particularly the regulatory debates this year across Europe.

[00:02:14] AI Charlie: California and the White House. We also were honored to hear from Mistral, who also presented a great session at the AI Engineer World's Fair Open Models track. As always, don't forget to check the show notes for the YouTube link to their talk, as well as their slides. Watch out and take care.

[00:02:35] Luca Intro

[00:02:35] Luca Soldaini: Cool. Yeah, thanks for having me over. I'm Luca. I'm a research scientist at the Allen Institute for AI. I threw together a few slides on sort of like a recap of like interesting themes in open models for, for 2024. Have about maybe 20, 25 minutes of slides, and then we can chat if there are any questions.

[00:02:57] Luca Soldaini: If I can advance to the next slide. [00:03:00] Okay, cool. So I did the quick check of like, to sort of get a sense of like, how much 2024 was different from 2023. So I went on Hugging Face and sort of get, tried to get a picture of what kind of models were released in 2023 and like, what do we get in 2024?

[00:03:16] Luca Soldaini: 2023 we get, we got things like both LLAMA 1 and 2, we got Mistral, we got MPT, Falcon models, I think the YI model came in at the end. Tail end of the year. It was a pretty good year. But then I did the same for 2024. And it's actually quite stark difference. You have models that are, you know, reveling frontier level.

[00:03:38] Luca Soldaini: Performance of what you can get from closed models from like Quen, from DeepSeq. We got Llama3. We got all sorts of different models. I added our own Olmo at the bottom. There's this growing group of like, Fully open models that I'm going to touch on a little bit later. But you know, just looking at the slides, it feels like 2024 [00:04:00] was just smooth sailing, happy knees, much better than previous year.

[00:04:04] Luca Soldaini: And you know, you can plot you can pick your favorite benchmark Or least favorite, I don't know, depending on what point you're trying to make. And plot, you know, your closed model, your open model and sort of spin it in ways that show that, oh, you know open models are much closer to where closed models are today versus to Versus last year where the gap was fairly significant.

[00:04:29] Luca Soldaini: So one thing that I think I don't know if I have to convince people in this room, but usually when I give this talks about like open models, there is always like this background question in, in, in people's mind of like, why should we use open models? APIs argument, you know, it's, it's. Just an HTTP request to get output from a, from one of the best model out there.

[00:04:53] Luca Soldaini: Why do I have to set up infra and use local models? And there are really like two answer. There is the more [00:05:00] researchy answer for this, which is where it might be. Background lays, which is just research. If you want to do research on language models, research thrives on, on open models, there is like large swath of research on modeling, on how these models behave on evaluation and inference on mechanistic interpretability that could not happen at all if you didn't have open models they're also for AI builders, they're also like.

[00:05:30] Luca Soldaini: Good use cases for using local models. You know, you have some, this is like a very not comprehensive slides, but you have things like there are some application where local models just blow closed models out of the water. So like retrieval, it's a very clear example. We might have like constraints like Edge AI applications where it makes sense.

[00:05:51] Luca Soldaini: But even just like in terms of like stability, being able to say this model is not changing under the hood. It's, there's plenty of good cases for, [00:06:00] for open models. And the community is just not models. Is I stole this slide from one of the Quent2 announcement blog posts. But it's super cool to see like how much tech exists around open models and serving them on making them efficient and hosting them.

[00:06:18] Luca Soldaini: It's pretty cool. And so. It's if you think about like where the term opens come from, comes from like the open source really open models meet the core tenants of, of open, of open source specifically when it comes around collaboration, there is truly a spirit, like through these open models, you can build on top of other people.

[00:06:41] Luca Soldaini: innovation. We see a lot of these even in our own work of like, you know, as we iterate in the various versions of Alma it's not just like every time we collect from scratch all the data. No, the first step is like, okay, what are the cool data sources and datasets people have put [00:07:00] together for language model for training?

[00:07:01] Luca Soldaini: Or when it comes to like our post training pipeline We one of the steps is you want to do some DPO and you use a lot of outputs of other models to improve your, your preference model. So it's really having like an open sort of ecosystem benefits and accelerates the development of open models.

[00:07:23] The Definition of Open Models

[00:07:23] Luca Soldaini: One thing that we got in 2024, which is not a specific model, but I thought it was really significant, is we first got we got our first open source AI definition. So this is from the open source initiative they've been generally the steward of a lot of the open source licenses when it comes to software and so they embarked on this journey in trying to figure out, okay, How does a license, an open source license for a model look like?

[00:07:52] Luca Soldaini: Majority of the work is very dry because licenses are dry. So I'm not going to walk through the license step by [00:08:00] step, but I'm just going to pick out one aspect that is very good and then one aspect that personally feels like it needs improvement on the good side. This this open source AI license actually.

[00:08:13] Luca Soldaini: This is very intuitive. If you ever build open source software and you have some expectation around like what open source looks like for software for, for AI, sort of matches your intuition. So, the weights need to be fairly available the code must be released with an open source license and there shouldn't be like license clauses that block specific use cases.

[00:08:39] Luca Soldaini: So. Under this definition, for example, LLAMA or some of the QUEN models are not open source because the license says you can't use this model for this or it says if you use this model you have to name the output this way or derivative needs to be named that way. Those clauses don't meet open source [00:09:00] definition and so they will not be covered.

[00:09:02] Luca Soldaini: The LLAMA license will not be covered under the open source definition. It's not perfect. One of the thing that, um, internally, you know, in discussion with with OSI, we were sort of disappointed is around the language. For data. So you might imagine that an open source AI model means a model where the data is freely available.

[00:09:26] Luca Soldaini: There were discussion around that, but at the end of the day, they decided to go with a softened stance where they say a model is open source if you provide sufficient detail information. On how to sort of replicate the data pipeline. So you have an equivalent system, sufficient, sufficiently detailed.

[00:09:46] Luca Soldaini: It's very, it's very fuzzy. Don't like that. An equivalent system is also very fuzzy. And this doesn't take into account the accessibility of the process, right? It might be that you provide enough [00:10:00] information, but this process costs, I don't know, 10 million to do. Now the open source definition. Like, any open source license has never been about accessibility, so that's never a factor in open source software, how accessible software is.

[00:10:14] Luca Soldaini: I can make a piece of open source, put it on my hard drive, and never access it. That software is still open source, the fact that it's not widely distributed doesn't change the license, but practically there are expectations of like, what we want good open sources to be. So, it's, It's kind of sad to see that the data component in this license is not as, as, Open as some of us would like would like it to be.

[00:10:40] Challenges for Open Models

[00:10:40] Luca Soldaini: and I linked a blog post that Nathan wrote on the topic that it's less rambly and easier to follow through. One thing that in general, I think it's fair to say about the state of open models in 2024 is that we know a lot more than what we knew in, [00:11:00] in 2023. Like both on the training data, like And the pre training data you curate on like how to do like all the post training, especially like on the RL side.

[00:11:10] Luca Soldaini: You know, 2023 was a lot of like throwing random darts at the board. I think 2024, we have clear recipes that, okay, don't get the same results as a closed lab because there is a cost in, in actually matching what they do. But at least we have a good sense of like, okay, this is, this is the path to get state of the art language model.

[00:11:31] Luca Soldaini: I think that one thing that it's a downside of 2024 is that I think we are more research constrained in 2023. It feels that, you know, the barrier for compute that you need to, to move innovation along as just being right rising and rising. So like, if you go back to this slide, there is now this, this cluster of models that are sort of released by the.

[00:11:57] Luca Soldaini: Compute rich club. Membership is [00:12:00] hotly debated. You know, some people don't want to be. Called the rich because it comes to expectations. Some people want to be called rich, but I don't know, there's debate, but like, these are players that have, you know, 10, 000, 50, 000 GPUs at minimum. And so they can do a lot of work and a lot of exploration and improving models that it's not very accessible.

[00:12:21] Luca Soldaini: To give you a sense of like how I personally think about. Research budget for each part of the, of the language model pipeline is like on the pre training side, you can maybe do something with a thousand GPUs, really you want 10, 000. And like, if you want real estate of the art, you know, your deep seek minimum is like 50, 000 and you can scale to infinity.

[00:12:44] Luca Soldaini: The more you have, the better it gets. Everyone on that side still complains that they don't have enough GPUs. Post training is a super wide sort of spectrum. You can do as little with like eight GPUs as long as you're able to [00:13:00] run, you know, a good version of, say, a LLAMA model, you can do a lot of work there.

[00:13:05] Luca Soldaini: You can scale a lot of the methodology, just like scales with compute, right? If you're interested in you know, your open replication of what OpenAI's O1 is you're going to be on the 10K spectrum of our GPUs. Inference, you can do a lot with very few resources. Evaluation, you can do a lot with, well, I should say at least one GPUs if you want to evaluate GPUs.

[00:13:30] Luca Soldaini: Open models but in general, like if you are, if you care a lot about intervention to do on this model, which it's my prefer area of, of research, then, you know, the resources that you need are quite, quite significant. Yeah. One other trends that has emerged in 2024 is this cluster of fully open models.

[00:13:54] Luca Soldaini: So Omo the model that we built at ai, two being one of them and you know, it's nice [00:14:00] that it's not just us. There's like a cluster of other mostly research efforts who are working on this. And so it's good to to give you a primer of what like fully open means. So fully open, the easy way to think about it is instead of just releasing a model checkpoint that you run, you release a full recipe so that other people working on it.

[00:14:24] Luca Soldaini: Working on that space can pick and choose whatever they want from your recipe and create their own model or improve on top of your model. You're giving out the full pipeline and all the details there instead of just like the end output. So I pull up the screenshot from our recent MOE model.

[00:14:43] Luca Soldaini: And like for this model, for example, we released the model itself. Data that was trained on, the code, both for training and inference all the logs that we got through the training run, as well as every intermediate checkpoint and like the fact that you release different part of the pipeline [00:15:00] allows others to do really cool things.

[00:15:02] Luca Soldaini: So for example, this tweet from early this year from folks in news research they use our pre training data to do a replication of the BitNet paper in the open. So they took just a Really like the initial part of a pipeline and then the, the thing on top of it. It goes both ways.

[00:15:21] Luca Soldaini: So for example, for the Olmo2 model a lot of our pre trained data for the first stage of pre training was from this DCLM initiative that was led by folks Ooh, a variety of ins a variety of institutions. It was a really nice group effort. But you know, for When it was nice to be able to say, okay, you know, the state of the art in terms of like what is done in the open has improved.

[00:15:46] AI2 Models - Olmo, Molmo, Pixmo etc

[00:15:46] Luca Soldaini: We don't have to like do all this work from scratch to catch up the state of the art. We can just take it directly and integrate it and do our own improvements on top of that. I'm going to spend a few minutes doing like a [00:16:00] shameless plug for some of our fully open recipes. So indulge me in this.

[00:16:05] Luca Soldaini: So a few things that we released this year was, as I was mentioning, there's OMOE model which is, I think still is state of the art MOE model in its size class. And it's also. Fully open, so every component of this model is available. We released a multi modal model called Molmo. Molmo is not just a model, but it's a full recipe of how you go from a text only model to a multi modal model, and we apply this recipe on top of Quent checkpoints, on top of Olmo checkpoints, as well as on top of OlmoE.

[00:16:37] Luca Soldaini: And I think there'd be a replication doing that on top of Mistral as well. The post training side we recently released 2. 0. 3. Same story. This is a recipe on how you go from a base model to A state of the art post training model. We use the Tulu recipe on top of Olmo, on top of Llama, and then there's been open replication effort [00:17:00] to do that on top of Quen as well.

[00:17:02] Luca Soldaini: It's really nice to see like, you know, when your recipe sort of, it's kind of turnkey, you can apply it to different models and it kind of just works. And finally, the last thing we released this year was Olmo 2, which so far is the best state of the art. Fully open language model a Sera combines aspect from all three of these previous models.

[00:17:22] Luca Soldaini: What we learn on the data side from MomoE and what we learn on like making models that are easy to adapt from the Momo project and the Tulu project. I will close with a little bit of reflection of like ways this, this ecosystem of open models like it's not all roses. It's not all happy. It feels like day to day, it's always in peril.

[00:17:44] Luca Soldaini: And, you know, I talked a little bit about like the compute issues that come with it. But it's really not just compute. One thing that is on top of my mind is due to like the environment and how you know, growing feelings about like how AI is treated. [00:18:00] It's actually harder to get access to a lot of the data that was used to train a lot of the models up to last year.

[00:18:06] Luca Soldaini: So this is a screenshot from really fabulous work from Shane Longpre who's, I think is in Europe about Just access of like diminishing access to data for language model pre training. So what they did is they went through every snapshot of common crawl. Common crawl is this publicly available scrape of the, of a subset of the internet.

[00:18:29] Luca Soldaini: And they looked at how For any given website whether a website that was accessible in say 2017, what, whether it was accessible or not in 2024. And what they found is as a reaction to like the close like of the existence of closed models like OpenAI or Cloud GPT or Cloud a lot of content owners have blanket Blocked any type of crawling to your website.

[00:18:57] Luca Soldaini: And this is something that we see also internally at [00:19:00] AI2. Like one project that we started this year is we wanted to, we wanted to understand, like, if you're a good citizen of the internet and you crawl following sort of norms and policy that have been established in the last 25 years, what can you crawl?

[00:19:17] Luca Soldaini: And we found that there's a lot of website where. The norms of how you express preference of whether to crawl your data or not are broken. A lot of people would block a lot of crawling, but do not advertise that in RobustDXT. You can only tell that they're crawling, that they're blocking you in crawling when you try doing it.

[00:19:37] Luca Soldaini: Sometimes you can't even crawl the robots. txt to, to check whether you're allowed or not. And then a lot of websites there's, there's like all these technologies that historically have been, have existed to make websites serving easier such as Cloudflare or DNS. They're now being repurposed for blocking AI or any type of crawling [00:20:00] in a way that is Very opaque to the content owners themselves.

[00:20:04] Luca Soldaini: So, you know, you go to these websites, you try to access them and they're not available and you get a feeling it's like, Oh, someone changed, something changed on the, on the DNS side that it's blocking this and likely the content owner has no idea. They're just using a Cloudflare for better, you know, load balancing.

[00:20:25] Luca Soldaini: And this is something that was sort of sprung on them with very little notice. And I think the problem is this, this blocking or ideas really, it impacts people in different ways. It disproportionately helps companies that have a headstart, which are usually the closed labs and it hurts incoming newcomer players where either have now to do things in a sketchy way or you're never going to get that content that the closed lab might have.

[00:20:54] Luca Soldaini: So there's a lot, it was a lot of coverage. I'm going to plug Nathan's blog post again. That is, [00:21:00] that I think the title of this one is very succinct which is like, we're actually not, You know, before thinking about running out of training data, we're actually running out of open training data. And so if we want better open models they should be on top of our mind.

[00:21:13] Regulation and Lobbying

[00:21:13] Luca Soldaini: The other thing that has emerged is that there is strong lobbying efforts on trying to define any kind of, AI as like a new extremely risky and I want to be precise here. Like the problem is now, um, like the problem is not not considering the risk of this technology. Every technology has risks that, that should always be considered.

[00:21:37] Luca Soldaini: The thing that it's like to me is sorry, is ingenious is like just putting this AI on a pedestal and calling it like, An unknown alien technology that has like new and undiscovered potentials to destroy humanity. When in reality, all the dangers I think are rooted in [00:22:00] dangers that we know from existing software industry or existing issues that come with when using software on on a lot of sensitive domains, like medical areas.

[00:22:13] Luca Soldaini: And I also noticed a lot of efforts that have actually been going on and trying to make this open model safe. I pasted one here from AI2, but there's actually like a lot of work that has been going on on like, okay, how do you make, if you're distributing this model, Openly, how do you make it safe?

[00:22:31] Luca Soldaini: How, what's the right balance between accessibility on open models and safety? And then also there's annoying brushing of sort of concerns that are then proved to be unfounded under the rug. You know, if you remember the beginning of this year, it was all about bio risk of these open models.

[00:22:48] Luca Soldaini: The whole thing fizzled because as being Finally, there's been like rigorous research, not just this paper from Cohere folks, but it's been rigorous research showing [00:23:00] that this is really not a concern that we should be worried about. Again, there is a lot of dangerous use of AI applications, but this one was just like, A lobbying ploy to just make things sound scarier than they actually are.

[00:23:15] Luca Soldaini: So I got to preface this part. It says, this is my personal opinion. It's not my employer, but I look at things like the SP 1047 from, from California. And I think we kind of dodged a bullet on, on this legislation. We, you know, the open source community, a lot of the community came together at the last, sort of the last minute and did a very good effort trying to explain all the negative impact of this bill.

[00:23:43] Luca Soldaini: But There's like, I feel like there's a lot of excitement on building these open models or like researching on these open models. And lobbying is not sexy it's kind of boring but it's sort of necessary to make sure that this ecosystem can, can really [00:24:00] thrive. This end of presentation, I have Some links, emails, sort of standard thing in case anyone wants to reach out and if folks have questions or anything they wanted to discuss.

[00:24:13] Luca Soldaini: Is there an open floor? I think we have Sophia

[00:24:16] swyx: who wants to who one, one very important open model that we haven't covered is Mistral. Ask her on this slide. Yeah, yeah. Well, well, it's nice to have the Mistral person talk recap the year in Mistral. But while Sophia gets set up, does anyone have like, just thoughts or questions about the progress in this space?

[00:24:32] Questions - Incentive Alignment

[00:24:32] swyx: Do you always have questions?

[00:24:34] Quesiton: I'm very curious how we should build incentives to build open models, things like Francois Chollet's ArcPrize, and other initiatives like that. What is your opinion on how we should better align incentives in the community so that open models stay open?

[00:24:49] Luca Soldaini: The incentive bit is, like, really hard.

[00:24:51] Luca Soldaini: Like, even It's something that I actually, even we think a lot about it internally because like building open models is risky. [00:25:00] It's very expensive. And so people don't want to take risky bets. I think the, definitely like the challenges like our challenge, I think those are like very valid approaches for it.

[00:25:13] Luca Soldaini: And then I think in general, promoting, building, so, any kind of effort to participate in this challenge, in those challenges, if we can promote doing that on top of open models and sort of really lean into like this multiplier effect, I think that is a good way to go. If there were more money for that.

[00:25:35] Luca Soldaini: For efforts like research efforts around open models. There's a lot of, I think there's a lot of investments in companies that at the moment are releasing their model in the open, which is really cool. But it's usually more because of commercial interest and not wanting to support this, this like open models in the longterm, it's a really hard problem because I think everyone is operating sort of [00:26:00] in what.

[00:26:01] Luca Soldaini: Everyone is at their local maximum, right? In ways that really optimize their position on the market. Global maximum is harder to achieve.

[00:26:11] Question2: Can I ask one question? No.

[00:26:12] Luca Soldaini: Yeah.

[00:26:13] Question2: So I think one of the gap between the closed and open source models is the mutability. So the closed source models like chat GPT works pretty good on the low resource languages, which is not the same on the open, open source models, right?

[00:26:27] Question2: So is it in your plan to improve on that?

[00:26:32] Luca Soldaini: I think in general,

[00:26:32] Luca Soldaini: yes, is I think it's. I think we'll see a lot of improvements there in, like, 2025. Like, there's groups like, Procurement English on the smaller side that are already working on, like, better crawl support, multilingual support. I think what I'm trying to say here is you really want to be experts.

[00:26:54] Luca Soldaini: who are actually in those countries that teach those languages to [00:27:00] participate in the international community. To give you, like, a very easy example I'm originally from Italy. I think I'm terribly equipped to build a model that works well in Italian. Because one of the things you need to be able to do is having that knowledge of, like, okay, how do I access, you know, how Libraries, or content that is from this region that covers this language.

[00:27:23] Luca Soldaini: I've been in the US long enough that I no longer know. So, I think that's the efforts that folks in Central Europe, for example, are doing. Around like, okay, let's tap into regional communities. To get access you know, to bring in collaborators from those areas. I think it's going to be, like, very crucial for getting products there.

[00:27:46] Mistral intro

[00:27:46] Sophia Yang: Hi everyone. Yeah, I'm super excited to be here to talk to you guys about Mistral. A really short and quick recap of what we have done, what kind of models and products we have released in the [00:28:00] past year and a half. So most of you We have already known that we are a small startup funded about a year and a half ago in Paris in May, 2003, it was funded by three of our co founders, and in September, 2003, we released our first open source model, Mistral 7b yeah, how, how many of you have used or heard about Mistral 7b?

[00:28:24] Sophia Yang: Hey, pretty much everyone. Thank you. Yeah, it's our Pretty popular and community. Our committee really loved this model, and in December 23, we, we released another popular model with the MLE architecture Mr. A X seven B and oh. Going into this year, you can see we have released a lot of things this year.

[00:28:46] Sophia Yang: First of all, in February 2004, we released MrSmall, MrLarge, LeChat, which is our chat interface, I will show you in a little bit. We released an embedding model for, you [00:29:00] know, converting your text into embedding vectors, and all of our models are available. The, the big cloud resources. So you can use our model on Google cloud, AWS, Azure Snowflake, IBM.

[00:29:16] Sophia Yang: So very useful for enterprise who wants to use our model through cloud. And in April and May this year, we released another powerful open source MOE model, AX22B. And we also released our first code. Code Model Coastal, which is amazing at 80 plus languages. And then we provided another fine tuning service for customization.

[00:29:41] Sophia Yang: So because we know the community love to fine tune our models, so we provide you a very nice and easy option for you to fine tune our model on our platform. And also we released our fine tuning code base called Menstrual finetune. It's open source, so feel free to take it. Take a look and.

[00:29:58] Sophia Yang: More models. [00:30:00] On July 2, November this year, we released many, many other models. First of all is the two new small, best small models. We have Minestra 3B great for Deploying on edge devices we have Minstrel 8B if you used to use Minstrel 7B, Minstrel 8B is a great replacement with much stronger performance than Minstrel 7B.

[00:30:25] Sophia Yang: We also collaborated with NVIDIA and open sourced another model, Nemo 12B another great model. And Just a few weeks ago, we updated Mistral Large with the version 2 with the updated, updated state of the art features and really great function calling capabilities. It's supporting function calling in LatentNate.

[00:30:45] Sophia Yang: And we released two multimodal models Pixtral 12b. It's this open source and Pixtral Large just amazing model for, models for not understanding images, but also great at text understanding. So. Yeah, a [00:31:00] lot of the image models are not so good at textual understanding, but pixel large and pixel 12b are good at both image understanding and textual understanding.

[00:31:09] Sophia Yang: And of course, we have models for research. Coastal Mamba is built on Mamba architecture and MathRoll, great with working with math problems. So yeah, that's another model.

[00:31:29] Sophia Yang: Here's another view of our model reference. We have several premier models, which means these models are mostly available through our API. I mean, all of the models are available throughout our API, except for Ministry 3B. But for the premier model, they have a special license. Minstrel research license, you can use it for free for exploration, but if you want to use it for enterprise for production use, you will need to purchase a license [00:32:00] from us.

[00:32:00] Sophia Yang: So on the top row here, we have Minstrel 3b and 8b as our premier model. Minstrel small for best, best low latency use cases, MrLarge is great for your most sophisticated use cases. PixelLarge is the frontier class multimodal model. And, and we have Coastral for great for coding and then again, MrEmbedding model.

[00:32:22] Sophia Yang: And The bottom, the bottom of the slides here, we have several Apache 2. 0 licensed open way models. Free for the community to use, and also if you want to fine tune it, use it for customization, production, feel free to do so. The latest, we have Pixtros 3 12b. We also have Mr. Nemo mum, Coastal Mamba and Mastro, as I mentioned, and we have three legacy models that we don't update anymore.

[00:32:49] Sophia Yang: So we recommend you to move to our newer models if you are still using them. And then, just a few weeks ago, [00:33:00] we did a lot of, uh, improvements to our code interface, Lachette. How many of you have used Lachette? Oh, no. Only a few. Okay. I highly recommend Lachette. It's chat. mistral. ai. It's free to use.

[00:33:16] Sophia Yang: It has all the amazing capabilities I'm going to show you right now. But before that, Lachette in French means cat. So this is actually a cat logo. If you You can tell this is the cat eyes. Yeah. So first of all, I want to show you something Maybe let's, let's take a look at image understanding.

[00:33:36] Sophia Yang: So here I have a receipts and I want to ask, just going to get the prompts. Cool. So basically I have a receipt and I said I ordered I don't know. Coffee and the sausage. How much do I owe? Add a 18 percent tip. So hopefully it was able to get the cost of the coffee and the [00:34:00] sausage and ignore the other things.

[00:34:03] Sophia Yang: And yeah, I don't really understand this, but I think this is coffee. It's yeah. Nine, eight. And then cost of the sausage, we have 22 here. And then it was able to add the cost, calculate the tip, and all that. Great. So, it's great at image understanding, it's great at OCR tasks. So, if you have OCR tasks, please use it.

[00:34:28] Sophia Yang: It's free on the chat. It's also available through our API. And also I want to show you a Canvas example. A lot of you may have used Canvas with other tools before. But, With Lachat, it's completely free again. Here, I'm asking it to create a canvas that's used PyScript to execute Python in my browser.

[00:34:51] Sophia Yang: Let's see if it works. Import this. Okay, so, yeah, so basically it's executing [00:35:00] Python here. Exactly what we wanted. And the other day, I was trying to ask Lachat to create a game for me. Let's see if we can make it work. Yeah, the Tetris game. Yep. Let's just get one row. Maybe. Oh no. Okay. All right. You get the idea. I failed my mission. Okay. Here we go. Yay! Cool. Yeah. So as you can see, Lachet can write, like, a code about a simple game pretty easily. And you can ask Lachet to explain the code. Make updates however you like. Another example. There is a bar here I want to move.

[00:35:48] Sophia Yang: Okay, great, okay. And let's go back to another one. Yeah, we also have web search capabilities. Like, you can [00:36:00] ask what's the latest AI news. Image generation is pretty cool. Generate an image about researchers. Okay. In Vancouver? Yeah, it's Black Forest Labs flux Pro. Again, this is free, so Oh, cool.

[00:36:19] Sophia Yang: I guess researchers here are mostly from University of British Columbia. That's smart. Yeah. So this is Laia ira. Please feel free to use it. And let me know if you have any feedback. We're always looking for improvement and we're gonna release a lot more powerful features in the coming years.

[00:36:37] Sophia Yang: Thank you.

Happy holidays! We’ll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all all our LS supporters who helped fund the gorgeous venue and A/V production!

For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver.

The single most requested domain was computer vision, and we could think of no one better to help us recap 2024 than our friends at Roboflow, who was one of our earliest guests in 2023 and had one of this year’s top episodes in 2024 again. Roboflow has since raised a $40m Series B!

Links

Their slides are here:

All the trends and papers they picked:

* Isaac Robinson

* Sora (see our Video Diffusion pod) - extending diffusion from images to video

* SAM 2: Segment Anything in Images and Videos (see our SAM2 pod) - extending prompted masks to full video object segmentation

* DETR Dominancy: DETRs show Pareto improvement over YOLOs

* RT-DETR: DETRs Beat YOLOs on Real-time Object Detection

* LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection

* D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement

* Peter Robicheaux

* MMVP (Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs)

*

* Florence 2 (Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks)

* PalíGemma / PaliGemma 2

* PaliGemma: A versatile 3B VLM for transfer

* PaliGemma 2: A Family of Versatile VLMs for Transfer

* AlMv2 (Multimodal Autoregressive Pre-training of Large Vision Encoders)

* Vik Korrapati - Moondream

Full Talk on YouTube

Want more content like this? Like and subscribe to stay updated on our latest talks, interviews, and podcasts.

Transcript/Timestamps

[00:00:00] Intro

[00:00:05] AI Charlie: welcome to Latent Space Live, our first mini conference held at NeurIPS 2024 in Vancouver. This is Charlie, your AI co host. When we were thinking of ways to add value to our academic conference coverage, we realized that there was a lack of good talks, just recapping the best of 2024, going domain by domain.

[00:00:36] AI Charlie: We sent out a survey to the over 900 of you. who told us what you wanted, and then invited the best speakers in the Latent Space Network to cover each field. 200 of you joined us in person throughout the day, with over 2, 200 watching live online. Our second featured keynote is The Best of Vision 2024, with Peter Robichaud and Isaac [00:01:00] Robinson of Roboflow, with a special appearance from Vic Corrapati of Moondream.

[00:01:05] AI Charlie: When we did a poll of our attendees, the highest interest domain of the year was vision. And so our first port of call was our friends at Roboflow. Joseph Nelson helped us kickstart our vision coverage in episode 7 last year, and this year came back as a guest host with Nikki Ravey of Meta to cover segment Anything 2.

[00:01:25] AI Charlie: Roboflow have consistently been the leaders in open source vision models and tooling. With their SuperVision library recently eclipsing PyTorch's Vision library. And Roboflow Universe hosting hundreds of thousands of open source vision datasets and models. They have since announced a 40 million Series B led by Google Ventures.

[00:01:46] AI Charlie: Woohoo.

[00:01:48] Isaac's picks

[00:01:48] Isaac Robinson: Hi, we're Isaac and Peter from Roboflow, and we're going to talk about the best papers of 2024 in computer vision. So, for us, we defined best as what made [00:02:00] the biggest shifts in the space. And to determine that, we looked at what are some major trends that happened and what papers most contributed to those trends.

[00:02:09] Isaac Robinson: So I'm going to talk about a couple trends, Peter's going to talk about a trend, And then we're going to hand it off to Moondream. So, the trends that I'm interested in talking about are These are a major transition from models that run on per image basis to models that run using the same basic ideas on video.

[00:02:28] Isaac Robinson: And then also how debtors are starting to take over the real time object detection scene from the YOLOs, which have been dominant for years.

[00:02:37] Sora, OpenSora and Video Vision vs Generation

[00:02:37] Isaac Robinson: So as a highlight we're going to talk about Sora, which from my perspective is the biggest paper of 2024, even though it came out in February. Is the what?

[00:02:48] Isaac Robinson: Yeah. Yeah. So just it's a, SORA is just a a post. So I'm going to fill it in with details from replication efforts, including open SORA and related work, such as a stable [00:03:00] diffusion video. And then we're also going to talk about SAM2, which applies the SAM strategy to video. And then how debtors, These are the improvements in 2024 to debtors that are making them a Pareto improvement to YOLO based models.

[00:03:15] Isaac Robinson: So to start this off, we're going to talk about the state of the art of video generation at the end of 2023, MagVIT MagVIT is a discrete token, video tokenizer akin to VQ, GAN, but applied to video sequences. And it actually outperforms state of the art handcrafted video compression frameworks.

[00:03:38] Isaac Robinson: In terms of the bit rate versus human preference for quality and videos generated by autoregressing on these discrete tokens generate some pretty nice stuff, but up to like five seconds length and, you know, not super detailed. And then suddenly a few months later we have this, which when I saw it, it was totally mind blowing to me.

[00:03:59] Isaac Robinson: 1080p, [00:04:00] a whole minute long. We've got light reflecting in puddles. That's reflective. Reminds me of those RTX demonstrations for next generation video games, such as Cyberpunk, but with better graphics. You can see some issues in the background if you look closely, but they're kind of, as with a lot of these models, the issues tend to be things that people aren't going to pay attention to unless they're looking for.

[00:04:24] Isaac Robinson: In the same way that like six fingers on a hand. You're not going to notice is a giveaway unless you're looking for it. So yeah, as we said, SORA does not have a paper. So we're going to be filling it in with context from the rest of the computer vision scene attempting to replicate these efforts. So the first step, you have an LLM caption, a huge amount of videos.

[00:04:48] Isaac Robinson: This, this is a trick that they introduced in Dolly 3, where they train a image captioning model to just generate very high quality captions for a huge corpus and then train a diffusion model [00:05:00] on that. Their Sora and their application efforts also show a bunch of other steps that are necessary for good video generation.

[00:05:09] Isaac Robinson: Including filtering by aesthetic score and filtering by making sure the videos have enough motion. So they're not just like kind of the generators not learning to just generate static frames. So. Then we encode our video into a series of space time latents. Once again, SORA, very sparse in details.

[00:05:29] Isaac Robinson: So the replication related works, OpenSORA actually uses a MAG VIT V2 itself to do this, but swapping out the discretization step with a classic VAE autoencoder framework. They show that there's a lot of benefit from getting the temporal compression, which makes a lot of sense as the Each sequential frames and videos have mostly redundant information.

[00:05:53] Isaac Robinson: So by compressing against, compressing in the temporal space, you allow the latent to hold [00:06:00] a lot more semantic information while avoiding that duplicate. So, we've got our spacetime latents. Possibly via, there's some 3D VAE, presumably a MAG VATV2 and then you throw it into a diffusion transformer.

[00:06:19] Isaac Robinson: So I think it's personally interesting to note that OpenSORA is using a MAG VATV2, which originally used an autoregressive transformer decoder to model the latent space, but is now using a diffusion diffusion transformer. So it's still a transformer happening. Just the question is like, is it?

[00:06:37] Isaac Robinson: Parameterizing the stochastic differential equation is, or parameterizing a conditional distribution via autoregression. It's also it's also worth noting that most diffusion models today, the, the very high performance ones are switching away from the classic, like DDPM denoising diffusion probability modeling framework to rectified flows.

[00:06:57] Isaac Robinson: Rectified flows have a very interesting property that as [00:07:00] they converge, they actually get closer to being able to be sampled with a single step. Which means that in practice, you can actually generate high quality samples much faster. Major problem of DDPM and related models for the past four years is just that they require many, many steps to generate high quality samples.

[00:07:22] Isaac Robinson: So, and naturally, the third step is throwing lots of compute at the problem. So I didn't, I never figured out how to manage to get this video to loop, but we see very little compute, medium compute, lots of compute. This is so interesting because the the original diffusion transformer paper from Facebook actually showed that, in fact, the specific hyperparameters of the transformer didn't really matter that much.

[00:07:48] Isaac Robinson: What mattered was that you were just increasing the amount of compute that the model had. So, I love how in the, once again, little blog posts, they don't even talk about [00:08:00] like the specific hyperparameters. They say, we're using a diffusion transformer, and we're just throwing more compute at it, and this is what happens.

[00:08:08] Isaac Robinson: OpenSora shows similar results. The primary issue I think here is that no one else has 32x compute budget. So we end up with these we end up in the middle of the domain and most of the related work, which is still super, super cool. It's just a little disappointing considering the context. So I think this is a beautiful extension of the framework that was introduced in 22 and 23 for these very high quality per image generation and then extending that to videos.

[00:08:39] Isaac Robinson: It's awesome. And it's GA as of Monday, except no one can seem to get access to it because they keep shutting down the login.

[00:08:46] SAM and SAM2

[00:08:46] Isaac Robinson: The next, so next paper I wanted to talk about is SAM. So we at Roboflow allow users to label data and train models on that data. Sam, for us, has saved our users 75 years of [00:09:00] labeling time.

[00:09:00] Isaac Robinson: We are the, to the best of my knowledge, the largest SAM API that exists. We also, SAM also allows us to have our users train just pure bounding box regression models and use those to generate high quality masks which has the great side effect of requiring less training data to have a meaningful convergence.

[00:09:20] Isaac Robinson: So most people are data limited in the real world. So anything that requires less data to get to a useful thing is that super useful. Most of our users actually run their object per frame object detectors on every frame in a video, or maybe not most, but many, many. And so Sam follows into this category of taking, Sam 2 falls into this category of taking something that really really works and applying it to a video which has the wonderful benefit of being plug and play with most of our Many of our users use cases.

[00:09:53] Isaac Robinson: We're, we're still building out a sufficiently mature pipeline to take advantage of that, but it's, it's in the works. [00:10:00] So here we've got a great example. We can click on cells and then follow them. You even notice the cell goes away and comes back and we can still keep track of it which is very challenging for existing object trackers.

[00:10:14] Isaac Robinson: High level overview of how SAM2 works. We there's a simple pipeline here where we can give, provide some type of prompt and it fills out the rest of the likely masks for that object throughout the rest of the video. So here we're giving a bounding box in the first frame, a set of positive negative points, or even just a simple mask.

[00:10:36] Isaac Robinson: I'm going to assume people are somewhat familiar with SAM. So I'm going to just give a high level overview of how SAM works. You have an image encoder that runs on every frame. SAM two can be used on a single image, in which case the only difference between SAM two and SAM is that image encoder, which Sam used a standard VIT [00:11:00] Sam two replaced that with a hara hierarchical encoder, which gets approximately the same results, but leads to a six times faster inference, which is.

[00:11:11] Isaac Robinson: Excellent, especially considering how in a trend of 23 was replacing the VAT with more efficient backbones. In the case where you're doing video segmentation, the difference is that you actually create a memory bank and you cross attend the features from the image encoder based on the memory bank.

[00:11:31] Isaac Robinson: So the feature set that is created is essentially well, I'll go more into it in a couple of slides, but we take the features from the past couple frames, plus a set of object pointers and the set of prompts and use that to generate our new masks. Then we then fuse the new masks for this frame with the.

[00:11:57] Isaac Robinson: Image features and add that to the memory bank. [00:12:00] It's, well, I'll say more in a minute. The just like SAM, the SAM2 actually uses a data engine to create its data set in that people are, they assembled a huge amount of reference data, used people to label some of it and train the model used the model to label more of it and asked people to refine the predictions of the model.

[00:12:20] Isaac Robinson: And then ultimately the data set is just created from the engine Final output of the model on the reference data. It's very interesting. This paradigm is so interesting to me because it unifies a model in a dataset in a way that is very unique. It seems unlikely that another model could come in and have such a tight.

[00:12:37] Isaac Robinson: So brief overview of how the memory bank works, the paper did not have a great visual, so I'm just, I'm going to fill in a bit more. So we take the last couple of frames from our video. And we take the last couple of frames from our video attend that, along with the set of prompts that we provided, they could come from the future, [00:13:00] they could come from anywhere in the video, as well as reference object pointers, saying, by the way, here's what we've found so far attending to the last few frames has the interesting benefit of allowing it to model complex object motion without actually

[00:13:18] Isaac Robinson: By limiting the amount of frames that you attend to, you manage to keep the model running in real time. This is such an interesting topic for me because one would assume that attending to all of the frames is super essential, or having some type of summarization of all the frames is super essential for high performance.

[00:13:35] Isaac Robinson: But we see in their later ablation that that actually is not the case. So here, just to make sure that there is some benchmarking happening, we just compared to some of the stuff that's came out prior, and indeed the SAM2 strategy does improve on the state of the art. This ablation deep in their dependencies was super interesting to me.

[00:13:59] Isaac Robinson: [00:14:00] We see in section C, the number of memories. One would assume that increasing the count of memories would meaningfully increase performance. And we see that it has some impact, but not the type that you'd expect. And that it meaningfully decreases speed, which justifies, in my mind, just having this FIFO queue of memories.

[00:14:20] Isaac Robinson: Although in the future, I'm super interested to see A more dedicated summarization of all of the last video, not just a stacking of the last frames. So that another extension of beautiful per frame work into the video domain.

[00:14:42] Realtime detection: DETRs > YOLO

[00:14:42] Isaac Robinson: The next trend I'm interested in talking about is this interesting at RoboFlow, we're super interested in training real time object detectors.

[00:14:50] Isaac Robinson: Those are bread and butter. And so we're doing a lot to keep track of what is actually happening in that space. We are finally starting to see something change. So, [00:15:00] for years, YOLOs have been the dominant way of doing real time object detection, and we can see here that they've essentially stagnated.

[00:15:08] Isaac Robinson: The performance between 10 and 11 is not meaningfully different, at least, you know, in this type of high level chart. And even from the last couple series, there's not. A major change so YOLOs have hit a plateau, debtors have not. So we can look here and see the YOLO series has this plateau. And then these RT debtor, LW debtor, and Define have meaningfully changed that plateau so that in fact, the best Define models are plus 4.

[00:15:43] Isaac Robinson: 6 AP on Cocoa at the same latency. So three major steps to accomplish this. The first RT deditor, which is technically a 2023 paper preprint, but published officially in 24, so I'm going to include that. I hope that's okay. [00:16:00] That is showed that RT deditor showed that we could actually match or out speed YOLOs.

[00:16:04] Isaac Robinson: And then LWdebtor showed that pre training is hugely effective on debtors and much less so on YOLOs. And then DeFine added the types of bells and whistles that we expect from these types, this, this arena. So the major improvements that RTdebtor shows was Taking the multi scale features that debtors typically pass into their encoder and decoupling them into a much more efficient transformer encoder.

[00:16:30] Isaac Robinson: The transformer is of course, quadratic complexity. So decreasing the amount of stuff that you pass in at once is super helpful for increasing your runtime or increasing your throughput. So that change basically brought us up to yellow speed and then they do a hardcore analysis on. Benchmarking YOLOs, including the NMS step.

[00:16:54] Isaac Robinson: Once you once you include the NMS in the latency calculation, you see that in fact, these debtors [00:17:00] are outperforming, at least this time, the the, the YOLOs that existed. Then LW debtor goes in and suggests that in fact, the frame, the huge boost here is from pre training. So, this is the define line, and this is the define line without pre training.

[00:17:19] Isaac Robinson: It's within range, it's still an improvement over the YOLOs, but Really huge boost comes from the benefit of pre training. When YOLOx came out in 2021, they showed that they got much better results by having a much, much longer training time, but they found that when they did that, they actually did not benefit from pre training.

[00:17:40] Isaac Robinson: So, you see in this graph from LWdebtor, in fact, YOLOs do have a real benefit from pre training, but it goes away as we increase the training time. Then, the debtors converge much faster. LWdebtor trains for only 50 epochs, RTdebtor is 60 epochs. So, one could assume that, in fact, [00:18:00] the entire extra gain from pre training is that you're not destroying your original weights.

[00:18:06] Isaac Robinson: By relying on this long training cycle. And then LWdebtor also shows superior performance to our favorite data set, Roboflow 100 which means that they do better on the real world, not just on Cocoa. Then Define throws all the bells and whistles at it. Yellow models tend to have a lot of very specific complicated loss functions.

[00:18:26] Isaac Robinson: This Define brings that into the debtor world and shows consistent improvement on a variety of debtor based frameworks. So bring these all together and we see that suddenly we have almost 60 AP on Cocoa while running in like 10 milliseconds. Huge, huge stuff. So we're spending a lot of time trying to build models that work better with less data and debtors are clearly becoming a promising step in that direction.

[00:18:56] Isaac Robinson: The, what we're interested in seeing [00:19:00] from the debtors in this, this trend to next is. Codetter and the models that are currently sitting on the top of the leaderboard for large scale inference scale really well as you switch out the backbone. We're very interested in seeing and having people publish a paper, potentially us, on what happens if you take these real time ones and then throw a Swingy at it.

[00:19:23] Isaac Robinson: Like, do we have a Pareto curve that extends from the real time domain all the way up to the super, super slow but high performance domain? We also want to see people benchmarking in RF100 more, because that type of data is what's relevant for most users. And we want to see more pre training, because pre training works now.

[00:19:43] Isaac Robinson: It's super cool.

[00:19:48] Peter's Picks

[00:19:48] Peter Robicheaux: Alright, so, yeah, so in that theme one of the big things that we're focusing on is how do we get more out of our pre trained models. And one of the lenses to look at this is through sort of [00:20:00] this, this new requirement for like, how Fine grained visual details and your representations that are extracted from your foundation model.

[00:20:08] Peter Robicheaux: So it's sort of a hook for this Oh, yeah, this is just a list of all the the papers that I'm going to mention I just want to make sure I set an actual paper so you can find it later

[00:20:18] MMVP (Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs)

[00:20:18] Peter Robicheaux: Yeah, so sort of the big hook here is that I make the claim that LLMs can't see if you go to if you go to Claude or ChatGPT you ask it to see this Watch and tell me what time it is, it fails, right?

[00:20:34] Peter Robicheaux: And so you could say, like, maybe, maybe the Like, this is, like, a very classic test of an LLM, but you could say, Okay, maybe this, this image is, like, too zoomed out, And it just, like, it'll do better if we increase the resolution, And it has easier time finding these fine grained features, Like, where the watch hands are pointing.

[00:20:53] Peter Robicheaux: Nodice. And you can say, okay, well, maybe the model just doesn't know how to tell time from knowing the position of the hands. But if you actually prompt [00:21:00] it textually, it's very easy for it to tell the time. So this to me is proof that these LLMs literally cannot see the position of the watch hands and it can't see those details.

[00:21:08] Peter Robicheaux: So the question is sort of why? And for you anthropic heads out there, cloud fails too. So the, the, my first pick for best paper of 2024 Envision is this MMVP paper, which tries to investigate the Why do LLMs not have the ability to see fine grained details? And so, for instance, it comes up with a lot of images like this, where you ask it a question that seems very visually apparent to us, like, which way is the school bus facing?

[00:21:32] Peter Robicheaux: And it gets it wrong, and then, of course, it makes up details to support its wrong claim. And so, the process by which it finds these images is sort of contained in its hypothesis for why it can't. See these details. So it hypothesizes that models that have been initialized with, with Clip as their vision encoder, they don't have fine grained details and the, the features extracted using Clip because Clip sort of doesn't need to find these fine grained [00:22:00] details to do its job correctly, which is just to match captions and images, right?

[00:22:04] Peter Robicheaux: And sort of at a high level, even if ChatGPT wasn't initialized with Clip and wasn't trained contrastively at all. The vision encoder wasn't trained contrastively at all. Still, in order to do its job of capturing the image it could do a pretty good job without actually finding the exact position of all the objects and visual features in the image, right?

[00:22:21] Peter Robicheaux: So This paper finds a set of difficult images for these types of models. And the way it does it is it looks for embeddings that are similar in clip space, but far in DynaV2 space. So DynaV2 is a foundation model that was trained self supervised purely on image data. And it kind of uses like some complex student teacher framework, but essentially, and like, it patches out like certain areas of the image or like crops with certain areas of the image and tries to make sure that those have consistent representations, which is a way for it to learn very fine grained visual features.

[00:22:54] Peter Robicheaux: And so if you take things that are very close in clip space and very far in DynaV2 space, you get a set of images [00:23:00] that Basically, pairs of images that are hard for a chat GPT and other big language models to distinguish. So, if you then ask it questions about this image, well, as you can see from this chart, it's going to answer the same way for both images, right?

[00:23:14] Peter Robicheaux: Because to, to, from the perspective of the vision encoder, they're the same image. And so if you ask a question like, how many eyes does this animal have? It answers the same for both. And like all these other models, including Lava do the same thing, right? And so this is the benchmark that they create, which is like finding clip, like clip line pairs, which is pairs of images that are similar in clip space and creating a data set of multiple choice questions based off of those.

[00:23:39] Peter Robicheaux: And so how do these models do? Well, really bad. Lava, I think, So, so, chat2BT and Jim and I do a little bit better than random guessing, but, like, half of the performance of humans who find these problems to be very easy. Lava is, interestingly, extremely negatively correlated with this dataset. It does much, much, much, much worse [00:24:00] than random guessing, which means that this process has done a very good job of identifying hard images for, for Lava, specifically.

[00:24:07] Peter Robicheaux: And that's because Lava is basically not trained for very long and is initialized from Clip, and so You would expect it to do poorly on this dataset. So, one of the proposed solutions that this paper attempts is by basically saying, Okay, well if clip features aren't enough, What if we train the visual encoder of the language model also on dyno features?

[00:24:27] Peter Robicheaux: And so it, it proposes two different ways of doing this. One, additively which is basically interpolating between the two features, and then one is interleaving, which is just kind of like training one on the combination of both features. So there's this really interesting trend when you do the additive mixture of features.

[00:24:45] Peter Robicheaux: So zero is all clip features and one is all DynaV2 features. So. It, as you, so I think it's helpful to look at the right most chart first, which is as you increase the number of DynaV2 features, your model does worse and worse and [00:25:00] worse on the actual language modeling task. And that's because DynaV2 features were trained completely from a self supervised manner and completely in image space.

[00:25:08] Peter Robicheaux: It knows nothing about text. These features aren't really compatible with these text models. And so you can train an adapter all you want, but it seems that it's in such an alien language that it's like a very hard optimization for this. These models to solve. And so that kind of supports what's happening on the left, which is that, yeah, it gets better at answering these questions if as you include more dyna V two features up to a point, but then you, when you oversaturate, it completely loses its ability to like.

[00:25:36] Peter Robicheaux: Answer language and do language tasks. So you can also see with the interleaving, like they essentially double the number of tokens that are going into these models and just train on both, and it still doesn't really solve the MMVP task. It gets Lava 1. 5 above random guessing by a little bit, but it's still not close to ChachiPT or, you know, Any like human performance, obviously.

[00:25:59] Peter Robicheaux: [00:26:00] So clearly this proposed solution of just using DynaV2 features directly, isn't going to work. And basically what that means is that as a as a vision foundation model, DynaV2 is going to be insufficient for language tasks, right?

[00:26:14] Florence 2 (Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks)

[00:26:14] Peter Robicheaux: So my next pick for best paper of 2024 would be Florence 2, which tries to solve this problem by incorporating not only This dimension of spatial hierarchy, which is to say pixel level understanding, but also in making sure to include what they call semantic granularity, which ends up, the goal is basically to have features that are sufficient for finding objects in the image, so they're, they're, they have enough pixel information, but also can be talked about and can be reasoned about.

[00:26:44] Peter Robicheaux: And that's on the semantic granularity axis. So here's an example of basically three different paradigms of labeling that they do. So they, they create a big dataset. One is text, which is just captioning. And you would expect a model that's trained [00:27:00] only on captioning to have similar performance like chat2BT and like not have spatial hierarchy, not have features that are meaningful at the pixel level.

[00:27:08] Peter Robicheaux: And so they add another type, which is region text pairs, which is essentially either classifying a region or You're doing object detection or doing instance segmentation on that region or captioning that region. And then they have text phrased region annotations, which is essentially a triple. And basically, not only do you have a region that you've described, you also find it's like, It's placed in a descriptive paragraph about the image, which is basically trying to introduce even more like semantic understanding of these regions.

[00:27:39] Peter Robicheaux: And so like, for instance, if you're saying a woman riding on the road, right, you have to know what a woman is and what the road is and that she's on top of it. And that's, that's basically composing a bunch of objects in this visual space, but also thinking about it semantically, right? And so the way that they do this is they take basically they just dump Features from a vision encoder [00:28:00] straight into a encoder decoder transformer.

[00:28:03] Peter Robicheaux: And then they train a bunch of different tasks like object detection and so on as a language task. And I think that's one of the big things that we saw in 2024 is these, these vision language models operating in, on pixel space linguistically. So they introduced a bunch of new tokens to point to locations and

[00:28:22] Peter Robicheaux: So how does it work? How does it actually do? We can see if you look at the graph on the right, which is using the, the Dino, the the Dino framework your, your pre trained Florence 2 models transfer very, very well. They get 60%, 60 percent map on Cocoa, which is like approaching state of the art and they train

[00:28:42] Vik Korrapati: with, and they

[00:28:43] Peter Robicheaux: train with a much more more efficiently.

[00:28:47] Peter Robicheaux: So they, they converge a lot faster, which both of these things are pointing to the fact that they're actually leveraging their pre trained weights effectively. So where is it falling short? So these models, I forgot to mention, Florence is a 0. 2 [00:29:00] billion and a 0. 7 billion parameter count. So they're very, very small in terms of being a language model.

[00:29:05] Peter Robicheaux: And I think that. This framework, you can see saturation. So, what this graph is showing is that if you train a Florence 2 model purely on the image level and region level annotations and not including the pixel level annotations, like this, segmentation, it actually performs better as an object detector.

[00:29:25] Peter Robicheaux: And what that means is that it's not able to actually learn all the visual tasks that it's trying to learn because it doesn't have enough capacity.

[00:29:32] PalíGemma / PaliGemma 2

[00:29:32] Peter Robicheaux: So I'd like to see this paper explore larger model sizes, which brings us to our next big paper of 2024 or two papers. So PolyGemma came out earlier this year.

[00:29:42] Peter Robicheaux: PolyGemma 2 was released, I think like a week or two ago. Oh, I forgot to mention, you can actually train You can, like, label text datasets on RoboFlow and you can train a Florence 2 model and you can actually train a PolyGemma 2 model on RoboFlow, which we got into the platform within, like, 14 hours of release, which I was really excited about.

[00:29:59] Peter Robicheaux: So, anyway, so [00:30:00] PolyGemma 2, so PolyGemma is essentially doing the same thing, but instead of doing an encoder decoder, it just dumps everything into a decoder only transformer model. But it also introduced the concept of location tokens to point to objects in pixel space. PolyGemma 2, so PolyGemma uses Gemma as the language encoder, and it uses Gemma2B.

[00:30:17] Peter Robicheaux: PolyGemma 2 introduces using multiple different sizes of language encoders. So, the way that they sort of get around having to do encoder decoder is they use the concept of prefix loss. Which basically means that when it's generating, tokens autoregressively, it's all those tokens in the prefix, which is like the image that it's looking at and like a description of the task that it's trying to do.

[00:30:41] Peter Robicheaux: They're attending to each other fully, full attention. Which means that, you know, it can sort of. Find high level it's easier for the, the prefix to color, to color the output of the suffix and also to just find like features easily. So this is sort of [00:31:00] an example of like one of the tasks that was trained on, which is like, you describe the task in English and then you give it all these, like, You're asking for it to segment these two classes of objects, and then it finds, like, their locations using these tokens, and it finds their masks using some encoding of the masks into tokens.

[00:31:24] Peter Robicheaux: And, yeah, so, one of my critiques, I guess, of PolyGemma 1, at least, is that You find that performance saturates as a pre trained model after only 300 million examples seen. So, what this graph is representing is each blue dot is a performance on some downstream task. And you can see that after seeing 300 million examples, It sort of does equally well on all of the downtrend tasks that they tried it on, which was a lot as 1 billion examples, which to me also kind of suggests a lack of capacity for this model.

[00:31:58] Peter Robicheaux: PolyGemma2, [00:32:00] you can see the results on object detection. So these were transferred to to Coco. And you can see that this sort of also points to an increase in capacity being helpful to the model. You can see as. Both the resolution increases, and the parameter count of the language model increases, performance increases.

[00:32:16] Peter Robicheaux: So resolution makes sense, obviously, it helps to find small images, or small objects in the image. But it also makes sense for another reason, which is that it kind of gives the model a thinking register, and it gives it more tokens to, like, process when making its predictions. But yeah, you could, you could say, oh, 43.

[00:32:30] Peter Robicheaux: 6, that's not that great, like Florence 2 got 60. But this is not Training a dino or a debtor on top of this language or this image encoder. It's doing the raw language modeling task on Cocoa. So it doesn't have any of the bells and whistles. It doesn't have any of the fancy losses. It doesn't even have bipartite graph matching or anything like that.

[00:32:52] Peter Robicheaux: Okay, the big result and one of the reasons that I was really excited about this paper is that they blow everything else away [00:33:00] on MMVP. I mean, 47. 3, sure, that's nowhere near human accuracy, which, again, is 94%, but for a, you know, a 2 billion language, 2 billion parameter language model to be chat2BT, that's quite the achievement.

[00:33:12] Peter Robicheaux: And that sort of brings us to our final pick for paper of the year, which is AIMV2. So, AIMV2 sort of says, okay, Maybe this language model, like, maybe coming up with all these specific annotations to find features and with high fidelity and pixel space isn't actually necessary. And we can come up with an even simpler, more beautiful idea for combining you know, image tokens and pixel tokens in a way that's interfaceable for language tasks.

[00:33:44] Peter Robicheaux: And this is nice because it can scale, you can come up with lots more data if you don't have to come up with all these annotations, right? So the way that it works. is it does something very, very similar to PolyGemo, where you have a vision encoder that dumps image tokens into a decoder only transformer.

[00:33:59] Peter Robicheaux: But [00:34:00] the interesting thing is that it also autoregressively tries to learn the mean squared error of the image tokens. So instead of having to come up with fancy object detection or semantic, or segment, or segmentation labels, you can just try to reconstruct the image and have it learn fine grained features that way.

[00:34:16] Peter Robicheaux: And it does this in kind of, I think, a beautiful way that's kind of compatible with the PolyGemma line of thinking, which is randomly sampling a prefix line of thinking Prefix length and using only this number of image tokens as the prefix. And so doing a similar thing with the causal. So the causal with prefix is the, the attention mask on the right.

[00:34:35] Peter Robicheaux: So it's doing full block attention with some randomly sampled number of image tokens to then reconstruct the rest of the image and the downstream caption for that image. And so, This is the dataset that they train on. It's image or internet scale data, very high quality data created by the data filtering networks paper, essentially which is maybe The best clip data that exists.

[00:34:59] Peter Robicheaux: [00:35:00] And we can see that this is finally a model that doesn't saturate. It's even at the highest parameter count, it's, it appears to be, oh, at the highest parameter account, it appears to be improving in performance with more and more samples seen. And so you can sort of think that. You know, if we just keep bumping the parameter count and increasing the example scene, which is the, the, the line of thinking for language models, then it'll keep getting better.

[00:35:27] Peter Robicheaux: So how does it actually do at finding, oh, it also improves with resolution, which you would expect for a model that This is the ImageNet classification accuracy, but yeah, it does better if you increase the resolution, which means that it's actually leveraging and finding fine grained visual features.

[00:35:44] Peter Robicheaux: And so how does that actually do compared to CLIP on Cocoa? Well, you can see that if you slap a transformer detection head on it, Entry now in Cocoa, it's just 60. 2, which is also within spitting distance of Soda, which means that it does a very good job of [00:36:00] finding visual features, but you could say, okay, well, wait a second.

[00:36:03] Peter Robicheaux: Clip got to 59. 1, so. Like, how does this prove your claim at all? Because doesn't that mean like clip, which is known to be clip blind and do badly on MMVP, it's able to achieve a very high performance on fine, on this fine grained visual features task of object detection, well, they train on like, Tons of data.

[00:36:24] Peter Robicheaux: They train on like objects, 365, Cocoa, Flickr and everything else. And so I think that this benchmark doesn't do a great job of selling how good of a pre trained model MV2 is. And we would like to see the performance on fewer data as examples and not trained to convergence on object detection. So seeing it in the real world on like a dataset, like RoboFlow 100, I think would be quite interesting.

[00:36:48] Peter Robicheaux: And our, our, I guess our final, final pick for paper of 2024 would be Moondream. So introducing Vic to talk about that.

[00:36:54] swyx: But overall, that was exactly what I was looking for. Like best of 2024, an amazing job. Yeah, you can, [00:37:00] if there's any other questions while Vic gets set up, like vision stuff,

[00:37:07] swyx: yeah,

[00:37:11] swyx: Vic, go ahead. Hi,

[00:37:13] Vik Korrapati / Moondream

[00:37:13] question: well, while we're getting set up, hi, over here, thanks for the really awesome talk. One of the things that's been weird and surprising is that the foundation model companies Even these MLMs, they're just like worse than RT Tether at detection still. Like, if you wanted to pay a bunch of money to auto label your detection dataset, If you gave it to OpenAI or Cloud, that would be like a big waste.

[00:37:37] question: So I'm curious, just like, even Pali Gemma 2, like is worse. So, so I'm curious to hear your thoughts on like, how come, Nobody's cracked the code on like a generalist that really you know, beats a specialist model in computer vision like they have in in LLM land.[00:38:00]

[00:38:01] Isaac Robinson: Okay. It's a very, very interesting question. I think it depends on the specific domain. For image classification, it's basically there. In the, in AIMv2 showed, a simple attentional probe on the pre trained features gets like 90%, which is as well as anyone does. The, the, the, the bigger question, like, why isn't it transferring to object detection, especially like real time object detection.

[00:38:25] Isaac Robinson: I think, in my mind, there are two answers. One is, object detection is really, really, really the architectures are super domain specific. You know, we see these, all these super, super complicated things, and it's not super easy to, to, to build something that just transfers naturally like that, whereas image classification, you know, clip pre training transfers super, super quickly.

[00:38:48] Isaac Robinson: And the other thing is, until recently, the real time object detectors didn't even really benefit from pre training. Like, you see the YOLOs that are like, essentially saturated, showing very little [00:39:00] difference with pre training improvements, with using pre trained model at all. It's not surprising, necessarily, that People aren't looking at the effects of better and better pre training on real time detection.

[00:39:12] Isaac Robinson: Maybe that'll change in the next year. Does that answer your question?

[00:39:17] Peter Robicheaux: Can you guys hear me? Yeah, one thing I want to add is just like, or just to summarize, basically, is that like, Until 2024, you know, we haven't really seen a combination of transformer based object detectors and fancy losses, and PolyGemma suffers from the same problem, which is basically to say that these ResNet, or like the convolutional models, they have all these, like, extreme optimizations for doing object detection, but essentially, I think it's kind of been shown now that convolution models like just don't benefit from pre training and just don't like have the level of intelligence of transformer models.

[00:39:56] swyx: Awesome. Hi,

[00:39:59] Vik Korrapati: can [00:40:00] you hear me?

[00:40:01] swyx: Cool. I hear you. See you. Are you sharing your screen?

[00:40:04] Vik Korrapati: Hi. Might have forgotten to do that. Let me do

[00:40:07] swyx: that. Sorry, should have done

[00:40:08] Vik Korrapati: that.

[00:40:17] swyx: Here's your screen. Oh, classic. You might have to quit zoom and restart. What? It's fine. We have a capture of your screen.

[00:40:34] swyx: So let's get to it.

[00:40:35] Vik Korrapati: Okay, easy enough.

[00:40:49] Vik Korrapati: All right. Hi, everyone. My name is Vic. I've been working on Moondream for almost a year now. Like Shawn mentioned, I just went and looked and it turns out the first version I released December [00:41:00] 29, 2023. It's been a fascinating journey. So Moonbeam started off as a tiny vision language model. Since then, we've expanded scope a little bit to also try and build some tooling, client libraries, et cetera, to help people really deploy it.

[00:41:13] Vik Korrapati: Unlike traditional large models that are focused at assistant type use cases, we're laser focused on building capabilities that developers can, sorry, it's yeah, we're basically focused on building capabilities that developers can use to build vision applications that can run anywhere. So, in a lot of cases for vision more so than for text, you really care about being able to run on the edge, run in real time, etc.

[00:41:40] Vik Korrapati: So That's really important. We have we have different output modalities that we support. There's query where you can ask general English questions about an image and get back human like answers. There's captioning, which a lot of our users use for generating synthetic datasets to then train diffusion models and whatnot.

[00:41:57] Vik Korrapati: We've done a lot of work to minimize those sessions there. [00:42:00] So that's. Use lot. We have open vocabulary object detection built in similar to a couple of more recent models like Palagem, et cetera, where rather than having to train a dedicated model, you can just say show me soccer balls in this image or show me if there are any deer in this image, it'll detect it.

[00:42:14] Vik Korrapati: More recently, earlier this month, we released pointing capability where if all you're interested in is the center of an object you can just ask it to point out where that is. This is very useful when you're doing, you know, I automation type stuff. Let's see, LA we, we have two models out right now.

[00:42:33] Vik Korrapati: There's a general purpose to be para model, which runs fair. Like it's, it's it's fine if you're running on server. It's good for our local Amma desktop friends and it can run on flagship, flagship mobile phones, but it never. so much for joining us today, and we'll see you in the [00:43:00] next one. Less memory even with our not yet fully optimized inference client.

[00:43:06] Vik Korrapati: So the way we built our 0. 5b model was to start with the 2 billion parameter model and prune it while doing continual training to retain performance. We, our objective during the pruning was to preserve accuracy across a broad set of benchmarks. So the way we went about it was to estimate the importance of different components of the model, like attention heads, channels MLP rows and whatnot using basically a technique based on the gradient.

[00:43:37] Vik Korrapati: I'm not sure how much people want to know details. We'll be writing a paper about this, but feel free to grab me if you have more questions. Then we iteratively prune a small chunk that will minimize loss and performance retrain the model to recover performance and bring it back. The 0. 5b we released is more of a proof of concept that this is possible.

[00:43:54] Vik Korrapati: I think the thing that's really exciting about this is it makes it possible for for developers to build using the 2B param [00:44:00] model and just explore, build their application, and then once they're ready to deploy figure out what exactly they need out of the model and prune those capabilities into a smaller form factor that makes sense for their deployment target.

[00:44:12] Vik Korrapati: So yeah, very excited about that. Let me talk to you folks a little bit about another problem I've been working on recently, which is similar to the clocks example we've been talking about. We had a customer reach out who was talking about, like, who had a bunch of gauges out in the field. This is very common in manufacturing and oil and gas, where you have a bunch of analog devices that you need to monitor.

[00:44:34] Vik Korrapati: It's expensive to. And I was like, okay, let's have humans look at that and monitor stuff and make sure that the system gets shut down when the temperature goes over 80 or something. So I was like, yeah, this seems easy enough. Happy to, happy to help you distill that. Let's, let's get it going. Turns out our model couldn't do it at all.

[00:44:51] Vik Korrapati: I went and looked at other open source models to see if I could just generate a bunch of data and learn from that. Did not work either. So I was like, let's look at what the folks with [00:45:00] hundreds of billions of dollars in market cap have to offer. And yeah, that doesn't work either. My hypothesis is that like the, the way these models are trained are using a large amount of image text data scraped from the internet.

[00:45:15] Vik Korrapati: And that can be biased. In the case of gauges, most gauge images aren't gauges in the wild, they're product images. Detail images like these, where it's always set to zero. It's paired with an alt text that says something like GIVTO, pressure sensor, PSI, zero to 30 or something. And so the models are fairly good at picking up those details.

[00:45:35] Vik Korrapati: It'll tell you that it's a pressure gauge. It'll tell you what the brand is, but it doesn't really learn to pay attention to the needle over there. And so, yeah, that's a gap we need to address. So naturally my mind goes to like, let's use synthetic data to, Solve this problem. That works, but it's problematic because it turned out we needed millions of synthetic gauge images to get to reasonable performance.

[00:45:57] Vik Korrapati: And thinking about it, reading a gauge is like [00:46:00] not a one, like it's not a zero short process in our minds, right? Like if you had to tell me the reading in Celsius for this, Real world gauge. There's two dials on there. So first you have to figure out which one you have to be paying attention to, like the inner one or the outer one.

[00:46:14] Vik Korrapati: You look at the tip of the needle, you look at what labels it's between, and you count how many and do some math to figure out what that probably is. So what happens if we just add that as a Chain of thought to give the model better understanding of the different sub, to allow the model to better learn the subtasks it needs to perform to accomplish this goal.

[00:46:37] Vik Korrapati: So you can see in this example, this was actually generated by the latest version of our model. It's like, okay, Celsius is the inner scale. It's between 50 and 60. There's 10 ticks. So the second tick, it's a little debatable here, like there's a weird shadow situation going on, the dial is off, so I don't know what the ground truth is, but it works okay.

[00:46:57] Vik Korrapati: There's points on there that are, the points [00:47:00] over there are actually grounded. I don't know if this is easy to see, but when I click on those, there's a little red dot that moves around on the image. The model actually has to predict where this points are, I was already trying to do this with bounding boxes, but then Malmo came out with pointing capabilities.

[00:47:15] Vik Korrapati: And it's like pointing is a much better paradigm to to represent this. We see pretty good results. This one's actually for clock reading. I couldn't find our chart for gauge reading at the last minute. So the light. Blue chart is with our rounded chain of thought. This measures, we have, we built a clock reading benchmark about 500 images.

[00:47:37] Vik Korrapati: This measures accuracy on that. You can see it's a lot more sample efficient when you're using the chain of thought to model. Another big benefit from this approach is like, you can kind of understand how the model is. it and how it's failing. So in this example, the actual correct reading is 54 Celsius, the model output [00:48:00] 56, not too bad but you can actually go and see where it messed up. Like it got a lot of these right, except instead of saying it was on the 7th tick, it actually predicted that it was the 8th tick and that's why it went with 56.

[00:48:14] Vik Korrapati: So now that you know that this. Failing in this way, you can adjust how you're doing the chain of thought to maybe say like, actually count out each tick from 40, instead of just trying to say it's the eighth tick. Or you might say like, okay, I see that there's that middle thing, I'll count from there instead of all the way from 40.

[00:48:31] Vik Korrapati: So helps a ton. The other thing I'm excited about is a few short prompting or test time training with this. Like if a customer has a specific gauge that like we're seeing minor errors on, they can give us a couple of examples where like, if it's miss detecting the. Needle, they can go in and correct that in the chain of thought.

[00:48:49] Vik Korrapati: And hopefully that works the next time. Now, exciting approach, we only apply it to clocks and gauges. The real question is, is it going to generalize? Probably, like, there's some science [00:49:00] from text models that when you train on a broad number of tasks, it does generalize. And I'm seeing some science with our model as well.

[00:49:05] Vik Korrapati: So, in addition to the image based chain of thought stuff, I also added some spelling based chain of thought to help it understand better understand OCR, I guess. I don't understand why everyone doesn't do this, by the way. Like, it's trivial benchmark question. It's Very, very easy to nail. But I also wanted to support it for stuff like license plate, partial matching, like, hey, does any license plate in this image start with WHA or whatever?

[00:49:29] Vik Korrapati: So yeah, that sort of worked. All right, that, that ends my story about the gauges. If you think about what's going on over here it's interesting that like LLMs are showing enormous. Progress in reasoning, especially with the latest set of models that we've seen, but we're not really seeing, I have a feeling that VLMs are lagging behind, as we can see with these tasks that should be very simple for a human to do [00:50:00] that are very easy to find VLMs failing at.

[00:50:04] Vik Korrapati: My hypothesis on why this is the case is because On the internet, there's a ton of data that talks about how to reason. There's books about how to solve problems. There's books critiquing the books about how to solve problems. But humans are just so good at perception that we never really talk about it.

[00:50:20] Vik Korrapati: Like, maybe in art books where it's like, hey, to show that that mountain is further away, you need to desaturate it a bit or whatever. But the actual data on how to, like, look at images is, isn't really present. Also, the Data we have is kind of sketched. The best source of data we have is like image all text pairs on the internet and that's pretty low quality.

[00:50:40] Vik Korrapati: So yeah, I, I think our solution here is really just we need to teach them how to operate on individual tasks and figure out how to scale that out. All right. Yep. So conclusion. At Moondream we're trying to build amazing PLMs that run everywhere. Very hard problem. Much work ahead, but we're making a ton of progress and I'm really excited [00:51:00] about If anyone wants to chat about more technical details about how we're doing this or interest in collaborating, please, please hit me up.

[00:51:08] Isaac Robinson: Yeah,

[00:51:09] swyx: like, I always, when people say, when people say multi modality, like, you know, I always think about vision as the first among equals in all the modalities. So, I really appreciate having the experts in the room.

Happy holidays! We’ll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024 from friends of the pod!

For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver.

For our opening keynote, we could think of no one better to cover 'The State of AI Startups' than our friend Sarah Guo (AI superinvestor, founder of Conviction, host of No Priors!) and Pranav Reddy (Conviction partner) to share their takes on how the AI landscape evolved in 2024 examine the evolving AI landscape and what it means for startups, enterprises, and the industry as a whole! They completely understood the assignment.

Recorded live with 200+ in-person and 2200+ online attendees at NeurIPS 2024, this keynote kicks off our mini-conference series exploring different domains of AI development in 2024. Enjoy!

Links

Slides: https://x.com/saranormous/status/1866933642401886707

Sarh Guo: https://x.com/saranormous

Pranav Reddy: https://x.com/prnvrdy

Full Video on YouTube

Want more content like this? Like and subscribe to stay updated on our latest talks, interviews, and podcasts.

Our second podcast guest ever in March 2023 was Varun Mohan, CEO of Codeium; at the time, they had around 10,000 users and how they vowed to keep their autocomplete free forever: Today, over a million developers use their products, they still have their free tier, and they recently launched Windsurf, an AI IDE.

Chapters

* 00:00:00: Introductions & Catchup

* 00:03:52: Why they created Windsurf

* 00:05:52: Limitations of VS Code

* 00:10:12: Evaluation methods for Cascade and Windsurf

* 00:16:15: Listener questions about Windsurf launch

* 00:20:30: Remote execution and security concerns

* 00:25:18: Evolution of Codeium's strategy

* 00:28:29: Cascade and its capabilities

* 00:33:12: Multi-agent systems

* 00:37:02: Areas of improvement for Windsurf

* 00:39:12: Building an enterprise-first company

* 00:42:01: Copilot for X, AI UX, and Enterprise AI blog posts

Regular tickets are now sold out for Latent Space LIVE! at NeurIPS! We have just announced our last speaker and newest track, friend of the pod Nathan Lambert who will be recapping 2024 in Reasoning Models like o1! We opened up a handful of late bird tickets for those who are deciding now — use code DISCORDGANG if you need it. See you in Vancouver!

We’ve been sitting on our ICML recordings for a while (from today’s first-ever SOLO guest cohost, Brittany Walker), and in light of Sora Turbo’s launch (blogpost, tutorials) today, we figured it would be a good time to drop part one which had been gearing up to be a deep dive into the state of generative video worldsim, with a seamless transition to vision (the opposite modality), and finally robots (their ultimate application).

Sora, Genie, and the field of Generative Video World Simulators

Bill Peebles, author of Diffusion Transformers, gave his most recent Sora talk at ICML, which begins our episode:

* William (Bill) Peebles - SORA (slides)

Something that is often asked about Sora is how much inductive biases were introduced to achieve these results. Bill references the same principles brought by Hyung Won Chung from the o1 team - “sooner or later those biases come back to bite you”.

We also recommend these reads from throughout 2024 on Sora.

* Lilian Weng’s literature review of Video Diffusion Models

* Sora API leak

* Estimates of 100k-700k H100s needed to serve Sora (not Turbo)

* Artist guides on using Sora for professional storytelling

Google DeepMind had a remarkably strong presence at ICML on Video Generation Models, winning TWO Best Paper awards for:

* Genie: Generative Interactive Environments (covered in oral, poster, and workshop)

* VideoPoet: A Large Language Model for Zero-Shot Video Generation (see website)

We end this part by taking in Tali Dekel’s talk on The Future of Video Generation: Beyond Data and Scale.

Part 2: Generative Modeling and Diffusion

Since 2023, Sander Dieleman’s perspectives (blogpost, tweet) on diffusion as “spectral autoregression in the frequency domain” while working on Imagen and Veo have caught the public imagination, so we highlight his talk:

* Wading through the noise: an intuitive look at diffusion models

Then we go to Ben Poole for his talk on Inferring 3D Structure with 2D Priors, including his work on NeRFs and DreamFusion:

Then we investigate two flow matching papers - one from the Flow Matching co-authors - Ricky T. Q. Chen (FAIR, Meta)

And how it is implemented in Stable Diffusion 3 with Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Our last hit on Diffusion is a couple of oral presentations on speech, which we leave you to explore via our audio podcast

* NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models

* Speech Self-Supervised Learning Using Diffusion Model Synthetic Data

Part 3: Vision

The ICML Test of Time winner was DeCAF, which Trevor Darrell notably called “the OG vision foundation model”.

Lucas Beyer’s talk on “Vision in the age of LLMs — a data-centric perspective” was also well received online, and he talked about his journey from Vision Transformers to PaliGemma.

We give special honorable mention to MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark.

Part 4: Reinforcement Learning and Robotics

We segue vision into robotics with the help of Ashley Edwards, whose work on both the Gato and the Genie teams at Deepmind is summarized in Learning actions, policies, rewards, and environments from videos alone.

Brittany highlighted two poster session papers:

* Behavior Generation with Latent Actions

* We also recommend Lerrel Pinto’s On Building General-Purpose Robots

* PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs

However we must give the lion’s share of space to Chelsea Finn, now founder of Physical Intelligence, who gave FOUR talks on

* "What robots have taught me about machine learning"

* developing robot generalists

* robots that adapt autonomously

* how to give feedback to your language model

* special mention to PI colleague Sergey Levine on Robotic Foundation Models

We end the podcast with a position paper that links generative environments and RL/robotics: Automatic Environment Shaping is the Next Frontier in RL.

Timestamps

* [00:00:00] Intros

* [00:02:43] Sora - Bill Peebles

* [00:44:52] Genie: Generative Interactive Environments

* [01:00:17] Genie interview

* [01:12:33] VideoPoet: A Large Language Model for Zero-Shot Video Generation

* [01:30:51] VideoPoet interview - Dan Kondratyuk

* [01:42:00] Tali Dekel - The Future of Video Generation: Beyond Data and Scale.

* [02:27:07] Sander Dieleman - Wading through the noise: an intuitive look at diffusion models

* [03:06:20] Ben Poole - Inferring 3D Structure with 2D Priors

* [03:30:30] Ricky Chen - Flow Matching

* [04:00:03] Patrick Esser - Stable Diffusion 3

* [04:14:30] NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models

* [04:27:00] Speech Self-Supervised Learning Using Diffusion Model Synthetic Data

* [04:39:00] ICML Test of Time winner: DeCAF

* [05:03:40] Lucas Beyer: “Vision in the age of LLMs — a data-centric perspective”

* [05:42:00] Ashley Edwards: Learning actions, policies, rewards, and environments from videos alone.

* [06:03:30] Behavior Generation with Latent Actions interview

* [06:09:52] Chelsea Finn: "What robots have taught me about machine learning"

* [06:56:00] Position: Automatic Environment Shaping is the Next Frontier in RL

The full schedule for Latent Space LIVE! at NeurIPS has been announced, featuring Best of 2024 overview talks for the AI Startup Landscape, Computer Vision, Open Models, Transformers Killers, Synthetic Data, Agents, and Scaling, and speakers from Sarah Guo of Conviction, Roboflow, AI2/Meta, Recursal/Together, HuggingFace, OpenHands and SemiAnalysis. Join us for the IRL event/Livestream!

Alessio will also be holding a meetup at AWS Re:Invent in Las Vegas this Wednesday. See our new Events page for dates of AI Engineer Summit, Singapore, and World’s Fair in 2025. LAST CALL for questions for our big 2024 recap episode! Submit questions and messages on Speakpipe here for a chance to appear on the show!

When we first observed that GPT Wrappers are Good, Actually, we did not even have Bolt on our radar. Since we recorded our Anthropic episode discussing building Agents with the new Claude 3.5 Sonnet, Bolt.new (by Stackblitz) has easily cleared the $8m ARR bar, repeating and accelerating its initial $4m feat.

There are very many AI code generators and VS Code forks out there, but Bolt probably broke through initially because of its incredible zero shot low effort app generation:

But as we explain in the pod, Bolt also emphasized deploy (Netlify)/ backend (Supabase)/ fullstack capabilities on top of Stackblitz’s existing WebContainer full-WASM-powered-developer-environment-in-the-browser tech. Since then, the team has been shipping like mad (with weekly office hours), with bugfixing, full screen, multi-device, long context, diff based edits (using speculative decoding like we covered in Inference, Fast and Slow).

All of this has captured the imagination of low/no code builders like Greg Isenberg and many others on YouTube/TikTok/Reddit/X/Linkedin etc:

Just as with Fireworks, our relationship with Bolt/Stackblitz goes a bit deeper than normal - swyx advised the launch and got a front row seat to this epic journey, as well as demoed it with Realtime Voice at the recent OpenAI Dev Day. So we are very proud to be the first/closest to tell the full open story of Bolt/Stackblitz!

Flow Engineering + Qodo/AlphaCodium Update

In year 2 of the pod we have been on a roll getting former guests to return as guest cohosts (Harrison Chase, Aman Sanger, Jon Frankle), and it was a pleasure to catch Itamar Friedman back on the pod, giving us an update on all things Qodo and Testing Agents from our last catchup a year and a half ago:

Qodo (they renamed in September) went viral in early January this year with AlphaCodium (paper here, code here) beating DeepMind’s AlphaCode with high efficiency:

With a simple problem solving code agent:

* The first step is to have the model reason about the problem. They describe it using bullet points and focus on the goal, inputs, outputs, rules, constraints, and any other relevant details.

* Then, they make the model reason about the public tests and come up with an explanation of why the input leads to that particular output.

* The model generates two to three potential solutions in text and ranks them in terms of correctness, simplicity, and robustness.

* Then, it generates more diverse tests for the problem, covering cases not part of the original public tests.

* Iteratively, pick a solution, generate the code, and run it on a few test cases.

* If the tests fail, improve the code and repeat the process until the code passes every test.

swyx has previously written similar thoughts on types vs tests for putting bounds on program behavior, but AlphaCodium extends this to AI generated tests and code.

More recently, Itamar has also shown that AlphaCodium’s techniques also extend well to the o1 models:

Making Flow Engineering a useful technique to improve code model performance on every model. This is something we see AI Engineers uniquely well positioned to do compared to ML Engineers/Researchers.

Full Video Podcast

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Show Notes

* Itamar

* Qodo

* First episode

* Eric

* Bolt

* StackBlitz

* Thinkster

* AlphaCodium

* WebContainers

Chapters

* 00:00:00 Introductions & Updates

* 00:06:01 Generic vs. Specific AI Agents

* 00:07:40 Maintaining vs Creating with AI

* 00:17:46 Human vs Agent Computer Interfaces

* 00:20:15 Why Docker doesn't work for Bolt

* 00:24:23 Creating Testing and Code Review Loops

* 00:28:07 Bolt's Task Breakdown Flow

* 00:31:04 AI in Complex Enterprise Environments

* 00:41:43 AlphaCodium

* 00:44:39 Strategies for Breaking Down Complex Tasks

* 00:45:22 Building in Open Source

* 00:50:35 Choosing a product as a founder

* 00:59:03 Reflections on Bolt Success

* 01:06:07 Building a B2C GTM

* 01:18:11 AI Capabilities and Pricing Tiers

* 01:20:28 What makes Bolt unique

* 01:23:07 Future Growth and Product Development

* 01:29:06 Competitive Landscape in AI Engineering

* 01:30:01 Advice to Founders and Embracing AI

* 01:32:20 Having a baby and completing an Iron Man

Transcript

Alessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.

Swyx [00:00:12]: Hey, and today we're still in our sort of makeshift in-between studio, but we're very delighted to have a former returning guest host, Itamar. Welcome back.

Itamar [00:00:21]: Great to be here after a year or more. Yeah, a year and a half.

Swyx [00:00:24]: You're one of our earliest guests on Agents. Now you're CEO co-founder of Kodo. Right. Which has just been renamed. You also raised a $40 million Series A, and we can get caught up on everything, but we're also delighted to have our new guest, Eric. Welcome.

Eric [00:00:42]: Thank you. Excited to be here. Should I say Bolt or StackBlitz?

Swyx [00:00:45]: Like, is it like its own company now or?

Eric [00:00:47]: Yeah. Bolt's definitely bolt.new. That's the thing that we're probably the most known for, I imagine, at this point.

Swyx [00:00:54]: Which is ridiculous to say because you were working at StackBlitz for so long.

Eric [00:00:57]: Yeah. I mean, within a week, we were doing like double the amount of traffic. And StackBlitz had been online for seven years, and we were like, what? But anyways, yeah. So we're StackBlitz, the company behind bolt.new. If you've heard of bolt.new, that's our stuff. Yeah.

Swyx [00:01:12]: Yeah.

Itamar [00:01:13]: Excellent. I see, by the way, that the founder mode, you need to know to capture opportunities. So kudos on doing that, right? You're working on some technology, and then suddenly you can exploit that to a new world. Yeah.

Eric [00:01:24]: Totally. And I think, well, not to jump, but 100%, I mean, a couple of months ago, we had the idea for Bolt earlier this year, but we haven't really shared this too much publicly. But we actually had tried to build it with some of those state-of-the-art models back in January, February, you can kind of imagine which, and they just weren't good enough to actually do the code generation where the code was accurate and it was fast and whatever have you without a ton of like rag, but then there was like issues with that. So we put it on the shelf and then we got kind of a sneak peek of some of the new models that have come out in the past couple of months now. And so once we saw that, once we actually saw the code gen from it, we were like, oh my God, like, okay, we can build a product around this. And so that was really the impetus of us building the thing. But with that, it was StackBlitz, the core StackBlitz product the past seven years has been an IDE for developers. So the entire user experience flow we've built up just didn't make sense. And so when we kind of went out to build Bolt, we just thought, you know, if we were inventing our product today, what would the interface look like given what is now possible with the AI code gen? And so there's definitely a lot of conversations we had internally, but you know, just kind of when we logically laid it out, we were like, yeah, I think it makes sense to just greenfield a new thing and let's see what happens. If it works great, then we'll figure it out. If it doesn't work great, then it'll get deleted at some point. So that's kind of how it actually came to be.

Swyx [00:02:49]: I'll mention your background a little bit. You were also founder of Thinkster before you started StackBlitz. So both of you are second time founders. Both of you have sort of re-founded your company recently. Yours was more of a rename. I think a slightly different direction as well. And then we can talk about both. Maybe just chronologically, should we get caught up on where Kodo is first and then you know, just like what people should know since the last pod? Sure.

Itamar [00:03:12]: The last pod was two months after we launched and we basically had the vision that we talked about. The idea that software development is about specification, test and code, etc. We are more on the testing part as in essence, we think that if you solve testing, you solve software development. The beautiful chart that we'll put up on screen. And testing is a really big field, like there are many dimensions, unit testing, the level of the component, how big it is, how large it is. And then there is like different type of testing, is it regression or smoke or whatever. So back then we only had like one ID extension with unit tests as in focus. One and a half year later, first ID extension supports more type of testing as context aware. We index local, local repos, but also 10,000s of repos for Fortune 500 companies. We have another agent, another tool that is called, the pure agent is the open source and the commercial one is CodoMerge. And then we have another open source called CoverAgent, which is not yet a commercial product coming very soon. It's very impressive. It could be that already people are approving automated pull requests that they don't even aware in really big open sources. So once we have enough of these, we will also launch another agent. So for the first one and a half year, what we did is grew in our offering and mostly on the side of, does this code actually works, testing, code review, et cetera. And we believe that's the critical milestone that needs to be achieved to actually have the AI engineer for enterprise software. And then like for the first year was everything bottom up, getting to 1 million installation. 2024, that was 2023, 2024 was starting to monetize, to feel like how it is to make the first buck. So we did the teams offering, it went well with a thousand of teams, et cetera. And then we started like just a few months ago to do enterprise with everything you need, which is a lot of things that discussed in the last post that was just released by Codelm. So that's how we call it at Codelm. Just opening the brackets, our company name was Codelm AI, and we renamed to Codo and we call our models Codelm. So back to my point, so we started Enterprise Motion and already have multiple Fortune 100 companies. And then with that, we raised a series of $40 million. And what's exciting about it is that enables us to develop more agents. That's our focus. I think it's very different. We're not coming very soon with an ID or something like that.

Swyx [00:06:01]: You don't want to fork this code?

Itamar [00:06:03]: Maybe we'll fork JetBrains or something just to be different.

Swyx [00:06:08]: I noticed that, you know, I think the promise of general purpose agents has kind of died. Like everyone is doing kind of what you're doing. There's Codogen, Codomerge, and then there's a third one. What's the name of it?

Itamar [00:06:17]: Yeah. Codocover. Cover. Which is like a commercial version of a cover agent. It's coming soon.

Swyx [00:06:23]: Yeah. It's very similar with factory AI, also doing like droids. They all have special purpose doing things, but people don't really want general purpose agents. Right. The last time you were here, we talked about AutoGBT, the biggest thing of 2023. This year, not really relevant anymore. And I think it's mostly just because when you give me a general purpose agent, I don't know what to do with it.

Eric [00:06:42]: Yeah.

Itamar [00:06:43]: I totally agree with that. We're seeing it for a while and I think it will stay like that despite the computer use, et cetera, that supposedly can just replace us. You can just like prompt it to be, hey, now be a QA or be a QA person or a developer. I still think that there's a few reasons why you see like a dedicated agent. Again, I'm a bit more focused, like my head is more on complex software for big teams and enterprise, et cetera. And even think about permissions and what are the data sources and just the same way you manage permissions for users. Developers, you probably want to have dedicated guardrails and dedicated approvals for agents. I intentionally like touched a point on not many people think about. And of course, then what you can think of, like maybe there's different tools, tool use, et cetera. But just the first point by itself is a good reason why you want to have different agents.

Alessio [00:07:40]: Just to compare that with Bot.new, you're almost focused on like the application is very complex and now you need better tools to kind of manage it and build on top of it. On Bot.new, it's almost like I was using it the other day. There's basically like, hey, look, I'm just trying to get started. You know, I'm not very opinionated on like how you're going to implement this. Like this is what I want to do. And you build a beautiful app with it. What people ask as the next step, you know, going back to like the general versus like specific, have you had people say, hey, you know, this is great to start, but then I want a specific Bot.new dot whatever else to do a more vertical integration and kind of like development or what's the, what do people say?

Eric [00:08:18]: Yeah. I think, I think you kind of hit the, hit it head on, which is, you know, kind of the way that we've, we've kind of talked about internally is it's like people are using Bolt to go from like 0.0 to 1.0, like that's like kind of the biggest unlock that Bolt has versus most other things out there. I mean, I think that's kind of what's, what's very unique about Bolt. I think the, you know, the working on like existing enterprise applications is, I mean, it's crazy important because, you know, there's a, you look, when you look at the fortune 500, I mean, these code bases, some of these have been around for 20, 30 plus years. And so it's important to be going from, you know, 101.3 to 101.4, et cetera. I think for us, so what's been actually pretty interesting is we see there's kind of two different users for us that are coming in and it's very distinct. It's like people that are developers already. And then there's people that have never really written software and more if they have, it's been very, very minimal. And so in the first camp, what these developers are doing, like to go from zero to one, they're coming to Bolt and then they're ejecting the thing to get up or just downloading it and, you know, opening cursor, like whatever to, to, you know, keep iterating on the thing. And sometimes they'll bring it back to Bolt to like add in a huge piece of functionality or something. Right. But for the people that don't know how to code, they're actually just, they, they live in this thing. And that was one of the weird things when we launched is, you know, within a day of us being online, one of the most popular YouTube videos, and there's been a ton since, which was, you know, there's like, oh, Bolt is the cursor killer. And I originally saw the headlines and I was like, thanks for the views. I mean, I don't know. This doesn't make sense to me. That's not, that's not what we kind of thought.

Swyx [00:09:44]: It's how YouTubers talk to each other. Well, everything kills everything else.

Eric [00:09:47]: Totally. But what blew my mind was that there was any comparison because it's like cursor is a, is a local IDE product. But when, when we actually kind of dug into it and we, and we have people that are using our product saying this, I'm not using cursor. And I was like, what? And it turns out there are hundreds of thousands of people that we have seen that we're using cursor and we're trying to build apps with that where they're not traditional software does, but we're heavily leaning on the AI. And as you can imagine, it is very complicated, right? To do that with cursor. So when Bolt came out, they're like, wow, this thing's amazing because it kind of inverts the complexity where it's like, you know, it's not an IDE, it's, it's a, it's a chat-based sort of interface that we have. So that's kind of the split, which is rather interesting. We've had like the first startups now launch off of Bolt entirely where this, you know, tomorrow I'm doing a live stream with this guy named Paul, who he's built an entire CRM using this thing and you know, with backend, et cetera. And people have made their first money on the internet period, you know, launching this with Stripe or whatever have you. So that's, that's kind of the two main, the two main categories of folks that we see using Bolt though.

Itamar [00:10:51]: I agree that I don't understand the comparison. It doesn't make sense to me. I think like we have like two type of families of tools. One is like we re-imagine the software development. I think Bolt is there and I think like a cursor is more like a evolution of what we already have. It's like taking the IDE and it's, it's amazing and it's okay, let's, let's adapt the IDE to an era where LLMs can do a lot for us. And Bolt is more like, okay, let's rethink everything totally. And I think we see a few tools there, like maybe Vercel, Veo and maybe Repl.it in that area. And then in the area of let's expedite, let's change, let's, let's progress with what we already have. You can see Cursor and Kodo, but we're different between ourselves, Cursor and Kodo, but definitely I think that comparison doesn't make sense.

Alessio [00:11:42]: And just to set the context, this is not a Twitter demo. You've made 4 million of revenue in four weeks. So this is, this is actually working, you know, it's not a, what, what do you think that is? Like, there's been so many people demoing coding agents on Twitter and then it doesn't really work. And then you guys were just like, here you go, it's live, go use it, pay us for it. You know, is there anything in the development that was like interesting and maybe how that compares to building your own agents?

Eric [00:12:08]: We had no idea, honestly, like we, we, we've been pretty blown away and, and things have just kind of continued to grow faster since then. We're like, oh, today is week six. So I, I kind of came back to the point you just made, right, where it's, you, you kind of outlined, it's like, there's kind of this new market of like kind of rethinking the software development and then there's heavily augmenting existing developers. I think that, you know, both of which are, you know, AI code gen being extremely good, it's allowed existing developers, it's allowing existing developers to camera out software far faster than they could have ever before, right? It's like the ultimate power tool for an existing developer. But this code gen stuff is now so good. And then, and we saw this over the past, you know, from the beginning of the year when we tried to first build, it's actually lowered the barrier to people that, that aren't traditionally software engineers. But the kind of the key thing is if you kind of think about it from, imagine you've never written software before, right? My co-founder and I, he and I grew up down the street from each other in Chicago. We learned how to code when we were 13 together and we've been building stuff ever since. And this is back in like the mid 2000s or whatever, you know, there was nothing for free to learn from online on the internet and how to code. For our 13th birthdays, we asked our parents for, you know, O'Reilly books cause you couldn't get this at the library, right? And so instead of like an Xbox, we got, you know, programming books. But the hardest part for everyone learning to code is getting an environment set up locally, you know? And so when we built StackBlitz, like kind of the key thesis, like seven years ago, the insight we had was that, Hey, it seems like the browser has a lot of new APIs like WebAssembly and service workers, et cetera, where you could actually write an operating system that ran inside the browser that could boot in milliseconds. And you, you know, basically there's this missing capability of the web. Like the web should be able to build apps for the web, right? You should be able to build the web on the web. Every other platform has that, Visual Studio for Windows, Xcode for Mac. The web has no built in primitive for this. And so just like our built in kind of like nerd instinct on this was like, that seems like a huge hole and it's, you know, it will be very valuable or like, you know, very valuable problem to solve. So if you want to set up that environments, you know, this is what we spent the past seven years doing. And the reality is existing developers have running locally. They already know how to set up that environment. So the problem isn't as acute for them. When we put Bolt online, we took that technology called WebContainer and married it with these, you know, state of the art frontier models. And the people that have the most pain with getting stuff set up locally is people that don't code. I think that's been, you know, really the big explosive reason is no one else has been trying to make dev environments work inside of a browser tab, you know, for the past if since ever, other than basically our company, largely because there wasn't an immediate demand or need. So I think we kind of find ourselves at the right place at the right time. And again, for this market of people that don't know how to write software, you would kind of expect that you should be able to do this without downloading something to your computer in the same way that, hey, I don't have to download Photoshop now to make designs because there's Figma. I don't have to download Word because there's, you know, Google Docs. They're kind of looking at this as that sort of thing, right? Which was kind of the, you know, our impetus and kind of vision from the get-go. But you know, the code gen, the AI code gen stuff that's come out has just been, you know, an order of magnitude multiplier on how magic that is, right? So that's kind of my best distillation of like, what is going on here, you know?

Alessio [00:15:21]: And you can deploy too, right?

Eric [00:15:22]: Yeah.

Alessio [00:15:23]: Yeah.

Eric [00:15:24]: And so that's, what's really cool is it's, you know, we have deployment built in with Netlify and this is actually, I think, Sean, you actually built this at Netlify when you were there. Yeah. It's one of the most brilliant integrations actually, because, you know, effectively the API that Sean built, maybe you can speak to it, but like as a provider, we can just effectively give files to Netlify without the user even logging in and they have a live website. And if they want to keep, hold onto it, they can click a link and claim it to their Netlify account. But it basically is just this really magic experience because when you come to Bolt, you say, I want a website. Like my mom, 70, 71 years old, made her first website, you know, on the internet two weeks ago, right? It was about her nursing days.

Swyx [00:16:03]: Oh, that's fantastic though. It wouldn't have been made.

Eric [00:16:06]: A hundred percent. Cause even in, you know, when we've had a lot of people building personal, like deeply personal stuff, like in the first week we launched this, the sales guy from the East Coast, you know, replied to a tweet of mine and he said, thank you so much for building this to your team. His daughter has a medical condition and so for her to travel, she has to like line up donors or something, you know, so ahead of time. And so he actually used Bolt to make a website to do that, to actually go and send it to folks in the region she was going to travel to ahead of time. I was really touched by it, but I also thought like, why, you know, why didn't he use like Wix or Squarespace? Right? I mean, this is, this is a solved problem, quote unquote, right? And then when I thought, I actually use Squarespace for my, for my, uh, the wedding website for my wife and I, like back in 2021, so I'm familiar, you know, it was, it was faster. I know how to code. I was like, this is faster. Right. And I thought back and I was like, there's a whole interface you have to learn how to use. And it's actually not that simple. There's like a million things you can configure in that thing. When you come to Bolt, there's a, there's a text box. You just say, I need a, I need a wedding website. Here's the date. Here's where it is. And here's a photo of me and my wife, put it somewhere relevant. It's actually the simplest way. And that's what my, when my mom came, she said, uh, I'm Pat Simons. I was a nurse in the seventies, you know, and like, here's the things I did and a website came out. So coming back to why is this such a, I think, why are we seeing this sort of growth? It's, this is the simplest interface I think maybe ever created to actually build it, a deploy a website. And then that website, my mom made, she's like, okay, this looks great. And there's, there's one button, you just click it, deploy, and it's live and you can buy a domain name, attach it to it. And you know, it's as simple as it gets, it's getting even simpler with some of the stuff we're working on. But anyways, so that's, it's, it's, uh, it's been really interesting to see some of the usage like that.

Swyx [00:17:46]: I can offer my perspective. So I, you know, I probably should have disclosed a little bit that, uh, I'm a, uh, stack list investor.

Alessio [00:17:53]: Canceled the episode. I know, I know. Don't play it now. Pause.

Eric actually reached out to ShowMeBolt before the launch. And we, you know, we talked a lot about, like, the framing of, of what we're going to talk about how we marketed the thing, but also, like, what we're So that's what Bolt was going to need, like a whole sort of infrastructure.

swyx: Netlify, I was a maintainer but I won't take claim for the anonymous upload. That's actually the origin story of Netlify. We can have Matt Billman talk about it, but that was [00:18:00] how Netlify started. You could drag and drop your zip file or folder from your desktop onto a website, it would have a live URL with no sign in.

swyx: And so that was the origin story of Netlify. And it just persists to today. And it's just like it's really nice, interesting that both Bolt and CognitionDevIn and a bunch of other sort of agent type startups, they all use Netlify to deploy because of this one feature. They don't really care about the other features.

swyx: But, but just because it's easy for computers to use and talk to it, like if you build an interface for computers specifically, that it's easy for them to Navigate, then they will be used in agents. And I think that's a learning that a lot of developer tools companies are having. That's my bolt launch story and now if I say all that stuff.

swyx: And I just wanted to come back to, like, the Webcontainers things, right? Like, I think you put a lot of weight on the technical modes. I think you also are just like, very good at product. So you've, you've like, built a better agent than a lot of people, the rest of us, including myself, who have tried to build these things, and we didn't get as far as you did.

swyx: Don't shortchange yourself on products. But I think specifically [00:19:00] on, on infra, on like the sandboxing, like this is a thing that people really want. Alessio has Bax E2B, which we'll have on at some point, talking about like the sort of the server full side. But yours is, you know, inside of the browser, serverless.

swyx: It doesn't cost you anything to serve one person versus a million people. It doesn't, doesn't cost you anything. I think that's interesting. I think in theory, we should be able to like run tests because you can run the full backend. Like, you can run Git, you can run Node, you can run maybe Python someday.

swyx: We talked about this. But ideally, you should be able to have a fully gentic loop, running code, seeing the errors, correcting code, and just kind of self healing, right? Like, I mean, isn't that the dream?

Eric: Totally.

swyx: Yeah,

Eric: totally. At least in bold, we've got, we've got a good amount of that today. I mean, there's a lot more for us to do, but one of the nice things, because like in web container, you know, there's a lot of kind of stuff you go Google like, you know, turn docker container into wasm.

Eric: You'll find a lot of stuff out there that will do that. The problem is it's very big, it's slow, and that ruins the experience. And so what we ended up doing is just writing an operating system from [00:20:00] scratch that was just purpose built to, you know, run in a browser tab. And the reason being is, you know, Docker 2 awesome things will give you an image that's like out 60 to 100 megabits, you know, maybe more, you know, and our, our OS, you know, kind of clocks in, I think, I think we're in like a, maybe, maybe a megabyte or less or something like that.

Eric: I mean, it's, it's, you know, really, really, you know, stripped down.

swyx: This is basically the task involved is I understand that it's. Mapping every single, single Linux call to some kind of web, web assembly implementation,

Eric: but more or less, and, and then there's a lot of things actually, like when you're looking at a dev environment, there's a lot of things that you don't need that a traditional OS is gonna have, right?

Eric: Like, you know audio drivers or you like, there's just like, there's just tons of things. Oh, yeah. Right. Yeah. That goes . Yeah. You can just kind, you can, you can kind of tos them. Or alternatively, what you can do is you can actually be the nice thing. And this is, this kind of comes back to the origins of browsers, which is, you know, they're, they're at the beginning of the web and, you know, the late nineties, there was two very different kind of visions for the web where Alan Kay vehemently [00:21:00] disagree with the idea that should be document based, which is, you know, Tim Berners Lee, you know, that, and that's kind of what ended up winning, winning was this document based kind of browsing documents on the web thing.

Eric: Alan Kay, he's got this like very famous quote where he said, you know, you want web browsers to be mini operating systems. They should download little mini binaries and execute with like a little mini virtualized operating system in there. And what's kind of interesting about the history, not to geek out on this aspect, what's kind of interesting about the history is both of those folks ended up being right.

Eric: Documents were actually the pragmatic way that the web worked. Was, you know, became the most ubiquitous platform in the world to the degree now that this is why WebAssembly has been invented is that we're doing, we need to do more low level things in a browser, same thing with WebGPU, et cetera. And so all these APIs, you know, to build an operating system came to the browser.

Eric: And that was actually the realization we had in 2017 was, holy heck, like you can actually, you know, service workers, which were designed for allowing your app to work offline. That was the kind of the key one where it was like, wait a second, you can actually now run. Web servers within a [00:22:00] browser, like you can run a server that you open up.

Eric: That's wild. Like full Node. js. Full Node. js. Like that capability. Like, I can have a URL that's programmatically controlled. By a web application itself, boom. Like the web can build the web. The primitive is there. Everyone at the time, like we talked to people that like worked on, you know Chrome and V8 and they were like, uhhhh.

Eric: You know, like I don't know. But it's one of those things you just kind of have to go do it to find out. So we spent a couple of years, you know, working on it and yeah. And, and, and got to work in back in 2021 is when we kind of put the first like data of web container online. But

swyx: in partnership with Google, right?

swyx: Like Google actually had to help you get over the finish line with stuff.

Eric: A hundred percent, because well, you know, over the years of when we were doing the R and D on the thing. Kind of the biggest challenge, the two ways that you can kind of test how powerful and capable a platform are, the two types of applications are one, video games, right, because they're just very compute intensive, a lot of calculations that have to happen, right?

Eric: The second one are IDEs, because you're talking about actually virtualizing the actual [00:23:00] runtime environment you are in to actually build apps on top of it, which requires sophisticated capabilities, a lot of access to data. You know, a good amount of compute power, right, to effectively, you know, building app in app sort of thing.

Eric: So those, those are the stress tests. So if your platform is missing stuff, those are the things where you find out. Those are, those are the people building games and IDEs. They're the ones filing bugs on operating system level stuff. And for us, browser level stuff.

Eric [00:23:47]: yeah, what ended up happening is we were just hammering, you know, the Chromium bug tracker, and they're like, who are these guys? Yeah. And, and they were amazing because I mean, just making Chrome DevTools be able to debug, I mean, it's, it's not, it wasn't originally built right for debugging an operating system, right? They've been phenomenal working with us and just kind of really pushing the limits, but that it's a rising tide that's kind of lifted all boats because now there's a lot of different types of applications that you can debug with Chrome Dev Tools that are running a browser that runs more reliably because just the stress testing that, that we and, you know, games that are coming to the web are kind of pushing as well, but.

Itamar [00:24:23]: That's awesome. About the testing, I think like most, let's say coding assistant from different kinds will need this loop of testing. And even I would add code review to some, to some extent that you mentioned. How is testing different from code review? Code review could be, for example, PR review, like a code review that is done at the point of when you want to merge branches. But I would say that code review, for example, checks best practices, maintainability, and so on. It's not just like CI, but more than CI. And testing is like a more like checking functionality, et cetera. So it's different. We call, by the way, all of these together code integrity, but that's a different story. Just to go back to the, to the testing and specifically. Yeah. It's, it's, it's since the first slide. Yeah. We're consistent. So if we go back to the testing, I think like, it's not surprising that for us testing is important and for Bolt it's testing important, but I want to shed some light on a different perspective of it. Like let's think about autonomous driving. Those startups that are doing autonomous driving for highway and autonomous driving for the city. And I think like we saw the autonomous of the highway much faster and reaching to a level, I don't know, four or so much faster than those in the city. Now, in both cases, you need testing and quote unquote testing, you know, verifying validation that you're doing the right thing on the road and you're reading and et cetera. But it's probably like so different in the city that it could be like actually different technology. And I claim that we're seeing something similar here. So when you're building the next Wix, and if I was them, I was like looking at you and being a bit scared. That's what you're disrupting, what you just said. Then basically, I would say that, for example, the UX UI is freaking important. And because you're you're more aiming for the end user. In this case, maybe it's an end user that doesn't know how to develop for developers. It's also important. But let alone those that do not know to develop, they need a slick UI UX. And I think like that's one reason, for example, I think Cursor have like really good technology. I don't know the underlying what's under the hood, but at least what they're saying. But I think also their UX UI is great. It's a lot because they did their own ID. While if you're aiming for the city AI, suddenly like there's a lot of testing and code review technology that it's not necessarily like that important. For example, let's talk about integration tests. Probably like a lot of what you're building involved at the moment is isolated applications. Maybe the vision or the end game is maybe like having one solution for everything. It could be that eventually the highway companies will go into the city and the other way around. But at the beginning, there is a difference. And integration tests are a good example. I guess they're a bit less important. And when you think about enterprise software, they're really important. So to recap, like I think like the idea of looping and verifying your test and verifying your code in different ways, testing or code review, et cetera, seems to be important in the highway AI and the city AI, but in different ways and different like critical for the city, even more and more variety. Actually, I was looking to ask you like what kind of loops you guys are doing. For example, when I'm using Bolt and I'm enjoying it a lot, then I do see like sometimes you're trying to catch the errors and fix them. And also, I noticed that you're breaking down tasks into smaller ones and then et cetera, which is already a common notion for a year ago. But it seems like you're doing it really well. So if you're willing to share anything about it.

Eric [00:28:07]: Yeah, yeah. I realized I never actually hit the punchline of what I was saying before. I mentioned the point about us kind of writing an operating system from scratch because what ended up being important about that is that to your point, it's actually a very, like compared to like a, you know, if you're like running cursor on anyone's machine, you kind of don't know what you're dealing with, with the OS you're running on. There could be an error happens. It could be like a million different things, right? There could be some config. There could be, it could be God knows what, right? The thing with WebConnect is because we wrote the entire thing from scratch. It's actually a unified image basically. And we can instrument it at any level that we think is going to be useful, which is exactly what we did when we started building Bolt is we instrumented stuff at like the process level, at the runtime level, you know, et cetera, et cetera, et cetera. Stuff that would just be not impossible to do on local, but to do that in a way that works across any operating system, whatever is, I mean, would just be insanely, you know, insanely difficult to do right and reliably. And that's what you saw when you've used Bolt is that when an error actually will occur, whether it's in the build process or the actual web application itself is failing or anything kind of in between, you can actually capture those errors. And today it's a very primitive way of how we've implemented it largely because the product just didn't exist 90 days ago. So we're like, we got some work ahead of us and we got to hire some more a little bit, but basically we present and we say, Hey, this is, here's kind of the things that went wrong. There's a fix it button and then a ignore button, and then you can just hit fix it. And then we take all that telemetry through our agent, you run it through our agent and say, kind of, here's the state of the application. Here's kind of the errors that we got from Node.js or the browser or whatever, and like dah, dah, dah, dah. And it can take a crack at actually solving it. And it's actually pretty darn good at being able to do that. That's kind of been a, you know, closing the loop and having it be a reliable kind of base has seemed to be a pretty big upgrade over doing stuff locally, just because I think that's a pretty key ingredient of it. And yeah, I think breaking things down into smaller tasks, like that's, that's kind of a key part of our agent. I think like Claude did a really good job with artifacts. I think, you know, us and kind of everyone else has, has kind of taken their approach of like actually breaking out certain tasks in a certain order into, you know, kind of a concrete way. And, and so actually the core of Bolt, I know we actually made open source. So you can actually go and check out like the system prompts and et cetera, and you can run it locally and whatever have you. So anyone that's interested in this stuff, I'd highly recommend taking a look at. There's not a lot of like stuff that's like open source in this realm. It's, that was one of the fun things that we've we thought would be cool to do. And people, people seem to like it. I mean, there's a lot of forks and people adding different models and stuff. So it's been cool to see.

Swyx [00:30:41]: Yeah. I'm happy to add, I added real-time voice for my opening day demo and it was really fun to hack with. So thank you for doing that. Yeah. Thank you. I'm going to steal your code.

Eric [00:30:52]: Because I want that.

Swyx [00:30:52]: It's funny because I built on top of the fork of Bolt.new that already has the multi LLM thing. And so you just told me you're going to merge that in. So then you're going to merge two layers of forks down into this thing. So it'll be fun.

Eric [00:31:03]: Heck yeah.

Alessio [00:31:04]: Just to touch on like the environment, Itamar, you maybe go into the most complicated environments that even the people that work there don't know how to run. How much of an impact does that have on your performance? Like, you know, it's most of the work you're doing actually figuring out environment and like the libraries, because I'm sure they're using outdated version of languages, they're using outdated libraries, they're using forks that have not been on the public internet before. How much of the work that you're doing is like there versus like at the LLM level?

Itamar [00:31:32]: One of the reasons I was asking about, you know, what are the steps to break things down, because it really matters. Like, what's the tech stack? How complicated the software is? It's hard to figure it out when you're dealing with the real world, any environment of enterprise as a city, when I'm like, while maybe sometimes like, I think you do enable like in Bolt, like to install stuff, but it's quite a like controlled environment. And that's a good thing to do, because then you narrow down and it's easier to make things work. So definitely, there are two dimensions, I think, actually spaces. One is the fact just like installing our software without yet like doing anything, making it work, just installing it because we work with enterprise and Fortune 500, etc. Many of them want on prem solution.

Swyx [00:32:22]: So you have how many deployment options?

Itamar [00:32:24]: Basically, we had, we did a metric metrics, say 96 options, because, you know, they're different dimensions. Like, for example, one dimension, we connect to your code management system to your Git. So are you having like GitHub, GitLab? Subversion? Is it like on cloud or deployed on prem? Just an example. Which model agree to use its APIs or ours? Like we have our Is it TestGPT? Yeah, when we started with TestGPT, it was a huge mistake name. It was cool back then, but I don't think it's a good idea to name a model after someone else's model. Anyway, that's my opinion. So we got

Swyx [00:33:02]: I'm interested in these learnings, like things that you change your mind on.

Itamar [00:33:06]: Eventually, when you're building a company, you're building a brand and you want to create your own brand. By the way, when I thought about Bolt.new, I also thought about if it's not a problem, because when I think about Bolt, I do think about like a couple of companies that are already called this way.

Swyx [00:33:19]: Curse companies. You could call it Codium just to...

Itamar [00:33:24]: Okay, thank you. Touche. Touche.

Eric [00:33:27]: Yeah, you got to imagine the board meeting before we launched Bolt, one of our investors, you can imagine they're like, are you sure? Because from the investment side, it's kind of a famous, very notorious Bolt. And they're like, are you sure you want to go with that name? Oh, yeah. Yeah, absolutely.

Itamar [00:33:43]: At this point, we have actually four models. There is a model for autocomplete. There's a model for the chat. There is a model dedicated for more for code review. And there is a model that is for code embedding. Actually, you might notice that there isn't a good code embedding model out there. Can you name one? Like dedicated for code?

Swyx [00:34:04]: There's code indexing, and then you can do sort of like the hide for code. And then you can embed the descriptions of the code.

Itamar [00:34:12]: Yeah, but you do see a lot of type of models that are dedicated for embedding and for different spaces, different fields, etc. And I'm not aware. And I know that if you go to the bedrock, try to find like there's a few code embedding models, but none of them are specialized for code.

Swyx [00:34:31]: Is there a benchmark that you would tell us to pay attention to?

Itamar [00:34:34]: Yeah, so it's coming. Wait for that. Anyway, we have our models. And just to go back to the 96 option of deployment. So I'm closing the brackets for us. So one is like dimensional, like what Git deployment you have, like what models do you agree to use? Dotter could be like if it's air-gapped completely, or you want VPC, and then you have Azure, GCP, and AWS, which is different. Do you use Kubernetes or do not? Because we want to exploit that. There are companies that do not do that, etc. I guess you know what I mean. So that's one thing. And considering that we are dealing with one of all four enterprises, we needed to deal with that. So you asked me about how complicated it is to solve that complex code. I said, it's just a deployment part. And then now to the software, we see a lot of different challenges. For example, some companies, they did actually a good job to build a lot of microservices. Let's not get to if it's good or not, but let's first assume that it is a good thing. A lot of microservices, each one of them has their own repo. And now you have tens of thousands of repos. And you as a developer want to develop something. And I remember me coming to a corporate for the first time. I don't know where to look at, like where to find things. So just doing a good indexing for that is like a challenge. And moreover, the regular indexing, the one that you can find, we wrote a few blogs on that. By the way, we also have some open source, different than yours, but actually three and growing. Then it doesn't work. You need to let the tech leads and the companies influence your indexing. For example, Mark with different repos with different colors. This is a high quality repo. This is a lower quality repo. This is a repo that we want to deprecate. This is a repo we want to grow, etc. And let that be part of your indexing. And only then things actually work for enterprise and they don't get to a fatigue of, oh, this is awesome. Oh, but I'm starting, it's annoying me. I think Copilot is an amazing tool, but I'm quoting others, meaning GitHub Copilot, that they see not so good retention of GitHub Copilot and enterprise. Ooh, spicy. Yeah. I saw snapshots of people and we have customers that are Copilot users as well. And also I saw research, some of them is public by the way, between 38 to 50% retention for users using Copilot and enterprise. So it's not so good. By the way, I don't think it's that bad, but it's not so good. So I think that's a reason because, yeah, it helps you auto-complete, but then, and especially if you're working on your repo alone, but if it's need that context of remote repos that you're code-based, that's hard. So to make things work, there's a lot of work on that, like giving the controllability for the tech leads, for the developer platform or developer experience department in the organization to influence how things are working. A short example, because if you have like really old legacy code, probably some of it is not so good anymore. If you just fine tune on these code base, then there is a bias to repeat those mistakes or old practices, etc. So you need, for example, as I mentioned, to influence that. For example, in Coda, you can have a markdown of best practices by the tech leads and Coda will include that and relate to that and will not offer suggestions that are not according to the best practices, just as an example. So that's just a short list of things that you need to do in order to deal with, like you mentioned, the 100.1 to 100.2 version of software. I just want to say what you're doing is extremely

Eric [00:38:32]: impressive because it's very difficult. I mean, the business of Stackplus, kind of before bulk came online, we sold a version of our IDE that went on-prem. So I understand what you're saying about the difficulty of getting stuff just working on-prem. Holy heck. I mean, that is extremely hard. I guess the question I have for you is, I mean, we were just doing that with kind of Kubernetes-based stuff, but the spread of Fortune 500 companies that you're working with, how are they doing the inference for this? Are you kind of plugging into Azure's OpenAI stuff and AWS's Bedrock, you know, Cloud stuff? Or are they just like running stuff on GPUs? Like, what is that? How are these folks approaching that? Because, man, what we saw on the enterprise side, I mean, I got to imagine that that's a huge challenge. Everything you said and more, like,

Itamar [00:39:15]: for example, like someone could be, and I don't think any of these is bad. Like, they made their decision. Like, for example, some people, they're, I want only AWS and VPC on AWS, no matter what. And then they, some of them, like there is a subset, I will say, I'm willing to take models only for from Bedrock and not ours. And we have a problem because there is no good code embedding model on Bedrock. And that's part of what we're doing now with AWS to solve that. We solve it in a different way. But if you are willing to run on AWS VPC, but run your run models on GPUs or inferentia, like the new version of the more coming out, then our models can run on that. But everything you said is right. Like, we see like on-prem deployment where they have their own GPUs. We see Azure where you're using OpenAI Azure. We see cases where you're running on GCP and they want OpenAI. Like this cross, like a case, although there is Gemini or even Sonnet, I think is available on GCP, just an example. So all the options, that's part of the challenge. I admit that we thought about it, but it was even more complicated. And it took us a few months to actually, that metrics that I mentioned, to start clicking each one of the blocks there. A few months is impressive. I mean,

Eric [00:40:35]: honestly, just that's okay. Every one of these enterprises is, their networking is different. Just everything's different. Every single one is different. I see you understand. Yeah. So that just cannot be understated. That it is, that's extremely impressive. Hats off.

Itamar [00:40:50]: It could be, by the way, like, for example, oh, we're only AWS, but our GitHub enterprise is on-prem. Oh, we forgot. So we need like a private link or whatever, like every time like that. It's not, and you do need to think about it if you want to work with an enterprise. And it's important. Like I understand like their, I respect their point of view.

Swyx [00:41:10]: And this primarily impacts your architecture, your tech choices. Like you have to, you can't choose some vendors because...

Itamar [00:41:15]: Yeah, definitely. To be frank, it makes us hard for a startup because it means that we want, we want everyone to enjoy all the variety of models. By the way, it was hard for us with our technology. I want to open a bracket, like a window. I guess you're familiar with our Alpha Codium, which is an open source.

Eric [00:41:33]: We got to go over that. Yeah. So I'll do that quickly.

Itamar [00:41:36]: Yeah. A pin in that. Yeah. Actually, we didn't have it in the last episode. So, so, okay.

Swyx [00:41:41]: Okay. We'll come back to that later, but let's talk about...

Itamar [00:41:43]: Yeah. So, so just like shortly, and then we can double click on Alpha Codium. But Alpha Codium is a open source tool. You can go and try it and lets you compete on CodeForce. This is a website and a competition and actually reach a master level level, like 95% with a click of a button. You don't need to do anything. And part of what we did there is taking a problem and breaking it to different, like smaller blocks. And then the models are doing a much better job. Like we all know it by now that taking small tasks and solving them, by the way, even O1, which is supposed to be able to do system two thinking like Greg from OpenAI like hinted, is doing better on these kinds of problems. But still, it's very useful to break it down for O1, despite O1 being able to think by itself. And that's what we presented like just a month ago, OpenAI released that now they are doing 93 percentile with O1 IOI left and International Olympiad of Formation. Sorry, I forgot. Exactly. I told you I forgot. And we took their O1 preview with Alpha Codium and did better. Like it just shows like, and there is a big difference between the preview and the IOI. It shows like that these models are not still system two thinkers, and there is a big difference. So maybe they're not complete system two. Yeah, they need some guidance. I call them system 1.5. We can, we can have it. I thought about it. Like, you know, I care about this philosophy stuff. And I think like we didn't see it even close to a system two thinking. I can elaborate later. But closing the brackets, like we take Alpha Codium and as our principle of thinking, we take tasks and break them down to smaller tasks. And then we want to exploit the best model to solve them. So I want to enable anyone to enjoy O1 and SONET and Gemini 1.5, etc. But at the same time, I need to develop my own models as well, because some of the Fortune 500 want to have all air gapped or whatever. So that's a challenge. Now you need to support so many models. And to some extent, I would say that the flow engineering, the breaking down to two different blocks is a necessity for us. Why? Because when you take a big block, a big problem, you need a very different prompt for each one of the models to actually work. But when you take a big problem and break it into small tasks, we can talk how we do that, then the prompt matters less. What I want to say, like all this, like as a startup trying to do different deployment, getting all the juice that you can get from models, etc. is a big problem. And one need to think about it. And one of our mitigation is that process of taking tasks and breaking them down. That's why I'm really interested to know how you guys are doing it. And part of what we do is also open source. So you can see.

Swyx [00:44:39]: There's a lot in there. But yeah, flow over prompt. I do believe that that does make sense. I feel like there's a lot that both of you can sort of exchange notes on breaking down problems. And I just want you guys to just go for it. This is fun to watch.

Eric [00:44:55]: Yeah. I mean, what's super interesting is the context you're working in is, because for us too with Bolt, we've started thinking because our kind of existing business line was going behind the firewall, right? We were like, how do we do this? Adding the inference aspect on, we're like, okay, how does... Because I mean, there's not a lot of prior art, right? I mean, this is all new. This is all new. So I definitely am going to have a lot of questions for you.

Itamar [00:45:17]: I'm here. We're very open, by the way. We have a paper on a blog or like whatever.

Swyx [00:45:22]: The Alphacodeum, GitHub, and we'll put all this in the show notes.

Itamar [00:45:25]: Yeah. And even the new results of O1, we published it.

Eric [00:45:29]: I love that. And I also just, I think spiritually, I like your approach of being transparent. Because I think there's a lot of hype-ium around AI stuff. And a lot of it is, it's just like, you have these companies that are just kind of keep their stuff closed source and then just max hype it, but then it's kind of nothing. And I think it kind of gives a bad rep to the incredible stuff that's actually happening here. And so I think it's stuff like what you're doing where, I mean, true merit and you're cracking open actual code for others to learn from and use. That strikes me as the right approach. And it's great to hear that you're making such incredible progress.

Itamar [00:46:02]: I have something to share about the open source. Most of our tools are, we have an open source version and then a premium pro version. But it's not an easy decision to do that. I actually wanted to ask you about your strategy, but I think in your case, there is, in my opinion, relatively a good strategy where a lot of parts of open source, but then you have the deployment and the environment, which is not right if I get it correctly. And then there's a clear, almost hugging face model. Yeah, you can do that, but why should you try to deploy it yourself, deploy it with us? But in our case, and I'm not sure you're not going to hit also some competitors, and I guess you are. I wanted to ask you, for example, on some of them. In our case, one day we looked on one of our competitors that is doing code review. We're a platform. We have the code review, the testing, et cetera, spread over the ID to get. And in each agent, we have a few startups or a big incumbents that are doing only that. So we noticed one of our competitors having not only a very similar UI of our open source, but actually even our typo. And you sit there and you're kind of like, yeah, we're not that good. We don't use enough Grammarly or whatever. And we had a couple of these and we saw it there. And then it's a challenge. And I want to ask you, Bald is doing so well, and then you open source it. So I think I know what my answer was. I gave it before, but still interesting

Eric [00:47:29]: to hear what you think. GeoHot said back, I don't know who he was up to at this exact moment, but I think on comma AI, all that stuff's open source. And someone had asked him, why is this open source? And he's like, if you're not actually confident that you can go and crush it and build the best thing, then yeah, you should probably keep your stuff closed source. He said something akin to that. I'm probably kind of butchering it, but I thought it was kind of a really good point. And that's not to say that you should just open source everything, because for obvious reasons, there's kind of strategic things you have to kind of take in mind. But I actually think a pretty liberal approach, as liberal as you kind of can be, it can really make a lot of sense. Because that is so validating that one of your competitors is taking your stuff and they're like, yeah, let's just kind of tweak the styles. I mean, clearly, right? I think it's kind of healthy because it keeps, I'm sure back at HQ that day when you saw that, you're like, oh, all right, well, we have to grind even harder to make sure we stay ahead. And so I think it's actually a very useful, motivating thing for the teams. Because you might feel this period of comfort. I think a lot of companies will have this period of comfort where they're not feeling the competition and one day they get disrupted. So kind of putting stuff out there and letting people push it forces you to face reality soon, right? And actually feel that incrementally so you can kind of adjust course. And that's for us, the open source version of Bolt has had a lot of features people have been begging us for, like persisting chat messages and checkpoints and stuff. Within the first week, that stuff was landed in the open source versions. And they're like, why can't you ship this? It's in the open, so people have forked it. And we're like, we're trying to keep our servers and GPUs online. But it's been great because the folks in the community did a great job, kept us on our toes. And we've got to know most of these folks too at this point that have been building these things. And so it actually was very instructive. Like, okay, well, if we're going to go kind of land this, there's some UX patterns we can kind of look at and the code is open source to this stuff. What's great about these, what's not. So anyways, NetNet, I think it's awesome. I think from a competitive point of view for us, I think in particular, what's interesting is the core technology of WebContainer going. And I think that right now, there's really nothing that's kind of on par with that. And we also, we have a business of, because WebContainer runs in your browser, but to make it work, you have to install stuff from NPM. You have to make cores bypass requests, like connected databases, which all require server-side proxying or acceleration. And so we actually sell WebContainer as a service. One of the core reasons we open-sourced kind of the core components of Bolt when we launched was that we think that there's going to be a lot more of these AI, in-your-browser AI co-gen experiences, kind of like what Anthropic did with Artifacts and Clod. By the way, Artifacts uses WebContainers. Not yet. No, yeah. Should I strike that? I think that they've got their own thing at the moment, but there's been a lot of interest in WebContainers from folks doing things in that sort of realm and in the AI labs and startups and everything in between. So I think there'll be, I imagine, over the coming months, there'll be lots of things being announced to folks kind of adopting it. But yeah, I think effectively...

Swyx [00:50:35]: Okay, I'll say this. If you're a large model lab and you want to build sandbox environments inside of your chat app, you should call Eric.

Itamar [00:50:43]: But wait, wait, wait, wait, wait, wait. I have a question about that. I think OpenAI, they felt that people are not using their model as they would want to. So they built ChatGPT. But I would say that ChatGPT now defines OpenAI. I know they're doing a lot of business from their APIs, but still, is this how you think? Isn't Bolt.new your business now? Why don't you focus on that instead of the...

Swyx [00:51:16]: What's your advice as a founder?

Eric [00:51:18]: You're right. And so going into it, we, candidly, we were like, Bolt.new, this thing is super cool. We think people are stoked. We think people will be stoked. But we were like, maybe that's allowed. Best case scenario, after month one, we'd be mind blown if we added a couple hundred K of error or something. And we were like, but we think there's probably going to be an immediate huge business. Because there was some early poll on folks wanting to put WebContainer into their product offerings, kind of similar to what Bolt is doing or whatever. We were actually prepared for the inverse outcome here. But I mean, well, I guess we've seen poll on both. But I mean, what's happened with Bolt, and you're right, it's actually the same strategy as like OpenAI or Anthropic, where we have our ChatGPT to OpenAI's APIs is Bolt to WebContainer. And so we've kind of taken that same approach. And we're seeing, I guess, some of the similar results, except right now, the revenue side is extremely lopsided to Bolt.

Itamar [00:52:16]: I think if you ask me what's my advice, I think you have three options. One is to focus on Bolt. The other is to focus on the WebContainer. The third is to raise one billion dollars and do them both. I'm serious. I think otherwise, you need to choose. And if you raise enough money, and I think it's big bucks, because you're going to be chased by competitors. And I think it will be challenging to do both. And maybe you can. I don't know. We do see these numbers right now, raising above $100 million, even without having

Eric [00:52:49]: a product. You can see these. It's excellent advice. And I think what's been amazing, but also kind of challenging is we're trying to forecast, okay, well, where are these things going? I mean, in the initial weeks, I think us and all the investors in the company that we're sharing this with, it was like, this is cool. Okay, we added 500k. Wow, that's crazy. Wow, we're at a million now. Most things, you have this kind of the tech crunch launch of initiation and then the thing of sorrow. And if there's going to be a downtrend, it's just not coming yet. Now that we're kind of looking ahead, we're six weeks in. So now we're getting enough confidence in our convictions to go, okay, this seems to be the trend line. I'll tell you another reason why

Swyx [00:53:33]: I think, where is Jasper? They actually just announced some new numbers recently. They're still surviving. They have gone down a lot. I think that the peak that I heard was a hundred

Itamar [00:53:42]: billion ARR. And now there's like tens of these. So I think their success was phenomenal, like what I see at Bolt. And I think if you want to keep that, probably, who am I? I'm just giving my two cents. You need to focus because you are going to see weeks, I think that you're disrupting their market. And you open sourced some of it and they have containers, I believe. And you need to fight. I can tell you that when we open source, I share with you a small competitor, but I can tell you, I have a friend who has built a billion dollar company and more. When we released Alpha Codium, he sent me a private email asking, what the f**k did you just do? Why did you release that? You should have kept it. Yeah, you released that open source. I'm thinking, build some stuff and now I can do that much more easily. I can tell you my answer and I thought that maybe you'll answer as well. Although I think Bolt is already very promising. For us, Alpha Codium 1 is like GPT 1. I agree with you. Being open and open source, etc. really helps to improve the product community, etc. But at some point, OpenAI closed their GPT 3.5 or whatever. And that was part of my answer. Alpha Codium is the agent that is compatible with GPT 1 and there is a lot to do for these agents to actually get that moment that we had with GPT 3.5, etc. as agents.

Eric [00:55:11]: Yeah, I think you're dead right. And I think it just comes back to what GeoHot said. It's like, if you want to win, there's no other option than out hustling everyone else. And so I think that's kind of out hustling in the sense really meaning building the best product, building the best experiences. And so I think that's the only way kind of almost any route and open source and stuff just kind of burns the ships in a sense. And maybe that's the simplest way of saying it. You're burning the ships, but also it builds a lot of goodwill. I mean, there's tons of benefits to it. Salesforce are doing that, right?

Itamar [00:55:43]: They're now going to be agent force or whatever. So you can also...

Swyx [00:55:47]: We're going to try to get Mark on the podcast. And they're good friends with Salesforce. Any parting thoughts, any trends that you're

Itamar [00:55:55]: super excited about? If we're talking about trends, I go back to our original podcast where we talked about the idea that the software world is built from specs, tests, and code. And I think you can see that one dimension are company startups that are rethinking the entire development environment, I think like Bolt, etc. And another dimension is where is their focus? Is it on the spec, is on the test and on the code? And I think it's interesting to see that from that view. We'll see more startup and more amazing announcements of new directions, new philosophy. So I think we'll see startup focusing, let's build everything from the spec. To some extent, I would say that Bolt is, from my understanding, you can say better, somewhere in the line between the spec and the code. Because you start, like I saw your demos, you're trying to describe things, not just in one row, because you want to look like you want it. So it's on that edge between connecting between spec and code. And you see others, I think all the IDEs, most of them are the new IDEs, or the fork are there. We are more focused from the test and to the code and to the spec, etc. So these are trends, I think we will see that. And I think another dimension to consider is, is it more for the highway AI, for the developers, maybe not even a technical person, or is it for the enterprise? And that also gives you different products. If they are aiming for different ICP, different ideal client profile, they will approach this triangle of spec and test and code. And that's how I see the world. And what I'm noticing is that we're seeing more and more of those new startups, new interfaces that are not focused on code. For example, talking more about the spec, talking more about the testing. Eventually, I think that that's where the world is going to. The code is going to be there, and there will be developers, etc. But as agent improves and capabilities of the LLMs and integrations to different parts of the development environment, we're going to see more and more focusing on the spec and the test. Basically, these two might unite, the spec and the test, because you can say that tests are runnable specs, to some extent. So that's another way to look at

Swyx [00:58:23]: it. Yeah, that is literally on the slide here, runnable tests, right here. Yeah, I'm consistent.

Itamar [00:58:27]: It's all consistent. Look, I talked about system one and system two more than a year ago. And now with O1, people are talking about system one. But I think we'll talk about it again, because I think they're totally, totally wrong about O1 being a system two. It is now in the hype or whatever, talking about that. But I think the agents are the ones that will take us towards system two. And the more they are aware of their environment, and aware of that sometimes they don't know what they don't know, then we'll really get to system two. But that's

Swyx [00:59:03]: a deeper discussion. It's a deeper discussion. I love the philosophy talk that we had last time as well. All right, so we're back on to Bolt, and Itamar had to leave for another interview. But we were just talking about what happened post-launch, right? And I held this emergency council of advisors for you, because we had never seen this before. And I was like, okay, I'm going to call all the smartest people I know to join this thing.

Eric [00:59:27]: Which was extremely helpful. And I'm so appreciative. There's been a handful of me.

Swyx [00:59:31]: You made one hire out of that.

Eric [00:59:34]: Yeah, because it was like, I think I can't remember where we were at kind of ARR-wise when I had messaged you.

Swyx [00:59:40]: It was like, you messaged me at like two or three. And then by the time we got everything together, it was four. And then, yeah, now it's at-

Alessio [00:59:48]: Since Eric sat down five minutes.

Swyx [00:59:52]: But I mean, it sounds like you accelerated, because you told me it was like 100k, 200k a day. And now it's accelerated?

Eric [00:59:58]: Yeah, this past- I mean, every week has been kind of a blowout week as far as- Is it TikTok? We're digging into the degree that we can of just like where all this stuff's coming from. I mean, there's a ton of word of mouth, right? So that you can't- which you can't just like look by refer, right? So there's a ton of direct. But yeah, I mean, there's a lot of TikTok. There's a ton of YouTube. It's kind of, I think, been a sensation in the sort of like entrepreneurial, build your own SaaS, indie hacker, even developer circles. And I think, too, our team's been doing a really good job. Our folks just kind of like flipped a switch. And people were just working through the weekends or whatever to get stuff fixed. And so the product- and you'll see people say this online. Like today, there was a tweet. Someone was like, yeah, I tried this like the first week and I couldn't get whatever to work. Came back today, six weeks later, and this is ridiculous. Like this is so good, right? And so I think there's been an incredible amount of improvement to the product, to the agent, also to like the underlying models, too. Like Sonnet, they just happened to do an update with their release a couple of weeks ago. And so when we put our new agent online and the new Sonnet, we saw a huge bump in conversion just based on that. And so yeah, we've gone at that. When we were chatting, that must have been three weeks ago, maybe an average of 100K ARR per day. And this week, I will see- I've said this every week, but we'll see if it holds. The past couple of days have been like half a million of ARR per day, which is insane. I think today we've had peak traffic, just kind of set the previous- and that's kind of been every day this week. But anyways, yeah, I think things just continue to accelerate, which is kind of blowing my mind, because it's just the sheer numbers of this stuff are just mind-boggling.

Alessio [01:01:40]: I think you almost suffered from the Twitter demo issues that other people had. The first time I saw Bolt, I saw the demo and I was like, oh, that's cool. I didn't go to try it because I was like, I've seen so many of these that it's like, I don't know if it's actually going to work. And then two days ago, I signed up to use it. I was building a Luma replacement. I'm done with Luma. And I was like, man, this thing really works. And I already knew you, of course. I was like, man, this thing really works. What the f**k? I was like, it's actually, I don't know if it's like the model, if it's like how you prompt it, but it's so good at coming up with the simplest thing to implement. So the Luma example, right? So first I was like, create a RSVP page for an event and it created a wedding RSVP. I don't know if it's your fault. I don't know if you bolted it. And then I was like, well, now it needs to have a way to create more events and added that. And then I was like, now it needs a way to like have an admin page to modify event. And maybe what I would have done as a developer is like, well, I'll create a different like admin view, you know, with all the events and then I'll have like the front end thing. And instead what it did is like, it created like a admin view with toggle on top and then like just a pencil button on every page to edit them in line, you know, and that was it. And I was like, yeah, that works just as well. And like for the model, that's probably the simplest way to do it because it like limits the amount of files that are there. Can you talk just more about how much of this is like the model coming out with it, how much you're prompting it to kind of like be very like

Eric [01:03:04]: compressed and concise. A ton of it is the model, but I think what's interesting though, is you're kind of baseline model. If I just like, if it's kind of like try and put it into like a, you know, way, if you had to quantify, quantify, you know, the effect is obviously the model is like this sort of like 10X multiplier. You're how good the bottom line model is huge, huge swing. And then kind of what you can do on top of that, you can squeeze out three, four X kind of more. And so that's kind of where the realm of, you know, prompt engineering and multi-agent approaches, et cetera, kind of kick in. And so I think, I think with us, you know, our folks, like the guy on our side that, you know, led the web engineering, like that kind of our core technology for the past, you know, seven years here, you know, his name is Dominic Elm based out of Germany and he was one of the founding engineers of the company. You had previous to StackBlitz, he actually was doing machine learning and he basically had built a StackBlitz, like online ID for machine learning. So I think like, I kind of like Google Colab sort of thing, or like Hugging Face has their kind of version of this. Back in 2016, it wasn't as much of a market for this stuff, but he had been doing a lot of, you know, training, you know, ML models and that sort of thing. So I guess, you know, as we began, you know, kind of digging into AI stuff over the past year, he's been kind of leading that off. And so a lot of it, I really attribute to Dom's specific angle, cause he has deep understanding of our technology and how it works. Cause he's, you know, led the engineering on web container, but as you know, deep understanding of how these models work going and actually kind of writing out these you know, whether it's like the, the, the prompt engineering aspect of it or multi-agent or whatever, have you, you know, that's sort of like that much context. And, and the, and the other folks on the team are, are, you know, in the same, same sort of spot that have been working on this stuff. I think we'd be able to squeeze out a lot more than I've seen almost anything else out there, at least in the term of building web apps, at least. But I guess I think it's, I think it's kind of just because we we have more context on, on a fewer number of heads at the company. So we can kind of connect the dots of it faster, you

Swyx [01:05:01]: know? Yeah. That's part of the issue with the whole raise a billion dollars thing. Like you actually run very lean and that's, that's actually been to your advantage.

Eric [01:05:08]: Totally. And I think, you know, and I think we, we have to staff up because I mean, we went from, you know, call it zero customers to, you know, 20, 30,000 kind of, you know, in six weeks, we have to have certainly more customer support, customer success stuff, et cetera. But you know, also just on, on engineering we have to ramp up, but I do think that there's a, we saw this in the 2021 cycle, right? Where, you know, adding tons more people can, can, can be a thing that really hurts, you know, the company because you can, it's just harder. It's really hard to manage lots of people. Not if you're a big enough company to warrant a certain headcount, a 100%, you kind of have to do it. Right. But I think for us, it's worked just to really grow, grow the team slowly and intentionally. And so I think we're going to take the same approach here at a bit of a faster clip than we were previously. But to me, that would just be general advice to startups is like slowly intentionally as fast as you can to meet demand or whatever. Part of what I felt like you're in a unique position to

Swyx [01:06:07]: talk about, but also kind of what we went through in our, in our call was I have PMF now, what is, is kind of what I've been saying. And so like, I think the first answer is hire a data scientist because we have to sort of figure out like from our data that you're now sitting on a ton of different customers and we don't really know the different customer segments. You're starting to get an idea of churn. You're starting to get an idea of like segmentation. You already had data enrichment. One of my most interesting quotes from you from that session was that because you were selling to enterprise for so long, you had already set up all that stuff and it's just like, wasn't useful for a more sort of developer bottom up centric approach.

Eric [01:06:46]: Yeah. And particularly because for the first time in the company's history, we're selling primarily to almost non-developers. And so everything that we've ever, all the playbooks we had not relevant here basically. Right. So the, and you're one of one of our investors I talked with earlier this week, basically brought up a really great point, which is like, you are now a B2C company and how you operate needs to reflect that.

Swyx [01:07:09]: Which is, which is what, I don't know.

Eric [01:07:11]: Which is basically from an analytics perspective, like you're tracking everything. Right. And then to your point, you have, you have people kind of around the clock slicing and dicing data to understand who are these people coming in, who are the types of people you actually want to retain versus people that, you know, are just going to churn out. And that's okay. Cause they're not the actual like ICP that you're going for. Right. When you're building stuff for enterprise software, the bar is a lot lower. And then to kind of to, from the conversation before one of the biggest, and this is kind of what we found with StackBlitz, which is kind of interesting, you know, you mentioned it, it's like, it's as a startup, it's very hard to sell on-prem extremely true. But if you can do it, it's like the promised land because you know, these, these companies you know, the fortune 500s, they can write really large checks. And so when you're going and selling to them, it doesn't matter so much like on your website. Sure. You want to track the conversion to the enterprise contact form or whatever. Right. But what, what actually really matters is like the, a lot of human touch points of, Hey, we want to have a quarterly call after just getting installed this stuff. There's a whole playbook for that. And you need to hire sales engineers that can be on the ground floor and helping people install it. Then after that, you got to, okay, how do we make sure they're kind of constantly successful? Because you can't access like we can, our enterprise customer instances, we have no idea how often they're using them. Why? Because the whole point is that we can't see what they're up to for a good reason, right? Like they, they need to own their data. And so the way it's actually much, a very complicated problem of how do you have like build relationships where everyone's getting on calls, they can share kind of the telemetry that, that they can see within their instance. And you can kind of extrapolate that and make sure they're happy and successful. So that's, there's a whole art of that, of doing enterprise well, that we've gone and done and closed these folks totally unrelated to doing BC completely, completely unrelated for the most part. So anyway, so that, so that, you know, we're, as a company, we're, we're kind of reorienting, you know, our focus on, okay, going and actually really leaning in on analytics, whatever have you. And fortunately, like my co-founder and I, the art, the enterprise business of stack was, was the first time we had ever done enterprise primarily like things to the company we did before was B2C. Like we were selling people courses on how to do web development basically. Right. So a lot of the skillset that, you know, I had built up there, I able to pull that back off the shelf, dust it off, sharpen the blade. And, you know, we're doing email marketing, we're doing live streams, you know? So, so that's, it's, it's kind of cool to, you know, be shifting back to some of the, the, the, where we cut our teeth on back in the day.

Alessio [01:09:35]: How did you pick the pricing? Because I had to pay.

Swyx [01:09:38]: That's fantastic. You want to like slight, slightly like, yeah, you got a bit. It's like,

Alessio [01:09:44]: you're running out of tokens, dude. I was like, f**k, I'm running out of tokens. It's like, I don't want to run out of tokens, but there's like five different tiers. Yeah. Right. Which are kind of like token based and capacity based. Yep. How do you kind of reconcile that? And the consumer side where maybe the consumer doesn't even really need to know what a token is, right? Like on that, like your mom probably doesn't really care what an AI token is. How did you structure it to start? How did you come up with that? And then maybe ideas that you have to like improve or like modify that.

Eric [01:10:12]: Totally. Yeah. So we, so when we first launched with StackBlitz is like, we were an enterprise play, right? And so when we launched in 2017, I think we tried pricing 2018 or 2019, but like it was free for a long time. And then we had a 9𝑝𝑙𝑎𝑛𝑎𝑛𝑑𝑡ℎ𝑎𝑡𝑤𝑎𝑠𝑗𝑢𝑠𝑡𝑡ℎ𝑒𝑤𝑎𝑦𝑖𝑡𝑤𝑎𝑠.𝐼𝑡𝑤𝑎𝑠,𝑖𝑡𝑤𝑎𝑠𝑘𝑖𝑛𝑑𝑜𝑓𝑙𝑖𝑘𝑒𝑜𝑢𝑟,𝑜𝑢𝑟𝑑𝑜𝑙𝑙𝑎𝑟50ℎ𝑜𝑡𝑑𝑜𝑔𝑎𝑡𝐶𝑜𝑠𝑡𝑐𝑜.𝐼𝑡′𝑠𝑘𝑖𝑛𝑑𝑜𝑓𝑙𝑖𝑘𝑒𝑡ℎ𝑖𝑠,𝑡ℎ𝑖𝑠,𝑦𝑜𝑢𝑘𝑛𝑜𝑤,𝑗𝑢𝑠𝑡𝑙𝑜𝑤𝑝𝑟𝑖𝑐𝑒,𝑗𝑢𝑠𝑡,𝑦𝑜𝑢𝑘𝑛𝑜𝑤,𝑖𝑡,𝑖𝑡𝑤𝑎𝑠𝑛′𝑡𝑡ℎ𝑒𝑝𝑟𝑖𝑚𝑎𝑟𝑦𝑟𝑒𝑣𝑑𝑟𝑖𝑣𝑒𝑟𝑎𝑛𝑑𝑤𝑒𝑗𝑢𝑠𝑡𝑤𝑎𝑛𝑡𝑒𝑑𝑡𝑜,𝑦𝑜𝑢𝑘𝑛𝑜𝑤,𝑠𝑎𝑦,𝐻𝑒𝑦,𝑝𝑎𝑦𝑓𝑜𝑟𝑠𝑜𝑚𝑒𝑚𝑜𝑟𝑒𝑠𝑡𝑜𝑟𝑎𝑔𝑒𝑎𝑛𝑑𝑝𝑟𝑖𝑣𝑎𝑡𝑒𝑝𝑟𝑜𝑗𝑒𝑐𝑡𝑠𝑜𝑟𝑤ℎ𝑎𝑡𝑒𝑣𝑒𝑟.𝐴𝑛𝑑𝑠𝑜𝑤𝑒𝑤𝑒𝑛𝑡𝑡𝑜𝑙𝑎𝑢𝑛𝑐ℎ𝑏𝑜𝑙𝑡𝑎𝑔𝑎𝑖𝑛,𝑙𝑖𝑘𝑒𝑜𝑢𝑟𝑒𝑥𝑝𝑒𝑐𝑡𝑎𝑡𝑖𝑜𝑛𝑤𝑎𝑠,𝐻𝑒𝑦,𝑤𝑒′𝑙𝑙𝑝𝑟𝑜𝑏𝑎𝑏𝑙𝑦𝑔𝑒𝑡𝑎𝑔𝑜𝑜𝑑𝑛𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝑝𝑒𝑜𝑝𝑙𝑒𝑡ℎ𝑎𝑡′𝑙𝑙𝑠𝑖𝑔𝑛𝑢𝑝𝑎𝑛𝑑𝑏𝑒𝑒𝑥𝑐𝑖𝑡𝑒𝑑𝑎𝑏𝑜𝑢𝑡𝑖𝑡.𝐴𝑛𝑑𝑦𝑜𝑢𝑘𝑛𝑜𝑤,𝑤𝑒′𝑟𝑒𝑛𝑜𝑡𝑡𝑜𝑜𝑐𝑜𝑛𝑐𝑒𝑟𝑛𝑒𝑑,𝑦𝑜𝑢𝑘𝑛𝑜𝑤,𝑤𝑒′𝑟𝑒𝑗𝑢𝑠𝑡,𝑤𝑒′𝑟𝑒𝑗𝑢𝑠𝑡𝑛𝑜𝑡,𝑤𝑒𝑤𝑒𝑟𝑒𝑢𝑛𝑝𝑟𝑒𝑝𝑎𝑟𝑒𝑑𝑓𝑜𝑟𝑡ℎ𝑒𝑡𝑠𝑢𝑛𝑎𝑚𝑖𝑡ℎ𝑎𝑡ℎ𝑖𝑡.𝐴𝑛𝑑𝑠𝑜𝑎𝑓𝑡𝑒𝑟𝑔𝑜𝑖𝑛𝑔𝑜𝑛𝑙𝑖𝑛𝑒𝑡ℎ𝑒𝑓𝑖𝑟𝑠𝑡𝑤𝑒𝑒𝑘,𝑤𝑒𝑤𝑒𝑟𝑒𝑙𝑖𝑘𝑒,𝑤𝑜𝑤,𝑡ℎ𝑖𝑠𝑖𝑠𝑐𝑜𝑜𝑙.𝑇ℎ𝑒𝑟𝑒′𝑠𝑎,𝐼𝑚𝑒𝑎𝑛,𝑖𝑡𝑗𝑢𝑠𝑡𝑘𝑒𝑝𝑡𝑔𝑟𝑜𝑤𝑖𝑛𝑔.𝐴𝑛𝑑𝑡ℎ𝑒𝑛𝑜𝑛𝑐𝑒𝑤𝑒ℎ𝑖𝑡𝑤𝑒𝑒𝑘𝑡𝑤𝑜,𝐼𝑚𝑒𝑎𝑛,𝑤𝑒𝑤𝑒𝑟𝑒𝑗𝑢𝑠𝑡𝑛𝑖𝑛𝑒𝑏𝑢𝑐𝑘𝑠𝑤𝑎𝑠,𝐼𝑚𝑒𝑎𝑛,𝑖𝑡′𝑠𝑙𝑖𝑘𝑒𝑡ℎ𝑒𝑐ℎ𝑒𝑎𝑝𝑒𝑠𝑡𝐴𝐼𝑐𝑜𝑑𝑖𝑛𝑔𝑡ℎ𝑖𝑛𝑔𝑦𝑜𝑢𝑐𝑎𝑛𝑔𝑒𝑡𝑚𝑎𝑦𝑏𝑒𝑜𝑡ℎ𝑒𝑟𝑡ℎ𝑎𝑛𝑐𝑜𝑝𝑖𝑙𝑜𝑡,𝑏𝑢𝑡𝑙𝑖𝑘𝑒𝑤𝑒𝑤𝑒𝑟𝑒𝑜𝑣𝑒𝑟𝑟𝑢𝑛𝑏𝑦𝑠𝑢𝑝𝑝𝑜𝑟𝑡𝑡𝑖𝑐𝑘𝑒𝑡𝑠.𝐴𝑛𝑑𝐼𝑗𝑢𝑠𝑡,𝑎𝑛𝑑𝑗𝑢𝑠𝑡𝑡ℎ𝑒𝑠ℎ𝑒𝑒𝑟𝑣𝑜𝑙𝑢𝑚𝑒𝑜𝑓𝑝𝑒𝑜𝑝𝑙𝑒𝑐𝑜𝑚𝑖𝑛𝑔𝑖𝑛𝑎𝑛𝑑𝑖𝑡𝑗𝑢𝑠𝑡𝑙𝑎𝑤𝑠𝑜𝑓𝑠𝑢𝑝𝑝𝑙𝑦𝑎𝑛𝑑𝑑𝑒𝑚𝑎𝑛𝑑.𝑊𝑒𝑤𝑒𝑟𝑒𝑙𝑖𝑘𝑒,𝑜𝑘𝑎𝑦,𝑡ℎ𝑖𝑠𝑖𝑠𝑛′𝑡,𝑡ℎ𝑒𝑟𝑒′𝑠𝑛𝑜𝑤𝑎𝑦𝑤𝑒𝑐𝑎𝑛𝑠𝑐𝑎𝑙𝑒𝑡𝑜𝑚𝑒𝑒𝑡𝑡ℎ𝑖𝑠.𝐴𝑙𝑠𝑜𝑡ℎ𝑒𝑝𝑒𝑜𝑝𝑙𝑒𝑐𝑜𝑚𝑖𝑛𝑔𝑖𝑛𝑎𝑟𝑒𝑏𝑢𝑟𝑛𝑖𝑛𝑔𝑡ℎ𝑟𝑜𝑢𝑔ℎ𝑡ℎ𝑒𝑖𝑟𝑡𝑜𝑘𝑒𝑛𝑠𝑎𝑛𝑑𝑡ℎ𝑒𝑟𝑒′𝑠𝑛𝑜𝑤𝑎𝑦𝑡𝑜𝑎𝑐𝑡𝑢𝑎𝑙𝑙𝑦𝑙𝑖𝑘𝑒𝑏𝑢𝑦𝑚𝑜𝑟𝑒𝑜𝑓𝑡ℎ𝑒𝑠𝑒𝑡ℎ𝑖𝑛𝑔𝑠.𝐴𝑛𝑑𝑛𝑖𝑛𝑒𝑏𝑢𝑐𝑘𝑠𝑖𝑠𝑗𝑢𝑠𝑡,𝑦𝑜𝑢𝑐𝑎𝑛′𝑡𝑔𝑒𝑡𝑡ℎ𝑎𝑡𝑚𝑢𝑐ℎ𝑖𝑛𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑜𝑢𝑡𝑜𝑓𝑡ℎ𝑎𝑡.𝐴𝑛𝑑𝑠𝑜𝑡ℎ𝑒,ℎ𝑒𝑟𝑒′𝑠𝑡ℎ𝑒𝑜𝑡ℎ𝑒𝑟𝑡ℎ𝑖𝑛𝑔𝑡ℎ𝑎𝑡′𝑠𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑖𝑛𝑔𝑎𝑏𝑜𝑢𝑡𝑏𝑜𝑙𝑡𝑐𝑜𝑚𝑝𝑎𝑟𝑒𝑑𝑡𝑜𝑙𝑖𝑘𝑒𝑠𝑜𝑚𝑒𝑡ℎ𝑖𝑛𝑔𝑙𝑖𝑘𝑒𝑐𝑜𝑝𝑖𝑙𝑜𝑡𝑜𝑟𝑤ℎ𝑎𝑡𝑒𝑣𝑒𝑟.𝐴𝑛𝑑𝑡ℎ𝑖𝑠𝑘𝑖𝑛𝑑𝑜𝑓𝑡𝑖𝑒𝑑𝑡ℎ𝑖𝑠,𝑠𝑜𝑟𝑟𝑦,𝑎𝑙𝑖𝑡𝑡𝑙𝑒𝑏𝑖𝑡𝑜𝑓𝑎𝑟𝑜𝑢𝑛𝑑𝑎𝑏𝑜𝑢𝑡𝑤𝑎𝑦𝑡𝑜𝑎𝑛𝑠𝑤𝑒𝑟𝑦𝑜𝑢𝑟𝑞𝑢𝑒𝑠𝑡𝑖𝑜𝑛.𝐵𝑢𝑡𝑏𝑎𝑠𝑖𝑐𝑎𝑙𝑙𝑦𝑤ℎ𝑎𝑡𝑤𝑒𝑒𝑛𝑑𝑒𝑑𝑢𝑝𝑎𝑡𝑡ℎ𝑎𝑡𝑚𝑜𝑚𝑒𝑛𝑡,𝑤𝑒𝑒𝑛𝑑𝑒𝑑𝑢𝑝𝑟𝑒𝑎𝑙𝑖𝑧𝑖𝑛𝑔𝑖𝑠𝑡ℎ𝑎𝑡𝑤ℎ𝑒𝑛𝑦𝑜𝑢𝑢𝑠𝑒𝑐𝑜𝑝𝑖𝑙𝑜𝑡,𝑤ℎ𝑎𝑡𝑖𝑡′𝑠𝑠𝑒𝑛𝑑𝑖𝑛𝑔𝑢𝑝,𝑖𝑡𝑑𝑜𝑒𝑠𝑛′𝑡𝑝𝑟𝑜𝑣𝑖𝑑𝑒𝑎𝑙𝑜𝑡𝑜𝑓𝑐𝑜𝑛𝑡𝑒𝑥𝑡𝑜𝑓𝑦𝑜𝑢𝑟𝑐𝑜𝑑𝑒𝑏𝑎𝑠𝑒.𝑇ℎ𝑒𝑦𝑡𝑟𝑦𝑎𝑛𝑑𝑟𝑒𝑑𝑢𝑐𝑒𝑡ℎ𝑒𝑎𝑚𝑜𝑢𝑛𝑡𝑜𝑓𝑐𝑜𝑛𝑡𝑒𝑥𝑡𝑎𝑠𝑚𝑢𝑐ℎ𝑎𝑠𝑡ℎ𝑒𝑦𝑐𝑎𝑛.𝐴𝑛𝑑𝐼𝑡ℎ𝑖𝑛𝑘,𝑦𝑜𝑢𝑘𝑛𝑜𝑤,𝑡ℎ𝑒𝑜𝑟𝑖𝑔𝑖𝑛𝑠𝑜𝑓𝑡ℎ𝑖𝑠𝑠𝑡𝑢𝑓𝑓𝑖𝑠𝑡ℎ𝑒𝑦,𝑒𝑣𝑒𝑟𝑦𝑜𝑛𝑒𝑘𝑖𝑛𝑑𝑜𝑓𝑤𝑎𝑛𝑡𝑠𝑡ℎ𝑖𝑠𝑙𝑖𝑘𝑒𝑙𝑜𝑤𝑝𝑟𝑖𝑐𝑒𝑝𝑜𝑖𝑛𝑡𝑤ℎ𝑒𝑟𝑒𝑖𝑡′𝑠𝑙𝑖𝑘𝑒𝑎𝑙𝑙𝑦𝑜𝑢𝑐𝑎𝑛𝑒𝑎𝑡.𝑆𝑜𝑖𝑡𝑗𝑢𝑠𝑡𝑘𝑖𝑛𝑑𝑜𝑓,𝑡ℎ𝑎𝑡𝑘𝑖𝑛𝑑𝑜𝑓𝑓𝑒𝑒𝑙𝑠𝑙𝑖𝑘𝑒,𝑐𝑎𝑢𝑠𝑒𝑖𝑡′𝑠𝑙𝑖𝑘𝑒,𝑖𝑡𝑎𝑙𝑚𝑜𝑠𝑡𝑙𝑖𝑘𝑒𝑁𝑒𝑡𝑓𝑙𝑖𝑥,𝑖𝑡′𝑠𝑙𝑖𝑘𝑒,𝐼′𝑙𝑙𝑝𝑎𝑦𝑎𝑡ℎ𝑖𝑛𝑔.𝐴𝑛𝑑𝑡ℎ𝑒𝑛𝐼𝑐𝑎𝑛𝑗𝑢𝑠𝑡𝑑𝑜𝑎𝑠𝑚𝑢𝑐ℎ𝑜𝑓𝑡ℎ𝑒𝑚𝑜𝑣𝑖𝑒𝑤𝑎𝑡𝑐ℎ𝑖𝑛𝑔𝑎𝑠𝐼𝑤𝑎𝑛𝑡.𝐴𝑛𝑑𝐼𝑡ℎ𝑖𝑛𝑘,𝐼𝑡ℎ𝑖𝑛𝑘𝑡ℎ𝑎𝑡,𝑡ℎ𝑎𝑡𝑘𝑖𝑛𝑑𝑜𝑓𝑚𝑒𝑛𝑡𝑎𝑙𝑖𝑡𝑦,𝑤ℎ𝑒𝑛𝑡ℎ𝑒𝑠𝑒𝑓𝑖𝑟𝑠𝑡𝐴𝐼𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑠𝑐𝑎𝑚𝑒,𝑖𝑡𝑘𝑖𝑛𝑑𝑜𝑓𝑚𝑎𝑘𝑒𝑠𝑠𝑒𝑛𝑠𝑒.𝑇ℎ𝑒𝑦′𝑟𝑒𝑙𝑖𝑘𝑒,𝑜𝑘𝑎𝑦,𝑤𝑒𝑙𝑙𝑤𝑒,𝑤𝑒𝑑𝑜𝑛′𝑡𝑤𝑎𝑛𝑡𝑡𝑜𝑚𝑒𝑡𝑒𝑟𝑖𝑡.𝐶𝑎𝑢𝑠𝑒𝑡ℎ𝑎𝑡𝑑𝑜𝑒𝑠𝑛′𝑡𝑓𝑒𝑒𝑙𝑔𝑜𝑜𝑑.𝑅𝑖𝑔ℎ𝑡.𝐵𝑢𝑡𝑡ℎ𝑒𝑝𝑟𝑜𝑏𝑙𝑒𝑚𝑖𝑠𝑡ℎ𝑎𝑡𝑡ℎ𝑒𝑛𝑡ℎ𝑒𝑦′𝑟𝑒𝑖𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑖𝑧𝑒𝑑𝑡𝑜𝑛𝑜𝑡ℎ𝑎𝑣𝑒𝑖𝑡𝑏𝑒𝑎𝑏𝑙𝑒𝑡𝑜𝑘𝑒𝑒𝑝𝑡ℎ𝑒𝑚𝑜𝑟𝑒𝑐𝑜𝑛𝑡𝑒𝑥𝑡𝑦𝑜𝑢𝑔𝑖𝑣𝑒𝑖𝑡,𝑡ℎ𝑒𝑚𝑜𝑟𝑒𝑖𝑡𝑐𝑎𝑛𝑑𝑜.𝐴𝑛𝑑𝑡ℎ𝑎𝑡′𝑠𝑡ℎ𝑒𝑚𝑎𝑔𝑖𝑐𝑜𝑓𝑤ℎ𝑎𝑡𝑤𝑒′𝑟𝑒𝑑𝑜𝑖𝑛𝑔𝑤𝑖𝑡ℎ𝑏𝑜𝑙𝑑𝑖𝑠𝑤𝑒′𝑟𝑒𝑔𝑖𝑣𝑖𝑛𝑔𝑖𝑡𝑎𝑙𝑙𝑡ℎ𝑒𝑐𝑜𝑛𝑡𝑒𝑥𝑡𝑤𝑒𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑦𝑐𝑎𝑛.𝐴𝑛𝑑𝑡ℎ𝑎𝑡′𝑠𝑤ℎ𝑦𝑦𝑜𝑢𝑐𝑎𝑛𝑔𝑜𝑡𝑜𝑖𝑡𝑎𝑛𝑑𝑠𝑎𝑦,𝑚𝑎𝑘𝑒𝑚𝑒𝑎𝑛𝑅𝑆𝑉𝑃𝑠𝑖𝑡𝑒.𝐴𝑛𝑑𝑖𝑡𝑑𝑜𝑒𝑠𝑛′𝑡𝑏𝑒𝑐𝑎𝑢𝑠𝑒𝑖𝑡ℎ𝑎𝑠𝑐𝑜𝑛𝑡𝑒𝑥𝑡,𝑡ℎ𝑒𝑒𝑛𝑡𝑖𝑟𝑒𝑠𝑡𝑎𝑡𝑒𝑜𝑓𝑡ℎ𝑒𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛,𝑦𝑜𝑢𝑘𝑛𝑜𝑤,𝑒𝑡𝑐𝑒𝑡𝑒𝑟𝑎,𝑒𝑡𝑐𝑒𝑡𝑒𝑟𝑎.𝐴𝑛𝑑𝑡ℎ𝑎𝑡′𝑠𝑤ℎ𝑎𝑡𝑚𝑎𝑘𝑒𝑠𝑖𝑡𝑠𝑜𝑎𝑐𝑐𝑢𝑟𝑎𝑡𝑒.𝑉𝑒𝑟𝑠𝑢𝑠𝑖𝑓𝑦𝑜𝑢𝑔𝑜𝑡𝑜𝑐𝑜−𝑝𝑖𝑙𝑜𝑡𝑎𝑛𝑑𝑠𝑎𝑦𝑡ℎ𝑎𝑡𝑖𝑡,𝑡ℎ𝑒𝑟𝑒′𝑙𝑙𝑏𝑒,𝑦𝑜𝑢𝑘𝑛𝑜𝑤,𝑖𝑡𝑚𝑖𝑔ℎ𝑡𝑝𝑢𝑛𝑐ℎ𝑜𝑢𝑡𝑎𝑟𝑒𝑎𝑐𝑡𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡.𝑇ℎ𝑎𝑡′𝑠𝑡ℎ𝑒𝑏𝑢𝑡𝑡𝑜𝑛𝑡𝑜𝑐𝑟𝑒𝑎𝑡𝑒𝑡ℎ𝑒𝑡ℎ𝑖𝑛𝑔,𝑏𝑢𝑡𝑛𝑜𝑡𝑎𝑐𝑡𝑢𝑎𝑙𝑙𝑦𝑚𝑜𝑟𝑒𝑡ℎ𝑎𝑛𝑡ℎ𝑎𝑡.𝑆𝑜𝑎𝑛𝑦𝑤𝑎𝑦,𝑠𝑜,𝑢𝑚,𝑦𝑜𝑢𝑘𝑛𝑜𝑤,𝑎𝑛𝑑𝑎𝑡𝑡ℎ𝑒𝑡𝑖𝑚𝑒𝑤ℎ𝑒𝑛𝑝𝑒𝑜𝑝𝑙𝑒ℎ𝑎𝑣𝑒𝑏𝑜𝑢𝑔ℎ𝑡𝑡ℎ𝑒9planandthatwasjustthewayitwas.Itwas,itwaskindoflikeour,ourdollar50hotdogatCostco.It′skindoflikethis,this,youknow,justlowprice,just,youknow,it,itwasn′ttheprimaryrevdriverandwejustwantedto,youknow,say,Hey,payforsomemorestorageandprivateprojectsorwhatever.Andsowewenttolaunchboltagain,likeourexpectationwas,Hey,we′llprobablygetagoodnumberofpeoplethat′llsignupandbeexcitedaboutit.Andyouknow,we′renottooconcerned,youknow,we′rejust,we′rejustnot,wewereunpreparedforthetsunamithathit.Andsoaftergoingonlinethefirstweek,wewerelike,wow,thisiscool.There′sa,Imean,itjustkeptgrowing.Andthenoncewehitweektwo,Imean,wewerejustninebuckswas,Imean,it′slikethecheapestAIcodingthingyoucangetmaybeotherthancopilot,butlikewewereoverrunbysupporttickets.AndIjust,andjustthesheervolumeofpeoplecominginanditjustlawsofsupplyanddemand.Wewerelike,okay,thisisn′t,there′snowaywecanscaletomeetthis.Alsothepeoplecominginareburningthroughtheirtokensandthere′snowaytoactuallylikebuymoreofthesethings.Andninebucksisjust,youcan′tgetthatmuchinferenceoutofthat.Andsothe,here′stheotherthingthat′sinterestingaboutboltcomparedtolikesomethinglikecopilotorwhatever.Andthiskindoftiedthis,sorry,alittlebitofaroundaboutwaytoansweryourquestion.Butbasicallywhatweendedupatthatmoment,weendeduprealizingisthatwhenyouusecopilot,whatit′ssendingup,itdoesn′tprovidealotofcontextofyourcodebase.Theytryandreducetheamountofcontextasmuchastheycan.AndIthink,youknow,theoriginsofthisstuffisthey,everyonekindofwantsthislikelowpricepointwhereit′slikeallyoucaneat.Soitjustkindof,thatkindoffeelslike,causeit′slike,italmostlikeNetflix,it′slike,I′llpayathing.AndthenIcanjustdoasmuchofthemoviewatchingasIwant.AndIthink,Ithinkthat,thatkindofmentality,whenthesefirstAIproductscame,itkindofmakessense.They′relike,okay,wellwe,wedon′twanttometerit.Causethatdoesn′tfeelgood.Right.Buttheproblemisthatthenthey′reincentivizedtonothaveitbeabletokeepthemorecontextyougiveit,themoreitcando.Andthat′sthemagicofwhatwe′redoingwithboldiswe′regivingitallthecontextwepossiblycan.Andthat′swhyyoucangotoitandsay,makemeanRSVPsite.Anditdoesn′tbecauseithascontext,theentirestateoftheapplication,youknow,etcetera,etcetera.Andthat′swhatmakesitsoaccurate.Versusifyougotoco−pilotandsaythatit,there′llbe,youknow,itmightpunchoutareactcomponent.That′sthebuttontocreatethething,butnotactuallymorethanthat.Soanyway,so,um,youknow,andatthetimewhenpeoplehaveboughtthe9 plan, they were like, I want to give you more money. I want you to buy more tokens. How do I do that? And so our team scrambled that weekend, we just turned it around and just, you know, we said, okay, well, what do we think is reasonable? And we said, okay, so let's go, you immediately double the prices of the, of the base tier, because it's just not enough what people are getting on for nine bucks. So that'll be, that seems reasonable. It's kind of in line with everyone else. And then we added 50, 100 and $200 plans. Cause we're like, that should be enough. And so, yeah, so that, that's kind of the origins of it. And, and, um, it was, it was people that use it, fall in love with that and they want to use more of it. And the problem is the inference is expensive. And so we're not actually taking, you know, to date on the, on the revenue we've done, we have not really taken a margin at all on this stuff. Cause we're just trying to put all the value back into the folks that are there using the tool and just getting the maximum amount of value out of it. But it's really key to the kind of the magic of the experience. And so the other, the other thing kind of worth mentioning is there's kind of the ARR number, but then we, you can also buy additional tokens, you know, just with usage-based billing effectively. And that's accounting for an additional 20, 30% of, of revenue that's coming to the company. People are actually using this to do their jobs. Like, you think, think about a web development agency before this thing, they're going in using Figma to make a design. They have to pay the designer. They have to like punch that out into code, kind of man. And maybe like co-pilot can help a little bit with punching out this thing that they're coming to this thing. And there's just wild stories online where it's like guy bake, local bakeries, like we need a website. He's like, okay, well, I'm going to charge you a thousand bucks. They're like, okay, that sounds great. Reasonable price. 30 minutes later, he's like, here's a deploy preview of your thing. How does that look? They're like, wow, holy crap. I'm not giving you a thousand bucks. But they did, they were, they were, they were like, this usually takes months, you know? So some of the biggest power users are people that build websites for a living because this is the, the alpha on this is insane.

Alessio [01:14:26]: That's almost like the gap, right? It's like, it used to be that if I ask you before this to do a website and in 30 minutes you return to me and you give me something, I'm like, you know, you're probably just copying something else you've done before, you know, versus now it's almost like, it doesn't really matter how much time it takes you because everybody's going to be so fast with these things. It's more like the value. And that's why when you're pricing TRL, it was almost like, there's only really going to be like either 20𝑎𝑚𝑜𝑛𝑡ℎ𝑢𝑠𝑒𝑟𝑠𝑜𝑟𝑙𝑖𝑘𝑒𝑎𝑡ℎ𝑜𝑢𝑠𝑎𝑛𝑑𝑑𝑜𝑙𝑙𝑎𝑟𝑠𝑎𝑚𝑜𝑛𝑡ℎ𝑢𝑠𝑒𝑟𝑠.𝑌𝑜𝑢𝑘𝑛𝑜𝑤,𝑖𝑡′𝑠𝑎𝑙𝑚𝑜𝑠𝑡𝑙𝑖𝑘𝑒𝑤ℎ𝑜′𝑠𝑔𝑜𝑖𝑛𝑔𝑡𝑜𝑢𝑠𝑒𝑡ℎ𝑒20amonthusersorlikeathousanddollarsamonthusers.Youknow,it′salmostlikewho′sgoingtousethe50 a month because it's kind of like in between, between being infrequent user and being like a power user, you know? So yeah, it makes sense that you have like a big part of like on demand

Eric [01:15:05]: on top of that. Yeah. And on the 50, there's actually a lot of people on the one. I think it's because it's like enough to actually like for developers are using this to just kind of like punch out components or designs or whatever, kind of gets them enough for, you know, kind of in a given month or whatever. And so it's been interesting to just kind of see the, the, you know, the, the upgrades that happen, but what's been kind of cool about the product is it's, and again, I think this is kind of novel and this is, you know, us being maybe a little more transparent than we should be or something, but like, I suspect we're just, I think we're going to see a lot more of this because we're hitting an inflection point coming back to the co-pilot thing. Part of the problem before is that it didn't matter if you provided more context, the models just weren't good enough to know what to even do with it. That's not the case now. You know, just one, one, you know, story of like one of the first people, one of the power, first power users that adopted Bolt was this gal in Thailand who's a PM at a software banking company. And she had an idea for this app called viralhooks.ai, which is basically, it's a tool that if you want to make viral TikToks and stuff, it's like, what's the hook of the video to make people watch. Right. And so basically she, you know, you can go and like, see, it goes and extracts hooks from other people's videos and helps you with like, you know, AI to write your own. And she had originally put the week before Bolt launched, she put that on Upwork and you know, some, I think a developer in like Ukraine had quoted her, you know, 5,000.𝐴𝑛𝑑𝑖𝑡′𝑠𝑔𝑜𝑖𝑛𝑔𝑡𝑜𝑡𝑎𝑘𝑒𝑙𝑖𝑘𝑒𝑡ℎ𝑟𝑒𝑒𝑚𝑜𝑛𝑡ℎ𝑠𝑜𝑟𝑠𝑜𝑚𝑒𝑡ℎ𝑖𝑛𝑔𝑙𝑖𝑘𝑒𝑡ℎ𝑎𝑡.𝑅𝑒𝑎𝑠𝑜𝑛𝑎𝑏𝑙𝑒𝑡𝑖𝑚𝑒𝑓𝑟𝑎𝑚𝑒,𝑟𝑖𝑔ℎ𝑡.𝐹𝑜𝑟𝑎𝑛𝑎𝑝𝑝𝑙𝑖𝑘𝑒𝑡ℎ𝑎𝑡,𝑟𝑒𝑎𝑠𝑜𝑛𝑎𝑏𝑙𝑒𝑝𝑟𝑖𝑐𝑒.𝑇ℎ𝑒𝑤𝑒𝑒𝑘𝑎𝑓𝑡𝑒𝑟𝑡ℎ𝑎𝑡𝐵𝑜𝑙𝑡𝑐𝑎𝑚𝑒𝑜𝑢𝑡,𝑠ℎ𝑒𝑏𝑜𝑢𝑔ℎ𝑡𝑡ℎ𝑒5,000.Andit′sgoingtotakelikethreemonthsorsomethinglikethat.Reasonabletimeframe,right.Foranapplikethat,reasonableprice.TheweekafterthatBoltcameout,sheboughtthe50 plan and she had the app built within a week or two. And so you're talking about like, that's it. And it's beautiful. She did an incredible job. Right. And so the numbers are wild. 5,000,𝑡ℎ𝑟𝑒𝑒𝑚𝑜𝑛𝑡ℎ𝑠𝑡𝑜5,000,threemonthsto50 and like a week. Yeah. You got to charge more. So it's, it's kind of like, so there's, there's people like when we've had a lot of people go, this pricing is insane. And we're like, well, we're not even taking really a margin at the moment on it, you know, but also, but when you, when you compare that to the price of actually going and building the cost of building quality software today, anyone who knows the price of building quality software, the alpha is obvious, right? It's a 99% cost production and five X faster, you know, delivery time, you know? So anyway, so that's, I think we're one of the first products that have actually come out kind of proving that, you know, in, in, in a revenue way to kind of underscore the point, as you can imagine, we've had, you know, kind of venture capital firms kind of reach out and kind of, you know, curious to kind of, you know, what we're up to or whatever. And so, you know, one of the most, you know, there's kind of one of the, the most notable ones or whatever reached out. So we kind of sent them, you know, you know, kind of our numbers. Actually it was the investor update, Sean, that, that I think you, you know, the, you know, the one you saw kind of gave him a snapshot of it. And they one of their analysts accidentally replied all on what we had sent them and with, with the analysis. And so on this part there, you know, one of the things they said was we haven't seen anything that's kind of eyeopening to see people going to $200 tier on this sort of thing. Haven't seen anything else like that in the space. Cause I think this is very new because of the new model capabilities, right? Where people, you know, it makes sense. Like you're willing to pay more money for this stuff. So. This is something I've talked about before in terms of matching

Swyx [01:18:11]: the dollar amount of spend to the capabilities of the AIs. The chart that I published in the past was, you know, OpenAI has like five levels of AGI-ness and level, level one is sort of like a chatbots, level two is reasoning, level three is agents, four is organizations, five is some, something super, super human. I don't remember what the exact levels are, but each, you can sort of each match each of them with like tiers. Like 20𝑖𝑠𝑙𝑖𝑘𝑒𝑡ℎ𝑒𝑐ℎ𝑎𝑡𝐺𝐵𝑇𝑡𝑖𝑒𝑟.20islikethechatGBTtier.200 is where you're at. 2,000𝑖𝑠ℎ𝑖𝑔ℎ𝑒𝑟,2,000ishigher,20,000, $200,000, right? Like you can see levels where it makes sense. I think BrightWave is also there, by the way. Like I don't know what BrightWave charges, but it's higher, right? Than a chatGBT. And like, you have to deliver more value for that, but you, you can do it now. Yep. So then why not? Everyone should do it.

Eric [01:18:58]: I think we're going to see a lot more of this. I think we're going to see, I think, you know, for AI, Cogen specifically, this is the first moment where I think that there's been that moment where it goes from zero to one, where it's like, yep. The price point, you know, the value, the value is so, like what you can get out of these things is so much higher than it was, you know, three, six months ago that I think we're going to see, I think we're going to see a lot more of this. Like we might, you know, Bolt is, I think one of the first things, but yeah, I mean, it's just, to me, it's inevitable that we're going to see many more things kind of leveraging this, this sort of use case and the amount of efficiency you can get out of using

Alessio [01:19:38]: these systems. Right. So yeah. Yeah. Yeah. Because I mean, the Bolt arbitrage would be quote the price based on the query, you know, you're selling high value tokens. Yeah. It's like, Hey, it's like your mom is like, you wouldn't charge your mom $2,000 to tell her stories, but like, you know, this person doing an app and like a product on it. Yeah. You got to pay more, you know, but it's hard right now. I understand. It's like, it's really hard to figure out how much you can push it, how much value the person will get out

Swyx [01:20:04]: of the thing. Yeah. So I want to riff a little bit on like stuff like this, right? I think you nailed a lot with the design system. You know, one of the differences between open source Bolt and the one that you have is actually like you, you spend a lot of time on the design system. I think, right. Most things just look great when they come out, but I think there's also a whole backend portion that they need. Was that a challenge? Is there anything that you sort of like figuring out that you want to riff on? Yeah. So I think one of the main things,

Eric [01:20:28]: I think you hit the nail on the head, which is, you know, kind of going into putting Bolt online. We originally, again, we've been selling to developers and so we were kind of like, this is a tool for prototyping and they'll download their code. But we ended up finding in the early user testing was how important the deployment story was and how, and this is something you said to me specifically, you're like backend, this needs to like backend needs to be part of this, like logging in, like off just to triple confirm you're dead right. That has been the absolute number one thing that folks coming to Bolt, you know, are looking to do is build a real app with a backend, with billing. And so one of this guy, Mauricio, he's one of our power users. He's like, there's three things that like every app that I'll ever want to build in Bolt, any of these other people in this community, you know, three things, a database, auth, and payments. So those three things, right. So that's- Admin dashboard. We can do that pretty decently, pretty decently. As in every database needs a WP admin. Yes. Yes. Correct. Totally. Totally. And so, yeah, today I think like viral hooks, for example, I think she's using Firebase for auth and database and that sort of thing. You know, so I think Firebase and Superbase, those are the two things that, that just work incredibly well. And so that's actually the point where we're at now, where, you know, right now it's, you know, folks have to still, you know, kind of go to Superbase, manually spin up a thing, come back to Bolt, but the thing that, you know, it's like that sort of processing thing with Firebase, each of those products are going to have their own little quirks that you have to, there's like kind of steps, right. And so- Boltbase. Yeah. Boltbase. Yeah. I think, yeah, I think initially we're like, okay, there should just be a way to like, for Bolt to just go and spin up these things on their behalf and just, and just, you know, both of them have APIs to do so. I'll go even further, like have like pre-warm

Swyx [01:22:12]: instances that you just assign, like it's already spun up, right. So it's, so it's like kind of serverless feeling, even as like, not really, but like yeah, just pre-warm and then just kind of assign it when, whenever someone like- That's a really great point. Yeah. Just keep, keep one

Eric [01:22:26]: Firebase in the hopper, basically. One, 10, 100, I don't know. More generally, this is what I felt

Swyx [01:22:32]: that I wanted to do on our call, which is like, when you have PMF, yes, you want to invest some time in like understanding your customers and do a data analytics and like tighten, tighten things up in general, like tighten up the pricing, tighten up the cost and all that. But then like, you also have to work on like, what is next, like the next level and growth, like you can still inflect. Yeah. I don't know what that is, but you know, I wanted to, I wanted to keep pushing you and I don't know if I did, mostly because I was serving as facilitator on that call. That's what I think. Like, I think you got to still keep pushing the frontier and I don't know what it, what it is, but like, you know, I want to hear what you got thinking about.

Eric [01:23:07]: I think there's, you know, we've addressed just a lot of the low hanging P0 stuff then, and we've actually seen, we've kind of the, you know, there's, there's key moments where it's just kind of like been going like that, which has been cool. Cause it's like, okay, well we were, we're just getting started. This is just the, this is just the fixing obvious things part. Fundamentally, I think a lot, what a lot of people are coming here to do is just, how can we just make it faster to go from idea to production? And a lot of it is like, I had, when I have to go to Firebase, Superbase, spin something up, run a migrate, you know, like add a table, but it's like the agent can do that, you know, so that stuff should be baked in. Yeah. And same thing with the deployment side. It's like right now it's going to Netlify, but people have to create a Netlify account and go and do that. Right. And so I think one of the things we're going to end up doing here is just having the hosting be baked in. And so I've been talking with Matt over at Netlify about this, cause they actually have a way to kind of white label stuff. And so, cause people are, they're just going to make a website, you know? And so it's I mean, that means also you take over domain registration. Can you imagine, right? Like a couple of months from now, you come to this thing, you're like, I want to make, I want to make an RSVP site. Right. And it's like, great. Do you, you know, do you have a name for it? Or do you want to, you know, a domain? You're like, I don't know a name. It's like, well, here's like 10 options and the.coms are able to look good. Yep. That one does. Okay. We want to buy it. Okay, great. It bought the DNS is pointed at the thing. Should we start building this? Okay. Does this look good? Yep. Okay. Am I okay to push this to prod? Yep. That looks good. You know, like that's without leaving the product.

Swyx [01:24:31]: Right. So to me, like it's tomorrow was the first to actually say like you are the new Wix. I never, I personally never thought about it that way. Wix is a $10 billion company where you want to go, you know, cause you still have a choice here. From what we're hearing from the folks using

Eric [01:24:43]: the product, I think I don't even think Wix is even able to solve their need, you know? But not to say that we don't want to, you know, that, that what you're saying is now we want, but, but I mean, yeah, like I think we want to solve folks problems. And I think that there's a huge gap in the market of being able to build, you know, kind of more sophisticated, high quality software like websites in a way that for someone who's a non-engineer. And so I think there's a huge market for that. And obviously, even if you're trying to build a wedding website, yeah, this is, this is easier and faster. Right. So I love it. I, you know, again, coming to the origins of why Albert, my co-founder and I are doing this is we've always just loved building stuff on the web. It's like this, I, this is the tool from what, even when stack was just the IDE interface to the technology, it's like, this is the thing we wish we had when we were 13 years old, you know? And with Bolt, oh my God, if this is the thing I wish we had when we were 13 years old, I'm so glad that my daughter's going to have this thing, you know? So anyways, yeah, I think it makes me pretty, pretty stoked that people are going to be able to actually build amazing web applications that can do really sophisticated things, you know? So yes, I think the short answer is heck yeah. I mean, yeah, that sort of market and totally right up our alley. One other angle that I wanted to pursue was

Swyx [01:25:53]: also the other languages. You know, you're very JavaScript centric. We've talked about Python forever. Ruby maybe, is that important? You know, like the previous generation of site builders were mostly Ruby shops and some PHP. Do we want to capture that or are we just like, you know, always been on JavaScript and just let JavaScript take over the world? You know, I think, I think

Eric [01:26:14]: we're, we're, we're certainly with great interest interested in other languages and we have like minimal support of Python and some C++ stuff in web container that you can like run or whatever. I think especially with the, with the stuff we're seeing though, it's the languages is kind of ancillary to the, to the, to the thing. Well, there's the ecosystem of like,

Swyx [01:26:31]: I want to end up with a code base that I can hire humans on to do the stuff that Bolt cannot do.

Eric [01:26:36]: Yeah, true. And I think, I think in that sense, like the, the, the JavaScript Node.js ecosystem is huge and well-established. So it's like, I think it'd be certainly be able to get people to work on this stuff. And I think the only thing that would be missing is it's like, are you building web apps that where a lot of the functionality is only in libraries that are in Python or something. Right. And I think just kind of seeing the applications that are being built here at, you know, I think that'd be like data science and like ML and that sort of thing. And so that's, we're not seeing a lot of that stuff, you know? And then, but I think that's like, we're like kind of a more generic approach is like what Repl.it's doing where they're spinning up real VMs. You can kind of run anything. And I think they started off with like doing Python service. I actually haven't tried their, their, you know, their new agent stuff that's based on.

Swyx [01:27:15]: Repl.it agent. Yeah. We're close friends. Repl.it has the database, the sort of live hosting, everything integrated that you're going to want to build. And you're, I think you're on a collision course with them, to be honest.

Eric [01:27:29]: We'll see. Cause I'm curious, you're not the first person to say that. I'm curious to see how it shakes out. Cause I think the challenge is focus. You know, when you are, what's kind of the end goal that you're shooting? Yeah, Repl.it's firmly for developers.

Swyx [01:27:45]: You're positioning it for non-developers like that. That's legit.

Eric [01:27:48]: Yeah. And even getting, even if focusing on a language or an ecosystem as well, because again, the problem is that these things can just break in a million ways. And so part of the, a lot of the work in making the experience better, like how do you get, like how make it, someone get an idea into the fingertips and live on prod, right? There's so much stuff in between there. And a lot of it is just errors that happen and how do you handle those? And a lot of that comes down to having a giant database of common errors that you can maybe even fine tune stuff on at some point, right? So doing that on, on one ecosystem, you can move a lot faster than if you're trying to support a lot of different languages. However, it's a, to the point of, if you're kind of targeting developers, they may not need that level of kind of streamline, you know, thing. I think that's kind of where I see the main divergence is that we are unabashedly focused on this ecosystem of, for building web apps. Got it. Yeah. You support it forever. Yeah. And so I'm very curious to see how, just how it all shakes out. Cause it's, I think what they're doing is actually, I mean, I'm very curious to see what Microsoft does because if anyone is good at giving out VMs, tying it to a coder and putting AI in it, it's Sia. He's got a cloud. He's got VS code. They've got code spaces. They've they're in open AI. Now they've got Anthropic and Copilot. I mean, I must imagine, I must imagine that they're cooking stuff over

Swyx [01:29:06]: there, you know? We'll make sure to ask him. We have many friends from Microsoft listening to the

Alessio [01:29:11]: pod. So just to wrap, I don't know, is there anything else Bolt related? I just have one personal question before we wrap the pod. Maybe like just advice, like now that you've

Swyx [01:29:20]: been through this journey, right? Advice to your former self. Oh, okay. Yeah. At which point? Advice yourself, like thinking about, there are many founders out there with a business where they're like, they're working really hard at it. It's interesting, but it's not an AI business. Yeah. And you kind of took the plunge to invest in this and it worked out for you. Maybe a lot of people are like, okay, like, you know, this guy got lucky. Obviously there's a little bit of luck in everything, but like, how do you improve your chances? Like, would you say, go for it? Would you say everyone should go for it? How would you advise someone who was in your shoes and thinking about, you know, maybe I should have a second product. Maybe I should take this, this experiment or maybe it doesn't work out. Like what is, what's the calculus here?

Eric [01:30:01]: Yeah. We were deeply skeptical going. I remember the conversation you and I had, you know, I was like this, I think there's something here. At that point we had built some amount, but I had waited a long time to give you the call. I said, this is your moment. Well, it was. So I remember specifically at the beginning of the conversation with Sean, he and I sat down at a coffee shop and, and, and SF, and, and so I was kind of giving him the pitch of like, you know, I think we have, I think that I can't remember the exact framing. I said, but it's, it's, it was obvious that Sean had heard a lot of people say this exact thing to him over the past year or two, which is like, Hey man, we've gotten AI play. Like this is our thing plus AI equals this, this could be crazy. And Sean, I get, you gave me this like skeptical look and then, and I was like, I really think so. And kind of here's why. Right. And and I think, I think that's, it's actually, I think it's, that is internally having, being skeptical of just kind of going and jumping on hype trains is, is good. Cause it's like, I think you, you know, your focus and your time and what you're putting your weight into is the most important thing when you're a founder. I think for us, like we actually, again, like I had mentioned at the beginning of this, you know, we had tried bold and didn't see the results and that was like a two week sprint and we rolled it back. Right. This, this isn't viable at this point, but then when, you know, once we, once we saw real tangible results of, you know, some of the new stuff, right. Okay. That, that changes. Thanks. And I think a lot of it is, is two is going and finding that out for yourself and then going and talking to the smartest people, you know, with more domain knowledge on that stuff than you have and going, here's kind of what we found. Does this track? So when Sean and I met and he, and he, and you know, we keep, he and I kind of, he saw it, we talked through it and he said, this is your moment. I specifically remember that. Cause I, I walked away from that and I was like, holy s**t, this, this is it. Like this, you know, like Sean's Sean's at the intersection of web and AI and as like, it, you know, has one of the best perspectives on this stuff of, of anyone I know that put a huge wind in our sales, honestly, of just like, okay, let's, let's go and really, let's go and double down here because you know, we had conviction before, but having someone who's in the space independently kind of verify meant a lot, you know, so it makes me uncomfortable, but thank you. I get it. I mean, and I waited, I waited until I was pretty darn sure it was not going to be a waste of time to

Alessio [01:32:12]: cool. Well, that's all I have. Yeah. And then on the personal side, you had a baby in April, you ran an Ironman in October. Now it's November.

Swyx [01:32:20]: He did Ironman while launching ball. I was trying to schedule the call for him and he was like, Nope, I'm sorry. I'm swimming. I was like, Hey, I'm on the swimming session. For those who don't know, actually, I did not know. I don't even know the distance of an Ironman. 13 hours. Your time was 12, 12, 12, 12, 15, 12, 15.

Eric [01:32:41]: Give me my minutes. No, no, I, it's, it can, it can completely depends on, you know, the course and just the, the, the person or whatever, right. And, but yeah, I mean, it's,

Swyx [01:32:51]: it's 2.4 cam open water, 2.4 mile open water swim, a hundred KM, a hundred mile, a hundred KM

Eric [01:32:58]: cycle. I think it's like, I think it's 112 mile a bike and then marathon. Yeah. Full 26.2 mile marathon. Yeah. It was why. Yeah. And you weren't, you were not like a super endurance athlete before, right? Like let's like make this clear. Yeah. Kind of a wild, a wild thing. So I, you know, back when I did, we, we had our daughter in April and at that time we were, the future of the company was, you know, we're, we're figuring out what are we going to do here at that time. It was, it was pro just prior to bolt kind of getting kicked into, you know, the rebirth of it with the new models and stuff. And so I knew that it was going to be, you know, having, having a child is, you know, if you talk to anyone that's done that you're, you don't have a lot of sleep. It's it's, you know, there's a lot of, you know, to, to, to be a great parent is, is a ton of work. And then also being a startup CEO where there's a lot of uncertainty or whatever the way I've always found, like when I have to go and you kind of knock it out of the park and all aspects of my life is, is going, yeah, just to, to make it all aspects of my life. And so I was, I just won. Yeah. I woke up one day, I was like, all right, I'm going to do an Ironman this year and I burned the ships, bought the, it's cost a thousand bucks to do. These didn't know that. And, you know, just started, I'd never ran a marathon at that point. And so I think it was like 45 or 60 days after that, I ran a marathon. My brother-in-law, he's, that was even more insane two weeks before the marathon. I was like, Hey, you want to run a marathon in two weeks? He's like, sure. And, and just did it with me. He did not an endurance athlete either. Right. But anyway, so yeah, so I was training, ended up getting a coach who's usually go, you're kind of online. He's up in Marin. Great guy was on the U S Olympic team for triathlons. And when I told him, okay, I'm going to, I'm doing Ironman, California in three months, he was like, are you insane? You know, like, what are you, you know, you'd ask for my opinion, but like, I just want you to know, I don't think this is a good idea. I think, you know, like you shouldn't do this, et cetera. And I ended up doing it, you know, I ended up getting it done. And so he was like, okay, like that's pretty bad. But what makes you, what makes you ignore expert advice here? Like

Swyx [01:34:59]: most sane people would be, would be like, okay, I mean, you know what you're doing? Like,

Eric [01:35:03]: I'll maybe wait a year. I think, and this is, this is kind of the, and the being a founder, right. It's, it's all about like, if you, like I mentioned earlier, it's like when we talk to people that worked on browser engines, they're like, you can't, you can't build what you're talking about. I think the job of a founder is, is to, is to solicit that advice. And, and what my coach actually said, he was right about certain things. There are certain areas where I was under indexed on, like, I was not, you know, spending nearly enough time on my bike, for example. Like after that, I was on my bike six hours a day on the weekends. That's a lot of time to spend in the saddle. Just like, just kind of, you know, and that was like, you know, for a couple of months leading up to it, he was right on, on certain aspects of it. And, but I kind of had to look internally and go, okay, like, what is he kind of missing about who I am and like, what I kind of know I'm capable of at this point. I mean, it was a nail biter. I mean, going into the thing, you know, it's, you get in, this is the same thing with launching bolt. It's like, or, or launching anything you get launch day, race day, you kind of go in, you're like, all right, here we go. Like we're going to, we're going to find out, we're going to find out, you know, how based in reality I was about all the decisions that led to this moment. And so I was going and doing the Ironman in like six months. Most people spend, you know, the, the folks he trains, usually it's, you know, one to two years on this stuff before you do try and do a full, you know, it's like going and kind of doing in that sort of timeframe. It's, it's, it's very similar to the same sort of skill set of going and building products. You have to really kind of look at the base reality and go make your own assessment on

Alessio [01:36:24]: it. Right. So cool. Great. Sorry to wrap. Thank you so much here. Thanks for your time.

We have announced our first speaker, friend of the show Dylan Patel, and topic slates for Latent Space LIVE! at NeurIPS. Sign up for IRL/Livestream and to debate!

We are still taking questions for our next big recap episode! Submit questions and messages on Speakpipe here for a chance to appear on the show!

The vibe shift we observed in July - in favor of Claude 3.5 Sonnet, first introduced in June — has been remarkably long lived and persistent, surviving multiple subsequent updates of 4o, o1 and Gemini versions, for Anthropic’s Claude to end 2024 as the preferred model for AI Engineers and even being the exclusive choice for new code agents like bolt.new (our next guest on the pod!), which unlocked so much performance from Claude Sonnet that it went from $0 to $4m ARR in 4 weeks when it launched last month.

Anthropic has now raised an additional $4b from Amazon and made an incredibly well received update of Claude 3.5 Sonnet (and Haiku), making significant improvements in performance over its predecessors:

Solving SWE-Bench

As part of the October Sonnet release, Anthropic teased a blink-and-you’ll miss it result:

The updated Claude 3.5 Sonnet shows wide-ranging improvements on industry benchmarks, with particularly strong gains in agentic coding and tool use tasks. On coding, it improves performance on SWE-bench Verified from 33.4% to 49.0%, scoring higher than all publicly available models—including reasoning models like OpenAI o1-preview and specialized systems designed for agentic coding. It also improves performance on TAU-bench, an agentic tool use task, from 62.6% to 69.2% in the retail domain, and from 36.0% to 46.0% in the more challenging airline domain. The new Claude 3.5 Sonnet offers these advancements at the same price and speed as its predecessor.

This was followed up by a blogpost a week later from today’s guest, Erik Schluntz, the engineer who implemented and scored this SOTA result using a simple, non-overengineered version of the SWE-Agent framework (you can see the submissions here). We have previously covered the SWE-Bench story extensively:

* Speaking with SWEBench/SWEAgent authors at ICLR

* Speaking with Cosine Genie, the previous SOTA (43.8%) on SWEBench Verified (with brief update at DevDay 2024)

* Speaking with Shunyu Yao on SWEBench and the ReAct paradigm driving SWE-Agent

One of the notable inclusions in this blogpost are the tools that Erik decided to give Claude, e.g. the “Edit Tool”:

The tools teased in the SWEBench submission/blogpost were then polished up and released with Computer Use…

And you can also see even more computer use tools given in the new Model Context Protocol servers:

Claude Computer Use

Because it is one of the best received AI releases of the year, we recommend watching the 2 minute Computer Use intro (and related demos) in its entirety:

Eric also worked on Claude’s function calling, tool use, and computer use APIs, so we discuss that in the episode.

Erik [00:53:39]: With computer use, just give the thing a browser that's logged into what you want to integrate with, and it's going to work immediately. And I see that reduction in friction as being incredibly exciting. Imagine a customer support team where, okay, hey, you got this customer support bot, but you need to go integrate it with all these things. And you don't have any engineers on your customer support team. But if you can just give the thing a browser that's logged into your systems that you need it to have access to, now, suddenly, in one day, you could be up and rolling with a fully integrated customer service bot that could go do all the actions you care about. So I think that's the most exciting thing for me about computer use, is reducing that friction of integrations to almost zero.

As you’ll see, this is very top of mind for Erik as a former Robotics founder who’s company basically used robots to interface with human physical systems like elevators.

Full Video episode

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Show Notes

* Eric Schluntz

* “Raising the bar on SWE-Bench Verified”

* Cobalt Robotics

* SWE-Bench

* SWE-Bench Verified

* Human Eval & other benchmarks

* Anthropic Workbench

* Aider

* Cursor

* Fireworks AI

* E2B

* Amanda Askell

* Toyota Research

* Physical Intelligence (Pi)

* Chelsea Finn

* Josh Albrecht

* Eric Jang

* 1X

* Dust

* Cosine Episode

* Bolt

* Adept Episode

* TauBench

* LMSys Episode

Timestamps

* [00:00:00] Introductions

* [00:03:39] What is SWE-Bench?

* [00:12:22] SWE-Bench vs HumanEval vs others

* [00:15:21] SWE-Agent architecture and runtime

* [00:21:18] Do you need code indexing?

* [00:24:50] Giving the agent tools

* [00:27:47] Sandboxing for coding agents

* [00:29:16] Why not write tests?

* [00:30:31] Redesigning engineering tools for LLMs

* [00:35:53] Multi-agent systems

* [00:37:52] Why XML so good?

* [00:42:57] Thoughts on agent frameworks

* [00:45:12] How many turns can an agent do?

* [00:47:12] Using multiple model types

* [00:51:40] Computer use and agent use cases

* [00:59:04] State of AI robotics

* [01:04:24] Robotics in manufacturing

* [01:05:01] Hardware challenges in robotics

* [01:09:21] Is self-driving a good business?

Transcript

Alessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners. And today we're in the new studio with my usual co-host, Shawn from Smol AI.

Swyx [00:00:14]: Hey, and today we're very blessed to have Erik Schluntz from Anthropic with us. Welcome.

Erik [00:00:19]: Hi, thanks very much. I'm Erik Schluntz. I'm a member of technical staff at Anthropic, working on tool use, computer use, and Swebench.

Swyx [00:00:27]: Yeah. Well, how did you get into just the whole AI journey? I think you spent some time at SpaceX as well? Yeah. And robotics. Yeah. There's a lot of overlap between like the robotics people and the AI people, and maybe like there's some interlap or interest between language models for robots right now. Maybe just a little bit of background on how you got to where you are. Yeah, sure.

Erik [00:00:50]: I was at SpaceX a long time ago, but before joining Anthropic, I was the CTO and co-founder of Cobalt Robotics. We built security and inspection robots. These are sort of five foot tall robots that would patrol through an office building or a warehouse looking for anything out of the ordinary. Very friendly, no tasers or anything. We would just sort of call a remote operator if we saw anything. We have about 100 of those out in the world, and had a team of about 100. We actually got acquired about six months ago, but I had left Cobalt about a year ago now, because I was starting to get a lot more excited about AI. I had been writing a lot of my code with things like Copilot, and I was like, wow, this is actually really cool. If you had told me 10 years ago that AI would be writing a lot of my code, I would say, hey, I think that's AGI. And so I kind of realized that we had passed this level, like, wow, this is actually really useful for engineering work. That got me a lot more excited about AI and learning about large language models. So I ended up taking a sabbatical and then doing a lot of reading and research myself and decided, hey, I want to go be at the core of this and joined Anthropic.

Alessio [00:01:53]: And why Anthropic? Did you consider other labs? Did you consider maybe some of the robotics companies?

Erik [00:02:00]: So I think at the time I was a little burnt out of robotics, and so also for the rest of this, any sort of negative things I say about robotics or hardware is coming from a place of burnout, and I reserve my right to change my opinion in a few years. Yeah, I looked around, but ultimately I knew a lot of people that I really trusted and I thought were incredibly smart at Anthropic, and I think that was the big deciding factor to come there. I was like, hey, this team's amazing. They're not just brilliant, but sort of like the most nice and kind people that I know, and so I just felt like I could be a really good culture fit. And ultimately, I do care a lot about AI safety and making sure that I don't want to build something that's used for bad purposes, and I felt like the best chance of that was joining Anthropic.

Alessio [00:02:39]: And from the outside, these labs kind of look like huge organizations that have these obscure

Swyx [00:02:44]: ways to organize.

Alessio [00:02:45]: How did you get, you joined Anthropic, did you already know you were going to work on of the stuff you publish or you kind of join and then you figure out where you land? I think people are always curious to learn more.

Erik [00:02:57]: Yeah, I've been very happy that Anthropic is very bottoms up and sort of very sort of receptive to whatever your interests are. And so I joined sort of being very transparent of like, hey, I'm most excited about code generation and AI that can actually go out and sort of touch the world or sort of help people build things. And, you know, those weren't my initial initial projects. I also came in and said, hey, I want to do the most valuable possible thing for this company and help Anthropic succeed. And, you know, like, let me find the balance of those. So I was working on lots of things at the beginning, you know, function calling, tool use. And then sort of as it became more and more relevant, I was like, oh, hey, like, let's it's time to go work on encoding agents and sort of started looking at SWE-Bench as sort of a really good benchmark for that.

Swyx [00:03:39]: So let's get right into SWE-Bench. That's one of the many claims to fame. I feel like there's just been a series of releases related with Cloud 3.5 Sonnet around about two or three months ago, 3.5 Sonnet came out and it was it was a step ahead in terms of a lot of people immediately fell in love with it for coding. And then last month you released a new updated version of Cloud Sonnet. We're not going to talk about the training for that because that's still confidential. But I think Anthropic's done a really good job, like applying the model to different things. So you took the lead on SWE-Bench, but then also we're going to talk a little bit about computer use later on. So maybe just give us a context about why you looked at SWE-Bench Verified and you actually came up with a whole system for building agents that would maximally use the model well. Yeah.

Erik [00:04:28]: So I'm on a sub team called Product Research. And basically the idea of product research is to really understand what end customers care about and want in the models and then work to try to make that happen. So we're not focused on sort of these more abstract general benchmarks like math problems or MMLU, but we really care about finding the things that are really valuable and making sure the models are great at those. And so because I've been interested in coding agents, I knew that this would be a really valuable thing. And I knew there were a lot of startups and our customers trying to build coding agents with our models. And so I said, hey, this is going to be a really good benchmark to be able to measure that and do well on it. And I wasn't the first person at Anthropic to find SWE-Bench, and there are lots of people that already knew about it and had done some internal efforts on it. It fell to me to sort of both implement the benchmark, which is very tricky, and then also to sort of make sure we had an agent and basically like a reference agent, maybe I'd call it, that could do very well on it. Ultimately, we want to provide how we implemented that reference agent so that people can build their own agents on top of our system and get sort of the most out of it as possible. So with this blog post we released on SWE-Bench, we released the exact tools and the prompt that we gave the model to be able to do well.

Swyx [00:05:46]: For people who don't know, who maybe haven't dived into SWE-Bench, I think the general perception is they're like tasks that a software engineer could do. I feel like that's an inaccurate description because it is basically, one, it's a subset of like 12 repos. It's everything they could find that every issue with like a matching commit that could be tested. So that's not every commit. And then SWE-Bench verified is further manually filtered by OpenAI. Is that an accurate description and anything you'd change about that? Yes.

Erik [00:06:14]: SWE-Bench is, it certainly is a subset of all tasks. It's first of all, it's only Python repos, so already fairly limited there. And it's just 12 of these popular open source repos. And yes, it's only ones where there were tests that passed at the beginning and also new tests that were introduced that test the new feature that's added. So it is, I think, a very limited subset of real engineering tasks. But I think it's also very valuable because even though it's a subset, it is true engineering tasks. And I think a lot of other benchmarks are really kind of these much more artificial setups of even if they're related to coding, they're more like coding interview style questions or puzzles that I think are very different from day-to-day what you end up doing. I don't know how frequently you all get to use recursion in your day-to-day job, but whenever I do, it's like a treat. And I think it's almost comical, and a lot of people joke about this in the industry, is how different interview questions are.

Swyx [00:07:13]: Dynamic programming. Yeah, exactly.

Erik [00:07:15]: Like, you code. From the day-to-day job. But I think one of the most interesting things about SWE-Bench is that all these other benchmarks are usually just isolated puzzles, and you're starting from scratch. Whereas SWE-Bench, you're starting in the context of an entire repository. And so it adds this entirely new dimension to the problem of finding the relevant files. And this is a huge part of real engineering, is it's actually pretty rare that you're starting something totally greenfield. You need to go and figure out where in a codebase you're going to make a change and understand how your work is going to interact with the rest of the systems. And I think SWE-Bench does a really good job of presenting that problem.

Alessio [00:07:51]: Why do we still use human eval? It's like 92%, I think. I don't even know if you can actually get to 100% because some of the data is not actually

Swyx [00:07:59]: solvable.

Alessio [00:08:00]: Do you see benchmarks like that, they should just get sunsetted? Because when you look at the model releases, it's like, oh, it's like 92% instead of like 89%, 90% on human eval versus, you know, SWE-Bench verified is you have 49%, right? Which is like, before 45% was state of the art, but maybe like six months ago it was like 30%, something like that. So is that a benchmark that you think is going to replace human eval, or do you think they're just going to run in parallel?

Erik [00:08:27]: I think there's still need for sort of many different varied evals. Like sometimes you do really care about just sort of greenfield code generation. And so I don't think that everything needs to go to sort of an agentic setup.

Swyx [00:08:39]: It would be very expensive to implement.

Erik [00:08:41]: The other thing I was going to say is that SWE-Bench is certainly hard to implement and expensive to run because each task, you have to parse, you know, a lot of the repo to understand where to put your code. And a lot of times you take many tries of writing code, running it, editing it. It can use a lot of tokens compared to something like human eval. So I think there's definitely a space for these more traditional coding evals that are sort of easy to implement, quick to run, and do get you some signal. Maybe hopefully there's just sort of harder versions of human eval that get created.

Alessio [00:09:14]: How do we get SWE-Bench verified to 92%? Do you think that's something where it's like line of sight to it, or it's like, you know, we need a whole lot of things to go right? Yeah, yeah.

Erik [00:09:23]: And actually, maybe I'll start with SWE-Bench versus SWE-Bench verified, which is I think something I missed earlier. So SWE-Bench is, as we described, this big set of tasks that were scraped.

Swyx [00:09:33]: Like 12,000 or something?

Erik [00:09:34]: Yeah, I think it's 2,000 in the final set. But a lot of those, even though a human did them, they're actually impossible given the information that comes with the task. The most classic example of this is the test looks for a very specific error string. You know, like assert message equals error, something, something, something. And unless you know that's exactly what you're looking for, there's no way the model is going to write that exact same error message, and so the tests are going to fail. So SWE-Bench verified was actually made in partnership with OpenAI, and they hired humans to go review all these tasks and pick out a subset to try to remove any obstacle like this that would make the tasks impossible. So in theory, all of these tasks should be fully doable by the model. And they also had humans grade how difficult they thought the problems would be. Between less than 15 minutes, I think 15 minutes to an hour, an hour to four hours, and greater than four hours. So that's kind of this interesting sort of how big the problem is as well. To get to SWE-Bench verified to 90%, actually, maybe I'll also start off with some of the remaining failures that I see when running our model on SWE-Bench. I'd say the biggest cases are the model sort of operates at the wrong level of abstraction. And what I mean by that is the model puts in maybe a smaller band-aid when really the task is asking for a bigger refactor. And some of those, you know, is the model's fault, but a lot of times if you're just sort of seeing the GitHub issue, it's not exactly clear which way you should do. So even though these tasks are possible, there's still some ambiguity in how the tasks are described. That being said, I think in general, language models frequently will produce a smaller diff when possible, rather than trying to do a big refactor. I think another area, at least the agent we created, didn't have any multimodal abilities, even though our models are very good at vision. So I think that's just a missed opportunity. And if I read through some of the traces, there's some funny things where, especially the tasks on matplotlib, which is a graphing library, the test script will save an image and the model will just say, okay, it looks great, you know, without looking at it. So there's certainly extra juice to squeeze there of just making sure the model really understands all the sides of the input that it's given, including multimodal. But yeah, I think like getting to 92%. So this is something that I have not looked at, but I'm very curious about. I want someone to look at, like, what is the union of all of the different tasks that have been solved by at least one attempt at SWE-Bench Verified. There's a ton of submissions to the benchmark, and so I'd be really curious to see how many of those 500 tasks at least someone has solved. And I think, you know, there's probably a bunch that none of the attempts have ever solved. And I think it'd be interesting to look at those and say, hey, is there some problem with these? Like, are these impossible? Or are they just really hard and only a human could do them?

Swyx [00:12:22]: Yeah, like specifically, is there a category of problems that are still unreachable by any LLM agent? Yeah, yeah. And I think there definitely are.

Erik [00:12:28]: The question is, are those fairly inaccessible or are they just impossible because of the descriptions? But I think certainly some of the tasks, especially the ones that the human graders reviewed as like taking longer than four hours are extremely difficult. I think we got a few of them right, but not very many at all in the benchmark.

Swyx [00:12:49]: And did those take less than four hours?

Erik [00:12:51]: They certainly did less than, yeah, than four hours.

Swyx [00:12:54]: Is there a correlation of length of time with like human estimated time? You know what I mean? Or do we have sort of more of X paradox type situations where it's something super easy for a model, but hard for a human?

Erik [00:13:06]: I actually haven't done the stats on that, but I think that'd be really interesting to see of like how many tokens does it take and how is that correlated with difficulty? What is the likelihood of success with difficulty? I think actually a really interesting thing that I saw, one of my coworkers who was also working on this named Simon, he was focusing just specifically on the very hard problems, the ones that are said to take longer than four hours. And he ended up sort of creating a much more detailed prompt than I used. And he got a higher score on the most difficult subset of problems, but a lower score overall on the whole benchmark. And the prompt that I made, which is sort of much more simple and bare bones, got a higher score on the overall benchmark, but lower score on the really hard problems. And I think some of that is the really detailed prompt made the model sort of overcomplicate a lot of the easy problems, because honestly, a lot of the suite bench problems, they really do just ask for a bandaid where it's like, hey, this crashes if this is none, and really all you need to do is put a check if none. And so sometimes trying to make the model think really deeply, it'll think in circles and overcomplicate something, which certainly human engineers are capable of as well. But I think there's some interesting thing of the best prompt for hard problems might not be the best prompt for easy problems.

Alessio [00:14:19]: How do we fix that? Are you supposed to fix it at the model level? How do I know what prompt I'm supposed to use?

Swyx [00:14:25]: Yeah.

Erik [00:14:26]: And I'll say this was a very small effect size, and so I think this isn't worth obsessing over. I would say that as people are building systems around agents, I think the more you can separate out the different kinds of work the agent needs to do, the better you can tailor a prompt for that task. And I think that also creates a lot of like, for instance, if you were trying to make an agent that could both solve hard programming tasks, and it could just write quick test files for something that someone else had already made, the best way to do those two tasks might be very different prompts. I see a lot of people build systems where they first sort of have a classification, and then route the problem to two different prompts. And that's sort of a very effective thing, because one, it makes the two different prompts much simpler and smaller, and it means you can have someone work on one of the prompts without any risk of affecting the other tasks. So it creates like a nice separation of concerns. Yeah.

Alessio [00:15:21]: And the other model behavior thing you mentioned, they prefer to generate like shorter diffs. Why is that? Like, is there a way? I think that's maybe like the lazy model question that people have is like, why are you not just generating the whole code instead of telling me to implement it?

Swyx [00:15:36]: Are you saving tokens? Yeah, exactly. It's like conspiracy theory. Yeah. Yeah.

Erik [00:15:41]: Yeah. So there's two different things there. One is like the, I'd say maybe like doing the easier solution rather than the hard solution. And I'd say the second one, I think what you're talking about is like the lazy model is like when the model says like dot, dot, dot, code remains the same.

Swyx [00:15:52]: Code goes here. Yeah. I'm like, thanks, dude.

Erik [00:15:55]: But honestly, like that just comes as like people on the internet will do stuff like that. And like, dude, if you're talking to a friend and you ask them like to give you some example code, they would definitely do that. They're not going to reroll the whole thing. And so I think that's just a matter of like, you know, sometimes you actually do just, just want like the relevant changes. And so I think it's, this is something where a lot of times like, you know, the models aren't good at mind reading of like which one you want. So I think that like the more explicit you can be in prompting to say, Hey, you know, give me the entire thing, no, no elisions versus just give me the relevant changes. And that's something, you know, we want to make the models always better at following those kinds of instructions.

Swyx [00:16:32]: I'll drop a couple of references here. We're recording this like a day after Dario, Lex Friedman just dropped his five hour pod with Dario and Amanda and the rest of the crew. And Dario actually made this interesting observation that like, we actually don't want, we complain about models being too chatty in text and then not chatty enough in code. And so like getting that right is kind of a awkward bar because, you know, you, you don't want it to yap in its responses, but then you also want it to be complete in, in code. And then sometimes it's not complete. Sometimes you just want it to diff, which is something that Enthopic has also released with a, you know, like the, the fast edit stuff that you guys did. And then the other thing I wanted to also double back on is the prompting stuff. You said, you said it was a small effect, but it was a noticeable effect in terms of like picking a prompt. I think we'll go into suite agent in a little bit, but I kind of reject the fact that, you know, you need to choose one prompt and like have your whole performance be predicated on that one prompt. I think something that Enthopic has done really well is meta prompting, prompting for a prompt. And so why can't you just develop a meta prompt for, for all the other prompts? And you know, if it's a simple task, make a simple prompt, if it's a hard task, make a hard prompt. Obviously I'm probably hand-waving a little bit, but I will definitely ask people to try the Enthopic Workbench meta prompting system if they haven't tried it yet. I went to the Build Day recently at Enthopic HQ, and it's the closest I've felt to an AGI, like learning how to operate itself that, yeah, it's, it's, it's really magical.

Erik [00:17:57]: Yeah, no, Claude is great at writing prompts for Claude.

Swyx [00:18:00]: Right, so meta prompting. Yeah, yeah.

Erik [00:18:02]: The way I think about this is that humans, even like very smart humans still use sort of checklists and use sort of scaffolding for themselves. Surgeons will still have checklists, even though they're incredible experts. And certainly, you know, a very senior engineer needs less structure than a junior engineer, but there still is some of that structure that you want to keep. And so I always try to anthropomorphize the models and try to think about for a human sort of what is the equivalent. And that's sort of, you know, how I think about these things is how much instruction would you give a human with the same task? And do you, would you need to give them a lot of instruction or a little bit of instruction?

Alessio [00:18:36]: Let's talk about the agent architecture maybe. So first, runtime, you let it run until it thinks it's done or it reaches 200k context window.

Swyx [00:18:45]: How did you come up? What's up with that?

Erik [00:18:47]: Yeah.

Swyx [00:18:48]: Yeah.

Erik [00:18:49]: I mean, this, so I'd say that a lot of previous agent work built sort of these very hard coded and rigid workflows where the model is sort of pushed through certain flows of steps. And I think to some extent, you know, that's needed with smaller models and models that are less smart. But one of the things that we really wanted to explore was like, let's really give Claude the reins here and not force Claude to do anything, but let Claude decide, you know, how it should approach the problem, what steps it should do. And so really, you know, what we did is like the most extreme version of this is just give it some tools that it can call and it's able to keep calling the tools, keep thinking, and then yeah, keep doing that until it thinks it's done. And that's sort of the most, the most minimal agent framework that we came up with. And I think that works very well. I think especially the new Sonnet 3.5 is very, very good at self-correction, has a lot of like grit. Claude will try things that fail and then try, you know, come back and sort of try different approaches. And I think that's something that you didn't see in a lot of previous models. Some of the existing agent frameworks that I looked at, they had whole systems built to try to detect loops and see, oh, is the model doing the same thing, you know, more than three times, then we have to pull it out. And I think like the smarter the models are, the less you need that kind of extra scaffolding. So yeah, just giving the model tools and letting it keep sample and call tools until it thinks it's done was the most minimal framework that we could think of. And so that's what we did.

Alessio [00:20:18]: So you're not pruning like bad paths from the context. If it tries to do something, it fails. You just burn all these tokens.

Swyx [00:20:25]: Yes.

Erik [00:20:26]: I would say the downside of this is that this is sort of a very token expensive way to do

Swyx [00:20:29]: this. But still, it's very common to prune bad paths because models get stuck. Yeah.

Erik [00:20:35]: But I'd say that, yeah, 3.5 is not getting stuck as much as previous models. And so, yeah, we wanted to at least just try the most minimal thing. Now, I would say that, you know, this is definitely an area of future research, especially if we talk about these problems that are going to take a human more than four hours. Those might be things where we're going to need to go prune bad paths to let the model be able to accomplish this task within 200k tokens. So certainly I think there's like future research to be done in that area, but it's not necessary to do well on these benchmarks.

Swyx [00:21:06]: Another thing I always have questions about on context window things, there's a mini cottage industry of code indexers that have sprung up for large code bases, like the ones in SweetBench. You didn't need them? We didn't.

Erik [00:21:18]: And I think I'd say there's like two reasons for this. One is like SweetBench specific and the other is a more general thing. The more general thing is that I think Sonnet is very good at what we call agentic search. And what this basically means is letting the model decide how to search for something. It gets the results and then it can decide, should it keep searching or is it done? Does it have everything it needs? So if you read through a lot of the traces of the SweetBench, the model is calling tools to view directories, list out things, view files. And it will do a few of those until it feels like it's found the file where the bug is. And then it will start working on that file. And I think like, again, this is all, everything we did was about just giving Claude the full reins. So there's no hard-coded system. There's no search system that you're relying on getting the correct files into context. This just totally lets Claude do it.

Swyx [00:22:11]: Or embedding things into a vector database. Exactly. Oops. No, no.

Erik [00:22:17]: This is very, very token expensive. And so certainly, and it also takes many, many turns. And so certainly if you want to do something in a single turn, you need to do RAG and just push stuff into the first prompt.

Alessio [00:22:28]: And just to make it clear, it's using the Bash tool, basically doing LS, looking at files and then doing CAD for the following context. It can do that.

Erik [00:22:35]: But it's file editing tool also has a command in it called view that can view a directory. It's very similar to LS, but it just sort of has some nice sort of quality of life improvements. So I think it'll only do an LS sort of two directories deep so that the model doesn't get overwhelmed if it does this on a huge file. I would say actually we did more engineering of the tools than the overall prompt. But the one other thing I want to say about this agentic search is that for SWE-Bench specifically, a lot of the tasks are bug reports, which means they have a stack trace in them. And that means right in that first prompt, it tells you where to go. And so I think this is a very easy case for the model to find the right files versus if you're using this as a general coding assistant where there isn't a stack trace or you're asking it to insert a new feature, I think there it's much harder to know which files to look at. And that might be an area where you would need to do more of this exhaustive search where an agentic search would take way too long.

Swyx [00:23:33]: As someone who spent the last few years in the JS world, it'd be interesting to see SWE-Bench JS because these stack traces are useless because of so much virtualization that we do. So they're very, very disconnected with where the code problems are actually appearing.

Erik [00:23:50]: That makes me feel better about my limited front-end experience, as I've always struggled with that problem.

Swyx [00:23:55]: It's not your fault. We've gotten ourselves into a very, very complicated situation. And I'm not sure it's entirely needed. But if you talk to our friends at Vercel, they will say it is.

Erik [00:24:04]: I will say SWE-Bench just released SWE-Bench Multimodal, which I believe is either entirely JavaScript or largely JavaScript. And it's entirely things that have visual components of them.

Swyx [00:24:15]: Are you going to tackle that? We will see.

Erik [00:24:17]: I think it's on the list and there's interest, but no guarantees yet.

Swyx [00:24:20]: Just as a side note, it occurs to me that every model lab, including Enthopic, but the others as well, you should have your own SWE-Bench, whatever your bug tracker tool. This is a general methodology that you can use to track progress, I guess.

Erik [00:24:34]: Yeah, sort of running on our own internal code base.

Swyx [00:24:36]: Yeah, that's a fun idea.

Alessio [00:24:37]: Since you spend so much time on the tool design, so you have this edit tool that can make changes and whatnot. Any learnings from that that you wish the AI IDEs would take in? Is there some special way to look at files, feed them in?

Erik [00:24:50]: I would say the core of that tool is string replace. And so we did a few different experiments with different ways to specify how to edit a file. And string replace, basically, the model has to write out the existing version of the string and then a new version, and that just gets swapped in. We found that to be the most reliable way to do these edits. Other things that we tried were having the model directly write a diff, having the model fully regenerate files. That one is actually the most accurate, but it takes so many tokens, and if you're in a very big file, it's cost prohibitive. There's basically a lot of different ways to represent the same task. And they actually have pretty big differences in terms of model accuracy. I think Eider, they have a really good blog where they explore some of these different methods for editing files, and they post results about them, which I think is interesting. But I think this is a really good example of the broader idea that you need to iterate on tools rather than just a prompt. And I think a lot of people, when they make tools for an LLM, they kind of treat it like they're just writing an API for a computer, and it's sort of very minimal. It's sort of just the bare bones of what you'd need, and honestly, it's so hard for the models to use those. Again, I come back to anthropomorphizing these models. Imagine you're a developer, and you just read this for the very first time, and you're trying to use it. You can do so much better than just sort of the bare API spec of what you'd often see. Include examples in the description. Include really detailed explanations of how things work. And I think that, again, also think about what is the easiest way for the model to represent the change that it wants to make. For file editing, as an example, writing a diff is actually... Let's take the most extreme example. You want the model to literally write a patch file. I think patch files have at the very beginning numbers of how many total lines change. That means before the model has actually written the edit, it needs to decide how many numbers or how many lines are going to change.

Swyx [00:26:52]: Don't quote me on that.

Erik [00:26:54]: I think it's something like that, but I don't know if that's exactly the diff format. But you can certainly have formats that are much easier to express without messing up than others. And I like to think about how much human effort goes into designing human interfaces for things. It's incredible. This is entirely what FrontEnd is about, is creating better interfaces to kind of do the same things. And I think that same amount of attention and effort needs to go into creating agent computer interfaces.

Swyx [00:27:19]: It's a topic we've discussed, ACI or whatever that looks like. I would also shout out that I think you released some of these toolings as part of computer use as well. And people really liked it. It's all open source if people want to check it out. I'm curious if there's an environment element that complements the tools. So how do you... Do you have a sandbox? Is it just Docker? Because that can be slow or resource intensive. Do you have anything else that you would recommend?

Erik [00:27:47]: I don't think I can talk about sort of public details or about private details about how we implement our sandboxing. But obviously, we need to have sort of safe, secure, and fast sandboxes for training for the models to be able to practice writing code and working in an environment.

Swyx [00:28:03]: I'm aware of a few startups working on agent sandboxing. E2B is a close friend of ours that Alessio has led around in, but also I think there's others where they're focusing on snapshotting memory so that it can do time travel for debugging. Computer use where you can control the mouse or keyboard or something like that. Whereas here, I think that the kinds of tools that we offer are very, very limited to coding agent work cases like bash, edit, you know, stuff like that. Yeah.

Erik [00:28:30]: I think the computer use demo that we released is an extension of that. It has the same bash and edit tools, but it also has the computer tool that lets it get screenshots and move the mouse and keyboard. Yeah. So I definitely think there's sort of more general tools there. And again, the tools we released as part of SweetBench were, I'd say they're very specific for like editing files and doing bash, but at the same time, that's actually very general if you think about it. Like anything that you would do on a command line or like editing files, you can do with those tools. And so we do want those tools to feel like any sort of computer terminal work could be done with those same tools rather than making tools that were like very specific for SweetBench like run tests as its own tool, for instance. Yeah.

Swyx [00:29:15]: You had a question about tests.

Alessio [00:29:16]: Yeah, exactly. I saw there's no test writer tool. Is it because it generates the code and then you're running it against SweetBench anyway, so it doesn't really need to write the test or?

Swyx [00:29:26]: Yeah.

Erik [00:29:27]: So this is one of the interesting things about SweetBench is that the tests that the model's output is graded on are hidden from it. That's basically so that the model can't cheat by looking at the tests and writing the exact solution. And I'd say typically the model, the first thing it does is it usually writes a little script to reproduce the error. And again, most SweetBench tasks are like, hey, here's a bug that I found. I run this and I get this error. So the first thing the model does is try to reproduce that. So it's kind of been rerunning that script as a mini test. But yeah, sometimes the model will like accidentally introduce a bug that breaks some other tests and it doesn't know about that.

Alessio [00:30:05]: And should we be redesigning any tools? We kind of talked about this and like having more examples, but I'm thinking even things of like Q as a query parameter in many APIs, it's like easier for the model to like re-query than read the Q. I'm sure it learned the Q by this point, but like, is there anything you've seen like building this where it's like, hey, if I were to redesign some CLI tools, some API tool, I would like change the way structure to make it better for LLMs?

Erik [00:30:31]: I don't think I've thought enough about that off the top of my head, but certainly like just making everything more human friendly, like having like more detailed documentation and examples. I think examples are really good in things like descriptions, like so many, like just using the Linux command line, like how many times I do like dash dash help or look at the man page or something. It's like, just give me one example of like how I actually use this. Like I don't want to go read through a hundred flags. Just give me the most common example. But again, so you know, things that would be useful for a human, I think are also very useful for a model.

Swyx [00:31:03]: Yeah. I mean, there's one thing that you cannot give to code agents that is useful for human is this access to the internet. I wonder how to design that in, because one of the issues that I also had with just the idea of a suite bench is that you can't do follow up questions. You can't like look around for similar implementations. These are all things that I do when I try to fix code and we don't do that. It's not, it wouldn't be fair, like it'd be too easy to cheat, but then also it's kind of not being fair to these agents because they're not operating in a real world situation. Like if I had a real world agent, of course I'm giving it access to the internet because I'm not trying to pass a benchmark. I don't have a question in there more, more just like, I feel like the most obvious tool access to the internet is not being used.

Erik [00:31:47]: I think that that's really important for humans, but honestly the models have so much general knowledge from pre-training that it's, it's like less important for them. I feel like versioning, you know, if you're working on a newer thing that was like, they came after the knowledge cutoff, then yes, I think that's very important. I think actually this, this is like a broader problem that there is a divergence between Sweebench and like what customers will actually care about who are working on a coding agent for real use. And I think one of those there is like internet access and being able to like, how do you pull in outside information? I think another one is like, if you have a real coding agent, you don't want to have it start on a task and like spin its wheels for hours because you gave it a bad prompt. You want it to come back immediately and ask follow up questions and like really make sure it has a very detailed understanding of what to do, then go off for a few hours and do work. So I think that like real tasks are going to be much more interactive with the agent rather than this kind of like one shot system. And right now there's no benchmark that, that measures that. And maybe I think it'd be interesting to have some benchmark that is more interactive. I don't know if you're familiar with TauBench, but it's a, it's a customer service benchmark where there's basically one LLM that's playing the user or the customer that's getting support and another LLM that's playing the support agent and they interact and try to resolve the issue.

Swyx [00:33:08]: Yeah. We talked to the LMSIS guys. Awesome. And they also did MTBench for people listening along. So maybe we need MTSWE-Bench. Sure. Yeah.

Erik [00:33:16]: So maybe, you know, you could have something where like before the SWE-Bench task starts, you have like a few back and forths with kind of like the, the author who can answer follow up questions about what they want the task to do. And of course you'd need to do that where it doesn't cheat and like just get the exact, the exact thing out of the human or out of the sort of user. But I think that would be a really interesting thing to see. If you look at sort of existing agent work, like a Repl.it's coding agent, I think one of the really great UX things they do is like first having the agent create a plan and then having the human approve that plan or give feedback. I think for agents in general, like having a planning step at the beginning, one, just having that plan will improve performance on the downstream task just because it's kind of like a bigger chain of thought, but also it's just such a better UX. It's way easier for a human to iterate on a plan with a model rather than iterating on the full task that sort of has a much slower time through each loop. If the human has approved this implementation plan, I think it makes the end result a lot more sort of auditable and trustable. So I think there's a lot of things sort of outside of SweetBench that will be very important for real agent usage in the world. Yeah.

Swyx [00:34:27]: I will say also, there's a couple of comments on names that you dropped. Copilot also does the plan stage before it writes code. I feel like those approaches have generally been less Twitter successful because it's not prompt to code, it's prompt plan code. You know, so there's a little bit of friction in there, but it's not much. Like it's, it actually, it's, it, you get a lot for what it's worth. I also like the way that Devin does it, where you can sort of edit the plan as it goes along. And then the other thing with Repl.it, we had a, we hosted a sort of dev day pregame with Repl.it and they also commented about multi-agents. So like having two agents kind of bounce off of each other. I think it's a similar approach to what you're talking about with kind of the few shot example, just as in the prompts of clarifying what the agent wants. But typically I think this would be implemented as a tool calling another agent, like a sub-agent I don't know if you explored that, do you like that idea?

Erik [00:35:20]: I haven't explored this enough, but I've definitely heard of people having good success with this. Of almost like basically having a few different sort of personas of agents, even if they're all the same LLM. I think this is one thing with multi-agent that a lot of people will kind of get confused by is they think it has to be different models behind each thing. But really it's sort of usually the same, the same model with different prompts. And yet having one, having them have different personas to kind of bring different sort of thoughts and priorities to the table. I've seen that work very well and sort of create a much more thorough and thought out

Swyx [00:35:53]: response.

Erik [00:35:53]: I think the downside is just that it adds a lot of complexity and it adds a lot of extra tokens. So I think it depends what you care about. If you want a plan that's very thorough and detailed, I think it's great. If you want a really quick, just like write this function, you know, you probably don't want to do that and have like a bunch of different calls before it does this.

Alessio [00:36:11]: And just talking about the prompt, why are XML tags so good in Cloud? I think initially people were like, oh, maybe you're just getting lucky with XML. But I saw obviously you use them in your own agent prompts, so they must work. And why is it so model specific to your family?

Erik [00:36:26]: Yeah, I think that there's, again, I'm not sure how much I can say, but I think there's historical reasons that internally we've preferred XML. I think also the one broader thing I'll say is that if you look at certain kinds of outputs, there is overhead to outputting in JSON. If you're trying to output code in JSON, there's a lot of extra escaping that needs to be done, and that actually hurts model performance across the board. Versus if you're in just a single XML tag, there's none of that sort of escaping that

Swyx [00:36:58]: needs to happen.

Erik [00:36:58]: That being said, I haven't tried having it write HTML and XML, which maybe then you start running into weird escaping things there. I'm not sure. But yeah, I'd say that's some historical reasons, and there's less overhead of escaping.

Swyx [00:37:12]: I use XML in other models as well, and it's just a really nice way to make sure that the thing that ends is tied to the thing that starts. That's the only way to do code fences where you're pretty sure example one start, example one end, that is one cohesive unit.

Alessio [00:37:30]: Because the braces are nondescriptive. Yeah, exactly.

Swyx [00:37:33]: That would be my simple reason. XML is good for everyone, not just Cloud. Cloud was just the first one to popularize it, I think.

Erik [00:37:39]: I do definitely prefer to read XML than read JSON.

Alessio [00:37:43]: Any other details that are maybe underappreciated? I know, for example, you had the absolute paths versus relative. Any other fun nuggets?

Erik [00:37:52]: I think that's a good sort of anecdote to mention about iterating on tools. Like I said, spend time prompt engineering your tools, and don't just write the prompt, but write the tool, and then actually give it to the model and read a bunch of transcripts about how the model tries to use the tool. I think by doing that, you will find areas where the model misunderstands a tool or makes mistakes, and then basically change the tool to make it foolproof. There's this Japanese term, pokayoke, about making tools mistake-proof. You know, the classic idea is you can have a plug that can fit either way, and that's dangerous, or you can make it asymmetric so that it can't fit this way, it has to go like this, and that's a better tool because you can't use it the wrong way. So for this example of absolute paths, one of the things that we saw while testing these tools is, oh, if the model has done CD and moved to a different directory, it would often get confused when trying to use the tool because it's now in a different directory, and so the paths aren't lining up. So we said, oh, well, let's just force the tool to always require an absolute path, and then that's easy for the model to understand. It knows sort of where it is. It knows where the files are. And then once we have it always giving absolute paths, it never messes up even, like, no matter where it is because it just, if you're using an absolute path, it doesn't matter where

Swyx [00:39:13]: you are.

Erik [00:39:13]: So iterations like that, you know, let us make the tool foolproof for the model. I'd say there's other categories of things where we see, oh, if the model, you know, opens vim, like, you know, it's never going to return. And so the tool is stuck.

Swyx [00:39:28]: Did it get stuck? Yeah. Get out of vim. What?

Erik [00:39:31]: Well, because the tool is, like, it just text in, text out. It's not interactive. So it's not like the model doesn't know how to get out of vim. It's that the way that the tool is, like, hooked up to the computer is not interactive. Yes, I mean, there is the meme of no one knows how to get out of vim. You know, basically, we just added instructions in the tool of, like, hey, don't launch commands that don't return.

Swyx [00:39:54]: Yeah, like, don't launch vim.

Erik [00:39:55]: Don't launch whatever. If you do need to do something, you know, put an ampersand after it to launch it in the background. And so, like, just, you know, putting kind of instructions like that just right in the description for the tool really helps the model. And I think, like, that's an underutilized space of prompt engineering, where, like, people might try to do that in the overall prompt, but just put that in the tool itself so the model knows that it's, like, for this tool, this is what's relevant.

Swyx [00:40:20]: You said you worked on the function calling and tool use before you actually started this vBench work, right? Was there any surprises? Because you basically went from creator of that API to user of that API. Any surprises or changes you would make now that you have extensively dog-fooded in a state-of-the-art agent?

Erik [00:40:39]: I want us to make, like, maybe, like, a little bit less verbose SDK. I think some way, like, right now, it just takes, I think we sort of force people to do the best practices of writing out sort of these full JSON schemas, but it would be really nice if you could just pass in a Python function as a tool. I think that could be something nice.

Swyx [00:40:58]: I think that there's a lot of, like, Python- There's helper libraries. ... structure, you know. I don't know if there's anyone else that is specializing for Anthropic. Maybe Jeremy Howard's and Simon Willis's stuff. They all have Cloud-specific stuff that they are working on. Cloudette. Cloudette, exactly. I also wanted to spend a little bit of time with SuiteAgent. It seems like a very general framework. Like, is there a reason you picked it apart from it's the same authors as vBench, or?

Erik [00:41:21]: The main thing we wanted to go with was the same authors as vBench, so it just felt sort of like the safest, most neutral option. And it was, you know, very high quality. It was very easy to modify, to work with. I would say it also actually, their underlying framework is sort of this, it's like, you

Swyx [00:41:39]: know, think, act, observe.

Erik [00:41:40]: That they kind of go through this loop, which is like a little bit more hard-coded than what we wanted to do, but it's still very close. That's still very general. So it felt like a good match as sort of the starting point for our agent. And we had already sort of worked with and talked with the SWE-Bench people directly, so it felt nice to just have, you know, we already know the authors. This will be easy to work with.

Swyx [00:42:00]: I'll share a little bit of like, this all seems disconnected, but once you figure out the people and where they go to school, it all makes sense. So it's all Princeton. Yeah, the SWE-Bench and SuiteAgent.

Erik [00:42:11]: It's a group out of Princeton.

Swyx [00:42:12]: Yeah, and we had Shun Yu on the pod, and he came up with the React paradigm, and that's think, act, observe. That's all React. So they're all friends. Yep, yeah, exactly.

Erik [00:42:22]: And you know, if you actually read our traces of our submission, you can actually see like think, act, observe in our logs. And we just didn't even change the printing code. So it's like doing still function calls under the hood, and the model can do sort of multiple function calls in a row without thinking in between if it wants to. But yeah, so a lot of similarities and a lot of things we inherited from SuiteAgent just as a starting point for the framework.

Alessio [00:42:47]: Any thoughts about other agent frameworks? I think there's, you know, the whole gamut from very simple to like very complex.

Swyx [00:42:53]: Autogen, CooEI, LandGraph. Yeah, yeah.

Erik [00:42:56]: I think I haven't explored a lot of them in detail. I would say with agent frameworks in general, they can certainly save you some like boilerplate. But I think there's actually this like downside of making agents too easy, where you end up very quickly like building a much more complex system than you need. And suddenly, you know, instead of having one prompt, you have five agents that are talking to each other and doing a dialogue. And it's like, because the framework made that 10 lines to do, you end up building something that's way too complex. So I think I would actually caution people to like try to start without these frameworks if you can, because you'll be closer to the raw prompts and be able to sort of directly understand what's going on. I think a lot of times these frameworks also, by trying to make everything feel really magical, you end up sort of really hiding what the actual prompt and output of the model is, and that can make it much harder to debug. So certainly these things have a place, and I think they do really help at getting rid of boilerplate, but they come with this cost of obfuscating what's really happening and making it too easy to very quickly add a lot of complexity. So yeah, I would recommend people to like try it from scratch, and it's like not that bad.

Alessio [00:44:08]: Would you rather have like a framework of tools? Do you almost see like, hey, it's maybe easier to get tools that are already well curated, like the ones that you build, if I had an easy way to get the best tool from you, and

Swyx [00:44:21]: like you maintain the definition?

Alessio [00:44:22]: Or yeah, any thoughts on how you want to formalize tool sharing?

Erik [00:44:26]: Yeah, I think that's something that we're certainly interested in exploring, and I think there is space for sort of these general tools that will be very broadly applicable. But at the same time, most people that are building on these, they do have much more specific things that they're trying to do. You know, I think that might be useful for hobbyists and demos, but the ultimate end applications are going to be bespoke. And so we just want to make sure that the model's great at any tool that it uses. But certainly something we're exploring.

Alessio [00:44:52]: So everything bespoke, no frameworks, no anything.

Swyx [00:44:55]: Just for now, for now.

Erik [00:44:56]: Yeah, I would say that like the best thing I've seen is people building up from like, build some good util functions, and then you can use those as building blocks. Yeah, yeah.

Alessio [00:45:05]: I have a utils folder, or like all these scripts. My framework is like def, call, and tropic. And then I just put all the defaults.

Swyx [00:45:12]: Yeah, exactly. There's a startup hidden in every utils folder, you know? No, totally not. Like, if you use it enough, like it's a startup, you know? At some point. I'm kind of curious, is there a maximum length of turns that it took? Like, what was the longest run? I actually don't.

Erik [00:45:27]: I mean, it had basically infinite turns until it ran into a 200k context. I should have looked this up. I don't know. And so for some of those failed cases where it eventually ran out of context, I mean, it was over 100 turns. I'm trying to remember like the longest successful run, but I think it was definitely over 100 turns that some of the times.

Swyx [00:45:48]: Which is not that much. It's a coffee break. Yeah.

Erik [00:45:52]: But certainly, you know, these things can be a lot of turns. And I think that's because some of these things are really hard, where it's going to take, you know, many tries to do it. And if you think about like, think about a task that takes a human four hours to do. Think about how many different files you read, and like times you edit a file in four hours. That's a lot more than 100.

Alessio [00:46:10]: How many times you open Twitter because you get distracted. But if you had a lot more compute, what's kind of like the return on the extra compute now? So like, you know, if you had thousands of turns or like whatever, like how much better would it get?

Erik [00:46:23]: Yeah, this I don't know. And I think this is, I think sort of one of the open areas of research in general with agents is memory and sort of how do you have something that can do work beyond its context length where you're just purely appending. So you mentioned earlier things like pruning bad paths. I think there's a lot of interesting work around there. Can you just roll back but summarize, hey, don't go down this path? There be dragons. Yeah, I think that's very interesting that you could have something that that uses way more tokens without ever using at a time more than 200k. So I think that's very interesting. I think the biggest thing is like, can you make the model sort of losslessly summarize what it's learned from trying different approaches and bring things back? I think that's sort of the big challenge.

Swyx [00:47:11]: What about different models?

Alessio [00:47:12]: So you have Haiku, which is like, you know, cheaper. So you're like, well, what if I have a Haiku to do a lot of these smaller things and then put it back up?

Erik [00:47:20]: I think Cursor might have said that they actually have a separate model for file editing.

Swyx [00:47:25]: I'm trying to remember.

Erik [00:47:25]: I think they were on maybe the Lex Fridman podcast where they said they have a bigger model, like write what the code should be and then a different model, like apply it. So I think there's a lot of interesting room for stuff like that. Yeah, fast supply.

Swyx [00:47:37]: We actually did a pod with Fireworks that they worked with on. It's speculative decoding.

Erik [00:47:41]: But I think there's also really interesting things about like, you know, paring down input tokens as well, especially sometimes the models trying to read like a 10,000 line file. That's a lot of tokens. And most of it is actually not going to be relevant. I think it'd be really interesting to like delegate that to Haiku. Haiku read this file and just pull out the most relevant functions. And then, you know, Sonnet reads just those and you save 90% on tokens. I think there's a lot of really interesting room for things like that. And again, we were just trying to do sort of the simplest, most minimal thing and show that it works. I'm really hoping that people, sort of the agent community builds things like that on top of our models. That's, again, why we released these tools. We're not going to go and do lots more submissions to SWE-Bench and try to prompt engineer this and build a bigger system. We want people to like the ecosystem to do that on top of our models. But yeah, so I think that's a really interesting one.

Swyx [00:48:32]: It turns out, I think you did do 3.5 Haiku with your tools and it scored a 40.6. Yes.

Erik [00:48:38]: So it did very well. It itself is actually very smart, which is great. But we haven't done any experiments with this combination of the two models. But yeah, I think that's one of the exciting things is that how well Haiku 3.5 did on SWE-Bench shows that sort of even our smallest, fastest model is very good at sort of thinking agentically and working on hard problems. Like it's not just sort of for writing simple text anymore.

Alessio [00:49:02]: And I know you're not going to talk about it, but like Sonnet is not even supposed to be the best model, you know? Like Opus, it's kind of like we left it at three back in the corner intro. At some point, I'm sure the new Opus will come out. And if you had Opus Plus on it, that sounds very, very good.

Swyx [00:49:19]: There's a run with SuiteAgent plus Opus, but that's the official SWE-Bench guys doing it.

Erik [00:49:24]: That was the older, you know, 3.0.

Swyx [00:49:25]: You didn't do yours. Yeah. Okay. Did you want to? I mean, you could just change the model name.

Erik [00:49:31]: I think we didn't submit it, but I think we included it in our model card.

Swyx [00:49:35]: Okay.

Erik [00:49:35]: We included the score as a comparison. Yeah.

Swyx [00:49:38]: Yeah.

Erik [00:49:38]: And Sonnet and Haiku, actually, I think the new ones, they both outperformed the original Opus. Yeah. I did see that.

Swyx [00:49:44]: Yeah. It's a little bit hard to find. Yeah.

Erik [00:49:47]: It's not an exciting score, so we didn't feel like they need to submit it to the benchmark.

Swyx [00:49:52]: We can cut over to computer use if we're okay with moving on to topics on this, if anything else. I think we're good.

Erik [00:49:58]: I'm trying to think if there's anything else SWE-Bench related.

Swyx [00:50:02]: It doesn't have to be also just specifically SWE-Bench, but just your thoughts on building agents, because you are one of the few people that have reached this leaderboard on building a coding agent. This is the state of the art. It's surprisingly not that hard to reach with some good principles. Right. There's obviously a ton of low-hanging fruit that we covered. Your thoughts on if you were to build a coding agent startup, what next?

Erik [00:50:24]: I think the really interesting question for me, for all the startups out there, is this kind of divergence between the benchmarks and what real customers will want. So I'm curious, maybe the next time you have a coding agent startup on the podcast, you should ask them that. What are the differences that they're starting to make? Tomorrow.

Swyx [00:50:40]: Oh, perfect, perfect. Yeah.

Erik [00:50:41]: I'm actually very curious what they will see, because I also have seen, I feel like it's slowed down a little bit if I don't see the startups submitting to SWE-Bench that much anymore.

Swyx [00:50:52]: Because of the traces, the trace. So we had Cosign on, they had a 50-something on full, on SWE-Bench full, which is the hardest one, and they were rejected because they didn't want to submit their traces. Yep. IP, you know? Yeah, that makes sense, that makes sense. Actually, tomorrow we're talking to Bolt, which is a cloud customer. You guys actually published a case study with them. I assume you weren't involved with that, but they were very happy with Cloud. Cool. One of the biggest launches of the year. Yeah, totally. We actually happened to be sitting in Adept's former office. My take on this is Anthropic shipped Adept as a feature. It's still a beta feature, but yes. What was it like when you tried it for the first time? Was it obvious that Cloud had reached that stage where you could do computer use? It was somewhat of a surprise to me.

Erik [00:51:40]: I had been on vacation, and I came back, and everyone's like, computer use works. So it was this very exciting moment. After the first go to Google, I think I tried to have it play Minecraft or something, and it actually installed and opened Minecraft.

Swyx [00:51:54]: I was like, wow, this is pretty cool.

Erik [00:51:55]: So I was like, wow, yeah, this thing can actually use a computer. And certainly, it is still beta. There's certain things that it's not very good at yet. But I'm really excited, I think, most broadly, not just for new things that weren't possible before, but as a much lower friction way to implement tool use. One anecdote from my days at Cobalt Robotics, we wanted our robots to be able to ride elevators, to go between floors and fully cover a building. The first way that we did this was doing API integrations with the elevator companies. Some of them actually had APIs. We could send a request, and it would move the elevator. Each new company we did took six months to do,

Swyx [00:52:37]: because they were very slow.

Erik [00:52:39]: They didn't really care.

Swyx [00:52:40]: Or an elevator, not an API.

Erik [00:52:42]: Even installing, once we had it with the company, they would have to literally go install an API box on the elevator that we wanted to use, and that would sometimes take six months.

Swyx [00:52:51]: So very slow.

Erik [00:52:52]: And eventually, we're like, okay, this is slowing down all of our customer deployments. And I was like, what if we just add an arm to the robot? And I added this little arm that could literally go and press the elevator buttons, and we use computer vision to do this. And we could deploy that in a single day, and have the robot being able to use the elevators. At the same time, it was slower than the API. It wasn't quite as reliable. Sometimes it would miss, and it would have to try to press it again.

Swyx [00:53:20]: But it would get there.

Erik [00:53:20]: But it was slower and a little bit less reliable. And I kind of see this as an analogy to computer use, of anything you can do with computer use today, you could probably write tool use and integrate it with APIs.

Swyx [00:53:33]: It's up to the language model.

Erik [00:53:34]: But that's going to take a bunch of software engineering to write those integrations.

Swyx [00:53:38]: You have to do all this stuff.

Erik [00:53:39]: With computer use, just give the thing a browser that's logged into what you want to integrate with, and it's going to work immediately. And I see that reduction in friction as being incredibly exciting. Imagine a customer support team where, okay, hey, you got this customer support bot, but you need to go integrate it with all these things. And you don't have any engineers on your customer support team. But if you can just give the thing a browser that's logged into your systems that you need it to have access to, now, suddenly, in one day, you could be up and rolling with a fully integrated customer service bot that could go do all the actions you care about. So I think that's the most exciting thing for me about computer use, is reducing that friction of integrations to almost zero.

Alessio [00:54:20]: Or farming on World of Warcraft.

Swyx [00:54:23]: Yes, or that.

Erik [00:54:23]: Just go computer use.

Alessio [00:54:25]: Very high-value use cases.

Swyx [00:54:27]: I always say about this, this is the oldest question in robotics or self-driving, which is, do you drive by vision or do you have special tools? And vision is the universal tool to claim all tools. There's trade-offs, but there's situations in which that will come. But this week's podcast, the one that we just put out, had Stan Polu from Dust saying that he doesn't see a future where it's the significant workhorse. I think there could be a separation between maybe the high-volume use cases. You want APIs. And then the long tail, you want computer use. I totally agree. Right?

Erik [00:55:00]: Or you'll start, you'll prototype something with computer use. And then, hey, this is working. Customers have adopted this feature. OK, let's go turn it into an API. And it'll be faster and use less tokens.

Swyx [00:55:11]: I'd be interested to see a computer use agent replace itself by figuring out the API and then just dropping out of the equation altogether.

Erik [00:55:20]: Yeah, that's really fun, actually.

Swyx [00:55:22]: If I was running an RPA company, you would have the RPA scripting. RPA, for people listening, is robotic process automation, where you would script things that always show up in sequence. So you don't have an LLM in the loop. And so basically what you need to do is train an LLM to code that script. And then you can naturally hand off from computer use to non-computer use.

Erik [00:55:43]: Or have some way to turn Claude's actions of computer use into a saved script that you can then run repeatedly.

Swyx [00:55:49]: Yeah, it'd be interesting to record that.

Alessio [00:55:50]: Why did you decide to not ship any sandbox harness for computer use? It's kind of like, hey, peace.

Swyx [00:55:58]: Run at your own risk. It's Docker, right?

Erik [00:55:59]: No, no, we launched it with, I think, a VM or Docker, a Docker as system.

Alessio [00:56:03]: But it's not for your actual computer, right? The Docker instance runs in the Docker. It's not for...

Swyx [00:56:10]: Yeah, it runs its own browser.

Erik [00:56:13]: I mean, the main reason for that, one, is sort of security. We don't want... The model can do anything. So we wanted to give it a sandbox, not have people do their own computer. At least sort of for our default experience. We really care about providing a nice sort of... Making the default safe, I think, is the best way for us to do it. And I mean, very quickly, people made modifications to let you run it on your own desktop. And that's fine.

Swyx [00:56:37]: Someone else can do that.

Erik [00:56:37]: But we don't want that to be the official, anthropic thing to run. I would say also, from a product perspective, right now, because this is sort of still in beta, I think a lot of the most useful use cases are... Like, a sandbox is actually what you want. You want something where, hey, it can't mess up anything in here. It only has what I gave it. Also, if it's using your computer, you know, you can't use your computer at the same time. I think you actually want it to have its own screen. It's like you and a person pair programming, but only on one laptop versus you have two laptops.

Swyx [00:57:07]: Everyone should totally have a side laptop where the computer uses... Cloud is just doing its thing. Yeah, yeah.

Erik [00:57:11]: I think it's such a better experience. Unless there's something very explicit you want it to do for you on your own computer.

Swyx [00:57:17]: It becomes like you're sort of shelling into a remote machine and, you know, maybe checking in on it every now and then. Like, I have fond memories of... Half our audience is going to be too young to remember this, but Citrix desktop experience, like, you were sort of remote into a machine that someone else was operating. And for a long time, that would be how you did, like, enterprise computing. Yeah, yeah. It's coming back. Any other implications of computer use? You know, is it a fun demo or is it, like, the future of Anthropic? I'm very excited about it.

Erik [00:57:50]: I think that, like, there's a lot of sort of very repetitive work that, like, computer use will be great for. I think I've seen some examples of people build, like, coding agents that then also, like, test the front end that they made. So I think it's very cool to, like, use computer use to be able to close the loop on a lot of things that right now just a terminal-based agent can't do. So I think that's very exciting.

Swyx [00:58:11]: It's kind of like end-to-end testing. Exactly. Yeah, yeah.

Erik [00:58:14]: The end sort of front-end and web testing is something I'm very excited about.

Swyx [00:58:18]: Yeah, I've seen Amanda also talking... This would be Amanda Askell, the head of Cloud Character. She goes on a lunch break and it generates, you know, research ideas for her. Giving it a name like computer use is very practical. It's like you're supposed to do things, but maybe sometimes it's not about doing things, it's about thinking. And thinking... In the process of thinking, you're using the computer. In some way that's, you know, solving SweetBench, like, you should be allowed to use the internet or you should be allowed to use a computer to solve it and use your vision and use whatever. Like, we're just sort of shackling it with all these restrictions just because we want to play nice for a benchmark. But really, you know, a full AI will be able to do all these things. To think. Yeah, we'll definitely be able to. To reason. To Google and search for things.

Erik [00:58:58]: Yeah, yeah. Pull down inspiration.

Alessio [00:59:00]: Can we just do a... before we wrap, a robotics corner?

Swyx [00:59:03]: Oh, yeah, yeah.

Alessio [00:59:04]: People are always curious, especially with somebody that is not trying to hype their own company. What's the state of AI robotics? Under-hyped, over-hyped?

Erik [00:59:12]: Yeah, and I'll say, like, these are my opinions, not Anthropic's. And again, coming from a place of a burned-out robotics founder, so take everything with a grain of salt. I would say on the positives, like, there is really sort of incredible progress that's happened in the last five years that I think will be a big unlock for robotics. The first is just general purpose language models. I mean, there was an old saying in robotics that if to fully describe your task is harder than to just do the task, you can never automate it. Because, like, it's going to take more effort to even tell the robot how to do this thing than to me just do it itself. LLM solved that. I no longer need to go exhaustively program in every little thing I could do. The thing just has common sense. And it's going to know, how do I make a Reuben sandwich? I'm not going to have to go program that in. Whereas before, like, the idea of even, like, a cooking thing, it's like, oh god, like, we're gonna have the team of engineers that are hard coding recipes for the long tail of anything. It would be a disaster. So I think that's one thing, is that bringing common sense really is, like, solves this huge problem of describing tasks. The second big innovation has been diffusion models for path planning. A lot of this work came out of Toyota Research. There's a lot of startups now that are working on this, like Physical Intelligence Pi, Chelsea Finn's startup out of Stanford. And the basic idea here is using a little bit of the, I'd say maybe more inspiration from diffusion rather than diffusion models themselves. But they're a way to basically learn an end-to-end sort of motion control. Whereas previously, all of robotics motion control was sort of very hard-coded. You either, you know, you're programming in explicit motions, or you're programming in an explicit goal and using an optimization library to find the shortest path to it. This is now something where you just give it a bunch of demonstrations. And again, just like using learning, it's basically like learning from these examples. What does it mean to go pick up a cup? And doing these in a way just like diffusion models, where they are somewhat conditioned by text, you can have the same model learn many different tasks. And then the hope is that these start to generalize. That if you've trained it on picking up coffee cups and picking up books, then when I say pick up the backpack, it knows how to do that too. Even though you've never trained it on that. That's kind of the holy grail here, is that you train it on 500 different tasks, and then that's enough to really get it to generalize to do anything you would need. I think that's like still a big TBD. And these people are working, have like measured some degree of generalization. But at the end of the day, it's also like LLMs. Like, you know, do you really care about the thing, being able to do something that no one has ever shown in training data? People for like a home robot, there's going to be like a hundred things that people really wanted to do. And you can just make sure it has good training for those things. What you do care about then is like generalization within a task of, oh, I've never seen this particular coffee mug before. Can I still pick it up? And those, the models do seem very good at. So these kind of are the two big things that are going for robotics right now, is LLMs for common sense and diffusion-inspired path planning algorithms. I think this is very promising, but I think there's a lot of hype. And I think where we are right now is where self-driving cars were 10 years ago. I think we have very cool demos that work. I mean, 10 years ago, you had videos of people driving a car on the highway, driving a car, you know, on a street with a safety driver. But it's really taken a long time to go from there to, I took a Waymo here today. And even Waymo is only in SF and a few other cities. And I think it takes a long time for these things to actually get everywhere and to get all the edge cases covered. I think that for robotics, the limiting factor is going to be reliability, that these models are really good at doing these demos of doing laundry or doing dishes. If they only work 99% of the time, that sounds good, but that's actually really annoying. Humans are really good at these tasks. Imagine if one out of every 100 dishes, it washed, it breaks. You would not want that robot in your house, or you certainly wouldn't want that in your factory if one of every 100 boxes that it moves, it drops and breaks things inside it. So I think for these things to really be useful, they're going to have to hit a very, very high level of reliability, just like self-driving cars. And I don't know how hard it's going to be for these models to move from the 95% reliability to 99.9. I think that's going to be the big thing. And I think also, I'm a little skeptical of how good the unit economics of these things will be. These robots are going to be very expensive to build. And if you're just trying to replace labor, like a one-for-one purchase, it kind of sets an upper cap about how much you can charge. And so it seems like it's not that great a business. I'm also worried about that for the self-driving car industry.

Alessio [01:04:05]: Do you see most of the applications actually taking some of the older, especially manufacturing machinery, which needs to be very precise? Even if it's off by just a few millimeters, it cannot screw up the whole thing and be able to adjust at the edge? Or do you think the net new use cases may be more interesting?

Erik [01:04:24]: I think it'd be very hard to replace a lot of those traditional manufacturing robots because everything relies on that precision. If you have a model that can, again, only get there 99% of the time, you don't want 1% of your cars to have the weld in the wrong spot. That's going to be a disaster. And a lot of manufacturing is all about getting rid of as much variance and uncertainty as

Swyx [01:04:47]: possible.

Erik [01:04:47]: Yeah.

Swyx [01:04:48]: And what about the hardware?

Alessio [01:04:49]: A lot of my friends that work in robotics, one of their big issues is sometimes you just have a servo that fails, and it takes a bunch of time to fix that.

Swyx [01:04:57]: Is that holding back things?

Alessio [01:04:58]: Or is the software still, anyway, not that ready?

Swyx [01:05:01]: I think both.

Erik [01:05:01]: I think there's been a lot more progress in the software in the last few years. And I think a lot of the humanoid robot companies now are really trying to build amazing hardware. Hardware is just so hard. It's something where you build your first robot, and it works. You're like, great. Then you build 10 of them. Five of them work. Three of them work half the time. Two of them don't work. And you built them all the same, and you don't know why. And it's just like the real world has this level of detail and differences that software

Swyx [01:05:28]: doesn't have.

Erik [01:05:29]: Imagine if every for loop you wrote, some of them just didn't work. Some of them were slower than others. How do you deal with that? Imagine if every binary that you shipped to a customer, each of those four loops was a

Swyx [01:05:41]: little different.

Erik [01:05:41]: It becomes just so hard to scale and maintain quality of these things. And I think that's what makes hardware really hard. It's not building one of something, but repeatedly building something and making it work reliably. Where again, you'll buy a batch of 100 motors, and each of those motors will behave a little bit differently to the same input command.

Swyx [01:06:01]: This is your lived experience at Cobalt.

Erik [01:06:03]: And robotics is all about how do you build something that's robust despite these differences.

Swyx [01:06:08]: We can't get the tolerance of motors down to-

Erik [01:06:10]: It's just everything.

Swyx [01:06:13]: It's actually everything.

Alessio [01:06:14]: Yeah.

Erik [01:06:15]: No, I mean, one of my horror stories was that at Cobalt, this was many years ago, we had a thermal camera on the robot that had a USB connection to the computer inside, which is, first of all, is a big mistake. You're not supposed to use a USB. It is not a reliable protocol. It's designed that if there's mistakes, the user can just unplug it and plug it back in. I see. And so typically things that are USB, they're not designed to the same level of very high reliability you need. Again, because they assume someone will just unplug it and replug it back in. You just say someone sometime.

Swyx [01:06:46]: I heard this too, and I didn't listen to it.

Erik [01:06:47]: I really wish I had before. Anyway, at a certain point, a bunch of these thermal cameras started failing, and we couldn't figure out why. And I asked everyone on the team, like, hey, what's changed? Did the software change around this? Did the hardware design change around this? And I was investigating all this stuff, looking at kernel logs of what's happening with this

Swyx [01:07:07]: thing.

Erik [01:07:07]: And finally, the procurement person was like, oh, yeah, well, I found this new vendor for USB cables last summer.

Swyx [01:07:14]: And I'm like, what?

Erik [01:07:15]: You switched which vendor were buying USB cables? I'm like, yeah, it's the same exact cable. It's just a dollar cheaper. And it turns out this was the problem. This new cable had slightly worse resistance or slightly worse EMI interference. And it worked most of the time. But 1% of the time, these cameras would fail, and we'd need to reboot a big part of the system. And it was all just because the same exact spec, these two different USB cables, slightly different. And so these are the kind of things you deal with with hardware.

Swyx [01:07:45]: For listeners, we had an episode with Josh Albrecht in BU where he talked about buying tens of thousands of GPUs. And just some of them will just not do math. Yeah, that's the same thing. You run some tests to find the bad batch, and then you return it to sender because they just, GPUs won't do math, right? Yeah, yeah, this is the thing.

Erik [01:08:05]: The real world has this level of detail. Eric Jang, he did AI at Google.

Swyx [01:08:11]: Yeah, 1X. Yeah, and then joined 1X.

Erik [01:08:13]: I see him post on Twitter occasionally of complaints about hardware and supply chain. And we know each other, and we joke occasionally. I went from robotics into AI, and he went from AI into robotics.

Swyx [01:08:26]: I mean, look, very, very promising. The time of the real world is unlimited, right? But just also a lot harder. And yeah, I do think something I also tell people about for why working software agents is they're infinitely clonable. Yeah, they always work the same way. Mostly, unless you're using Python. And yeah, I mean, this is the whole thesis. I'm also interested, you dropped a little bit of alpha there. I don't want to make sure we don't lose it. Like, you're kind of skeptical about self-driving as a business. So I want to double click on this a little bit, because I mean, I think that shouldn't be taken away. We do have some public Waymo numbers. Read from Waymo is pretty public with their stats. They're exceeding 100 Waymo trips a week. If you assume a 25𝑟𝑖𝑑𝑒𝑎𝑣𝑒𝑟𝑎𝑔𝑒,𝑡ℎ𝑎𝑡′𝑠25rideaverage,that′s130 million revenue run rate. At some point, they will recoup their investment, right? Like, what are we talking about here? Way to skepticism.

Erik [01:09:21]: I think, and again, I'm not an expert. I don't know their financials. I would say the thing I'm worried about is compared to an Uber, I don't know how much an Uber driver takes home a year, but call that the revenue that a Waymo is going to be making in that same year. Those cars are expensive. It's not about if you can hit profitability, it's about your cash conversion cycles. Is building one Waymo, how cheap can you make that compared to how much you're earning as the equivalent of what an Uber driver would take home? Because remember, an Uber driver, you're not getting that whole revenue. You think about, for the Uber driver, the cost of the car, the depreciation of the car. I'm not convinced how much profit Waymo can actually make per car.

Swyx [01:10:02]: That's, I think, my skepticism.

Alessio [01:10:02]: Well, they need to pre-assess the run Waymo because the Class C is like $110 grand, something

Swyx [01:10:09]: like that, plus the LiDAR. That's many years of, yeah, yeah, yeah. Exactly, exactly. Anything else?

Alessio [01:10:14]: Parting thoughts? Call to action? Rants?

Swyx [01:10:18]: The floor is yours.

Erik [01:10:19]: I'm very excited to see a lot more LLM agents out there in the world doing things. And I think they'll be, the biggest limiting thing will start to become, do people trust the output of these agents? And how do you trust the output of an agent that did five hours of work for you and is coming back with something? And if you can't find some way to trust that agent's work, it kind of wasn't valuable at all. So I think that's going to be a really important thing, is not just doing the work, but doing the work in a trustable, auditable way where you can also explain to the human, hey, here's exactly how this works and why and how I came to it. I think that's going to be really important.

Swyx [01:10:54]: Thank you so much. Yeah, thanks. This was great.

We have a full slate of upcoming events: AI Engineer London, AWS Re:Invent in Las Vegas, and now Latent Space LIVE! at NeurIPS in Vancouver and online. Sign up to join and speak!

We are still taking questions for our next big recap episode! Submit questions and messages on Speakpipe here for a chance to appear on the show!

We try to stay close to the inference providers as part of our coverage, as our podcasts with Together AI and Replicate will attest:

However one of the most notable pull quotes from our very well received Braintrust episode was his opinion that open source model adoption has NOT gone very well and is actually declining in relative market share terms (it is of course increasing in absolute terms):

Today’s guest, Lin Qiao, would wholly disagree. Her team of Pytorch/GPU experts are wholly dedicated toward helping you serve and finetune the full stack of open source models from Meta and others, across all modalities (Text, Audio, Image, Embedding, Vision-understanding), helping customers like Cursor and Hubspot scale up open source model inference both rapidly and affordably.

Fireworks has emerged after its successive funding rounds with top tier VCs as one of the leaders of the Compound AI movement, a term first coined by the Databricks/Mosaic gang at Berkeley AI and adapted as “Composite AI” by Gartner:

Replicating o1

We are the first podcast to discuss Fireworks’ f1, their proprietary replication of OpenAI’s o1. This has become a surprisingly hot area of competition in the past week as both Nous Forge and Deepseek r1 have launched competitive models.

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Timestamps

* 00:00:00 Introductions

* 00:02:08 Pre-history of Fireworks and PyTorch at Meta

* 00:09:49 Product Strategy: From Framework to Model Library

* 00:13:01 Compound AI Concept and Industry Dynamics

* 00:20:07 Fireworks' Distributed Inference Engine

* 00:22:58 OSS Model Support and Competitive Strategy

* 00:29:46 Declarative System Approach in AI

* 00:31:00 Can OSS replicate o1?

* 00:36:51 Fireworks f1

* 00:41:03 Collaboration with Cursor and Speculative Decoding

* 00:46:44 Fireworks quantization (and drama around it)

* 00:49:38 Pricing Strategy

* 00:51:51 Underrated Features of Fireworks Platform

* 00:55:17 Hiring

Transcript

Alessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner at CTO at Danceable Partners, and I'm joined by my co-host, Swyx founder, Osmalayar.

Swyx [00:00:11]: Hey, and today we're in a very special studio inside the Fireworks office with Lin Qiang, CEO of Fireworks. Welcome. Yeah.

Lin [00:00:20]: Oh, you should welcome us.

Swyx [00:00:21]: Yeah, welcome. Yeah, thanks for having us. It's unusual to be in the home of a startup, but it's also, I think our relationship is a bit unusual compared to all our normal guests. Definitely.

Lin [00:00:34]: Yeah. I'm super excited to talk about very interesting topics in that space with both of you.

Swyx [00:00:41]: You just celebrated your two-year anniversary yesterday.

Lin [00:00:43]: Yeah, it's quite a crazy journey. We circle around and share all the crazy stories across these two years, and it has been super fun. All the way from we experienced Silicon Valley bank run to we delete some data that shouldn't be deleted operationally. We went through a massive scale where we actually are busy getting capacity to, yeah, we learned to kind of work with it as a team with a lot of brilliant people across different places to join a company. It has really been a fun journey.

Alessio [00:01:24]: When you started, did you think the technical stuff will be harder or the bank run and then the people side? I think there's a lot of amazing researchers that want to do companies and it's like the hardest thing is going to be building the product and then you have all these different other things. So, were you surprised by what has been your experience the most?

Lin [00:01:42]: Yeah, to be honest with you, my focus has always been on the product side and then after the product goes to market. And I didn't realize the rest has been so complicated, operating a company and so on. But because I don't think about it, I just kind of manage it. So it's done. I think I just somehow don't think about it too much and solve whatever problem coming our way and it worked.

Swyx [00:02:08]: So let's, I guess, let's start at the pre-history, the initial history of Fireworks. You ran the PyTorch team at Meta for a number of years and we previously had Sumit Chintal on and I think we were just all very interested in the history of GenEI. Maybe not that many people know how deeply involved Faire and Meta were prior to the current GenEI revolution.

Lin [00:02:35]: My background is deep in distributed system, database management system. And I joined Meta from the data side and I saw this tremendous amount of data growth, which cost a lot of money and we're analyzing what's going on. And it's clear that AI is driving all this data generation. So it's a very interesting time because when I joined Meta, Meta is going through ramping down mobile-first, finishing the mobile-first transition and then starting AI-first. And there's a fundamental reason about that sequence because mobile-first gave a full range of user engagement that has never existed before. And all this user engagement generated a lot of data and this data power AI. So then the whole entire industry is also going through, falling through this same transition. When I see, oh, okay, this AI is powering all this data generation and look at where's our AI stack. There's no software, there's no hardware, there's no people, there's no team. I want to dive up there and help this movement. So when I started, it's very interesting industry landscape. There are a lot of AI frameworks. It's a kind of proliferation of AI frameworks happening in the industry. But all the AI frameworks focus on production and they use a very certain way of defining the graph of neural network and then use that to drive the model iteration and productionization. And PyTorch is completely different. So they could also assume that he was the user of his product. And he basically says, researchers face so much pain using existing AI frameworks, this is really hard to use and I'm going to do something different for myself. And that's the origin story of PyTorch. PyTorch actually started as the framework for researchers. They don't care about production at all. And as they grow in terms of adoption, so the interesting part of AI is research is the top of our normal production. There are so many researchers across academic, across industry, they innovate and they put their results out there in open source and that power the downstream productionization. So it's brilliant for MATA to establish PyTorch as a strategy to drive massive adoption in open source because MATA internally is a PyTorch shop. So it creates a flying wheel effect. So that's kind of a strategy behind PyTorch. But when I took on PyTorch, it's kind of at Caspo, MATA established PyTorch as the framework for both research and production. So no one has done that before. And we have to kind of rethink how to architect PyTorch so we can really sustain production workload, the stability, reliability, low latency, all this production concern was never a concern before. Now it's a concern. And we actually have to adjust its design and make it work for both sides. And that took us five years because MATA has so many AI use cases, all the way from ranking recommendation as powering the business top line or as ranking newsfeed, video ranking to site integrity detect bad content automatically using AI to all kinds of effects, translation, image classification, object detection, all this. And also across AI running on the server side, on mobile phones, on AI VR devices, the wide spectrum. So by the time we actually basically managed to support AI across ubiquitous everywhere across MATA. But interestingly, through open source engagement, we work with a lot of companies. It is clear to us like this industry is starting to take on AI first transition. And of course, MATA's hyperscale always go ahead of industry. And it feels like when we start this AI journey at MATA, there's no software, no hardware, no team. For many companies we engage with through PyTorch, we feel the pain. That's the genesis why we feel like, hey, if we create fireworks and support industry going through this transition, it will be a huge amount of impact. Of course, the problem that the industry is facing will not be the same as MATA. MATA is so big, right? So it's kind of skewed towards extreme scale and extreme optimization in the industry will be different. But we feel like we have the technical chop and we've seen a lot. We'll look to kind of drive that. So yeah, so that's how we started.

Swyx [00:06:58]: When you and I chatted about the origins of fireworks, it was originally envisioned more as a PyTorch platform, and then later became much more focused on generative AI. Is that fair to say? What was the customer discovery here?

Lin [00:07:13]: Right. So I would say our initial blueprint is we should build a PyTorch cloud because a PyTorch library and there's no SaaS platform to enable AI workloads.

Swyx [00:07:26]: Even in 2022, it's interesting.

Lin [00:07:28]: I would not say absolutely no, but cloud providers have some of those, but it's not first class citizen, right? At 2022, there's still like TensorFlow is massively in production. And this is all pre-gen AI, and PyTorch is kind of getting more and more adoption. But there's no PyTorch-first SaaS platform existing. At the same time, we are also a very pragmatic set of people. We really want to make sure from the get-go, we get really, really close to customers. We understand their use case, we understand their pain points, we understand the value we deliver to them. So we want to take a different approach instead of building a horizontal PyTorch cloud. We want to build a verticalized platform first. And then we talk with many customers. And interestingly, we started the company in September 2022, and in October, November, the OpenAI announced ChatGPT. And then boom, when we talked with many customers, they were like, can you help us work on the JNS aspect? So of course, there are some open source models. It's not as good at that time, but people are already putting a lot of attention there. Then we decided that if we're going to pick a vertical, we're going to pick JNI. The other reason is all JNI models are PyTorch models. So that's another reason. We believe that because of the nature of JNI, it's going to generate a lot of human consumable content. It will drive a lot of consumer, customer-developer-facing application and product innovation. Guaranteed. We're just at the beginning of this. Our prediction is for those kind of applications, the inference is much more important than training because inference scale is proportional to the up-limit award population. And training scale is proportional to the number of researchers. Of course, each training round could be very expensive. Although PyTorch supports both inference and training, we decided to laser focus on inference. So yeah, so that's how we got started. And we launched our public platform August last year. When we launched, it was a single product. It's a distributed inference engine with a simple API, open AI compatible API with many models. We started with LM and then we added a lot of models. Fast forward to now, we are a full platform with multiple product lines. So we love to kind of dive deep into what we offer. But that's a very fun journey in the past two years.

Alessio [00:09:49]: What was the transition from you start to focus on PyTorch and people want to understand the framework, get it live. And now say maybe most people that use you don't even really know much about PyTorch at all. You know, they're just trying to consume a model. From a product perspective, like what were some of the decisions early on? Like right in October, November, you were just like, hey, most people just care about the model, not about the framework. We're going to make it super easy or was it more a gradual transition to the model library

Swyx [00:10:16]: you have today?

Lin [00:10:17]: Yeah. So our product decision is all based on who is our ICP. And one thing I want to acknowledge here is the generic technology is disruptive. It's very different from AI before GNI. So it's a clear leap forward. Because before GNI, the companies that want to invest in AI, they have to train from scratch. There's no other way. There's no foundation model. It doesn't exist. So that means then to start a team, first hire a team who is capable of crunch data. There's a lot of data to crunch, right? Because training from scratch, you have to prepare a lot of data. And then they need to have GPUs to train, and then you start to manage GPUs. So then it becomes a very complex project. It takes a long time and not many companies can afford it, actually. And the GNI is a very different game right now, because it is a foundation model. So you don't have to train anymore. That makes AI much more accessible as a technology. As an app developer or product manager, even, not a developer, they can interact with GNI models directly. So our goal is to make AI accessible to all app developers and product engineers. That's our goal. So then getting them into the building model doesn't make any sense anymore with this new technology. And then building easy, accessible APIs is the most important. Early on, when we got started, we decided we're going to be open AI compatible. It's just kind of very easy for developers to adopt this new technology, and we will manage the underlying complexity of serving all these models.

Swyx [00:11:56]: Yeah, open AI has become the standard. Even as we're recording today, Gemini announced that they have open AI compatible APIs. Interesting. So we just need to drop it all in line, and then we have everyone popping in line.

Lin [00:12:09]: That's interesting, because we are working very closely with Meta as one of the partners. Meta, of course, is kind of very generous to donate many very, very strong open source models, expecting more to come. But also they have announced LamaStack, which is basically standardized, the upper level stack built on top of Lama models. So they don't just want to give out models and you figure out what the upper stack is. They instead want to build a community around the stack and build a new standard. I think there's an interesting dynamics in play in the industry right now, when it's more standardized across open AI, because they are kind of creating the top of the funnel, or standardized across Lama, because this is the most used open source model. So I think it's a lot of fun working at this time.

Swyx [00:13:01]: I've been a little bit more doubtful on LamaStack, I think you've been more positive. Basically it's just like the meta version of whatever Hugging Face offers, you know, or TensorRT, or BLM, or whatever the open source opportunity is. But to me, it's not clear that just because Meta open sources Lama, that the rest of LamaStack will be adopted. And it's not clear why I should adopt it. So I don't know if you agree.

Lin [00:13:27]: It's very early right now. That's why I kind of work very closely with them and give them feedback. The feedback to the meta team is very important. So then they can use that to continue to improve the model and also improve the higher level I think the success of LamaStack heavily depends on the community adoption. And there's no way around it. And I know the meta team would like to kind of work with a broader set of community. But it's very early.

Swyx [00:13:52]: One thing that after your Series B, so you raced for Benchmark, and then Sequoia. I remember being close to you for at least your Series B announcements, you started betting heavily on this term of Compound AI. It's not a term that we've covered very much in the podcast, but I think it's definitely getting a lot of adoption from Databricks and Berkeley people and all that. What's your take on Compound AI? Why is it resonating with people?

Lin [00:14:16]: Right. So let me give a little bit of context why we even consider that space.

Swyx [00:14:22]: Because like pre-Series B, there was no message, and now it's like on your landing page.

Lin [00:14:27]: So it's kind of very organic evolution from when we first launched our public platform, we are a single product. We are a distributed inference engine, where we do a lot of innovation, customized KUDA kernels, raw kernel kernels, running on different kinds of hardware, and build distributed disaggregated execution, inference execution, build all kinds of caching. So that is one. So that's kind of one product line, is the fast, most cost-efficient inference platform. Because we wrote PyTorch code, we know we basically have a special PyTorch build for that, together with a custom kernel we wrote. And then we worked with many more customers, we realized, oh, the distributed inference engine, our design is one size fits all. We want to have this inference endpoint, then everyone come in, and no matter what kind of form and shape or workload they have, it will just work for them. So that's great. But the reality is, we realized all customers have different kinds of use cases. The use cases come in all different forms and shapes. And the end result is the data distribution in their inference workload doesn't align with the data distribution in the training data for the model. It's a given, actually. If you think about it, because researchers have to guesstimate what is important, what's not important in preparing data for training. So because of that misalignment, then we leave a lot of quality, latency, cost improvement on the table. So then we're saying, OK, we want to heavily invest in a customization engine. And we actually announced it called FHIR Optimizer. So FHIR Optimizer basically helps users navigate a three-dimensional optimization space across quality, latency, and cost. So it's a three-dimensional curve. And even for one company, for different use cases, they want to land in different spots. So we automate that process for our customers. It's very simple. You have your inference workload. You inject into the optimizer along with the objective function. And then we spit out inference deployment config and the model setup. So it's your customized setup. So that is a completely different product. So that product thinking is one size fits all. And now on top of that, we provide a huge variety of state-of-the-art models, hundreds of them, varying from text to large state-of-the-art English models. That's where we started. And as we talk with many customers, we realize, oh, audio and text are very, very close. Many of our customers start to build assistants, all kinds of assistants using text. And they immediately want to add audio, audio in, audio out. So we support transcription, translation, speech synthesis, text, audio alignment, all different kinds of audio features. It's a big announcement. You should have heard by the time this is out. And the other areas of vision and text are very close with each other. Because a lot of information doesn't live in plain text. A lot of information lives in multimedia format, images, PDFs, screenshots, and many other different formats. So oftentimes to solve a problem, we need to put the vision model first to extract information and then use language model to process and then send out results. So vision is important. We also support vision model, various different kinds of vision models specialized in processing different kinds of source and extraction. And we're also going to have another announcement of a new API endpoint we'll support for people to upload various different kinds of multimedia content and then get the extract very accurate information out and feed that into LM. And of course, we support embedding because embedding is very important for semantic search, for RAG, and all this. And in addition to that, we also support text-to-image, image generation models, text-to-image, image-to-image, and we're adding text-to-video as well in our portfolio. So it's a very comprehensive set of model catalog that built on top of File Optimizer and Distributed Inference Engine. But then we talk with more customers, they solve business use case, and then we realize one model is not sufficient to solve their problem. And it's very clear because one is the model hallucinates. Many customers, when they onboard this JNI journey, they thought this is magical. JNI is going to solve all my problems magically. But then they realize, oh, this model hallucinates. It hallucinates because it's not deterministic, it's probabilistic. So it's designed to always give you an answer, but based on probabilities, so it hallucinates. And that's actually sometimes a feature for creative writing, for example. Sometimes it's a bug because, hey, you don't want to give misinformation. And different models also have different specialties. To solve a problem, you want to ask different special models to kind of decompose your task into multiple small tasks, narrow tasks, and then have an expert model solve that task really well. And of course, the model doesn't have all the information. It has limited knowledge because the training data is finite, not infinite. So the model oftentimes doesn't have real-time information. It doesn't know any proprietary information within the enterprise. It's clear that in order to really build a compiling application on top of JNI, we need a compound AI system. Compound AI system basically is going to have multiple models across modalities, along with APIs, whether it's public APIs, internal proprietary APIs, storage systems, database systems, knowledge to work together to deliver the best answer.

Swyx [00:20:07]: Are you going to offer a vector database?

Lin [00:20:09]: We actually heavily partner with several big vector database providers. Which is your favorite? They are all great in different ways. But it's public information, like MongoDB is our investor. And we have been working closely with them for a while.

Alessio [00:20:26]: When you say distributed inference engine, what do you mean exactly? Because when I hear your explanation, it's almost like you're centralizing a lot of the decisions through the Fireworks platform on the quality and whatnot. What do you mean distributed? It's like you have GPUs in a lot of different clusters, so you're sharding the inference across the same model.

Lin [00:20:45]: So first of all, we run across multiple GPUs. But the way we distribute across multiple GPUs is unique. We don't distribute the whole model monolithically across multiple GPUs. We chop them into pieces and scale them completely differently based on what's the bottleneck. We also are distributed across regions. We have been running in North America, EMEA, and Asia. We have regional affinity to applications because latency is extremely important. We are also doing global load balancing because a lot of applications there, they quickly scale to global population. And then at that scale, different content wakes up at a different time. And you want to kind of load balancing across. So all the way, and we also have, we manage various different kinds of hardware skew from different hardware vendors. And different hardware design is best for different types of workload, whether it's long context, short context, long generation. So all these different types of workload is best fitted for different kinds of hardware skew. And then we can even distribute across different hardware for a workload. So the distribution actually is all around in the full stack.

Swyx [00:22:02]: At some point, we'll show on the YouTube, the image that Ray, I think, has been working on with all the different modalities that you offer. To me, it's basically you offer the open source version of everything that OpenAI typically offers. I don't think there is. Actually, if you do text to video, you will be a superset of what OpenAI offers because they don't have Sora. Is that Mochi, by the way? Mochi. Mochi, right?

Lin [00:22:27]: Mochi. And there are a few others. I will say, the interesting thing is, I think we're betting on the open source community is going to proliferate. This is literally what we're seeing. And there's amazing video generation companies. There is amazing audio companies. Like cross-border, the innovation is off the chart, and we are building on top of that. I think that's the advantage we have compared with a closed source company.

Swyx [00:22:58]: I think I want to restate the value proposition of Fireworks for people who are comparing you versus a raw GPU provider like a RunPod or Lambda or anything like those, which is like you create the developer experience layer and you also make it easily scalable or serverless or as an endpoint. And then, I think for some models, you have custom kernels, but not all models.

Lin [00:23:25]: Almost for all models. For all large language models, all your models, and the VRMs. Almost for all models we serve.

Swyx [00:23:35]: And so that is called Fire Attention. I don't remember the speed numbers, but apparently much better than VLM, especially on a concurrency basis.

Lin [00:23:44]: So Fire Attention is specific mostly for language models, but for other modalities, we'll also have a customized kernel.

Swyx [00:23:51]: And I think the typical challenge for people is understanding that has value, and then there are other people who are also offering open-source models. Your mode is your ability to offer a good experience for all these customers. But if your existence is entirely reliant on people releasing nice open-source models, other people can also do the same thing.

Lin [00:24:14]: So I would say we build on top of open-source model foundation. So that's the kind of foundation we build on top of. But we look at the value prop from the lens of application developers and product engineers. So they want to create new UX. So what's happening in the industry right now is people are thinking about a completely new way of designing products. And I'm talking to so many founders, it's just mind-blowing. They help me understand existing way of doing PowerPoint, existing way of coding, existing way of managing customer service. It's actually putting a box in our head. For example, PowerPoint. So PowerPoint generation is we always need to think about how to fit into my storytelling into this format of slide one after another. And I'm going to juggle through design together with what story to tell. But the most important thing is what's our storytelling lines, right? And why don't we create a space that is not limited to any format? And those kind of new product UX design combined with automated content generation through Gen AI is the new thing that many founders are doing. What are the challenges they're facing? Let's go from there. One is, again, because a lot of products built on top of Gen AI, they are consumer-personal developer facing, and they require interactive experience. It's just a kind of product experience we all get used to. And our desire is to actually get faster and faster interaction. Otherwise, nobody wants to spend time, right? And then that requires low latency. And the other thing is the nature of consumer-personal developer facing is your audience is very big. You want to scale up to product market fit quickly. But if you lose money at a small scale, you're going to bankrupt quickly. So it's actually a big contrast. I actually have product market fit, but when I scale, I scale out of my business. So that's kind of a very funny way to think about it. So then having low latency and low cost is essential for those new applications and products to survive and really become a generation company. So that's the design point for our distributed inference engine and the file optimizer. File optimizer, you can think about that as a feedback loop. The more you feed your inference workload to our inference engine, the more we help you improve quality, lower latency further, lower your cost. It basically becomes better. And we automate that because we don't want you as an app developer or product engineer to think about how to figure out all these low-level details. It's impossible because you're not trained to do that at all. You should kind of keep your focus on the product innovation. And then the compound AI, we actually feel a lot of pain as the app developers, engineers, there are so many models. Every week, there's at least a new model coming out.

Swyx [00:27:09]: Tencent had a giant model this week. Yeah, yeah.

Lin [00:27:13]: I saw that. I saw that.

Swyx [00:27:15]: It's like $500 billion.

Lin [00:27:18]: So they're like, should I keep chasing this or should I forget about it? And which model should I pick to solve what kind of sub-problem? How do I even decompose my problem into those smaller problems and fit the model into it? I have no idea. And then there are two ways to think about this design. I think I talked about that in the past. One is imperative, as in you figure out how to do it. You give developer tools to dictate how to do it. Or you build a declarative system where a developer tells what they want to do, not how. So these are completely two different designs. So the analogy I want to draw is, in the data world, the database management system is a declarative system because people use database, use SQL. SQL is a way you say, what do you want to extract out of a database? What kind of result do you want? But you don't figure out which node is going to, how many nodes you're going to run on top of, how you redefine your disk, which index you use, which project. You don't need to worry about any of those. And database management system will figure out, generate a new best plan, and execute on that. So database is declarative. And it makes it super easy. You just learn SQL, which is learn a semantic meaning of SQL, and you can use it. Imperative side is there are a lot of ETL pipelines. And people design this DAG system with triggers, with actions, and you dictate exactly what to do. And if it fails, then how to recover. So that's an imperative system. We have seen a range of systems in the ecosystem go different ways. I think there's value of both. There's value of both. I don't think one is going to subsume the other. But we are leaning more into the philosophy of the declarative system. Because from the lens of app developer and product engineer, that would be easiest for them to integrate.

Swyx [00:29:07]: I understand that's also why PyTorch won as well, right? This is one of the reasons. Ease of use.

Lin [00:29:14]: Focus on ease of use, and then let the system take on the hard challenges and complexities. So we follow, we extend that thinking into current system design. So another announcement is we will also announce our next declarative system is going to appear as a model that has extremely high quality. And this model is inspired by Owen's announcement for OpenAI. You should see that by the time we announce this or soon.

Alessio [00:29:46]: Trained by you.

Lin [00:29:47]: Yes.

Alessio [00:29:48]: Is this the first model that you trained? It's not the first.

Lin [00:29:52]: We actually have trained a model called FireFunction. It's a function calling model. It's our first step into compound AI system. Because function calling model can dispatch a request into multiple APIs. We have pre-baked set of APIs the model learned. You can also add additional APIs through the configuration to let model dispatch accordingly. So we have a very high quality function calling model that's already released. We have actually three versions. The latest version is very high quality. But now we take a further step that you don't even need to use function calling model. You use our new model we're going to release. It will solve a lot of problems approaching very high OpenAI quality. So I'm very excited about that.

Swyx [00:30:41]: Do you have any benchmarks yet?

Lin [00:30:43]: We have a benchmark. We're going to release it hopefully next week. We just put our model to LMSYS and people are guessing. Is this the next Gemini model or a MADIS model? People are guessing. That's very interesting. We're watching the Reddit discussion right now.

Swyx [00:31:00]: I have to ask more questions about this. When OpenAI released o1, a lot of people asked about whether or not it's a single model or whether it's a chain of models. Noam and basically everyone on the Strawberry team was very insistent that what they did for reinforcement learning, chain of thought, cannot be replicated by a whole bunch of open source model calls. Do you think that that is wrong? Have you done the same amount of work on RL as they have or was it a different direction?

Lin [00:31:29]: I think they take a very specific approach where the caliber of team is very high. So I do think they are the domain expert in doing the things they are doing. I don't think there's only one way to achieve the same goal. We're on the same direction in the sense that the quality scaling law is shifting from training to inference. For that, I fully agree with them. But we're taking a completely different approach to the problem. All of that is because, of course, we didn't train the model from scratch. All of that is because we built on the show of giants. The current model available we have access to is getting better and better. The future trend is the gap between the open source model and the co-source model. It's just going to shrink to the point there's not much difference. And then we're on the same level field. That's why I think our early investment in inference and all the work we do around balancing across quality, latency, and cost pay off because we have accumulated a lot of experience and that empowers us to release this new model that is approaching open-ended quality.

Alessio [00:32:39]: I guess the question is, what do you think the gap to catch up will be? Because I think everybody agrees with open source models eventually will catch up. And I think with 4, then with Lama 3.2, 3.1, 4.5b, we close the gap. And then 0.1 just reopened the gap so much and it's unclear. Obviously, you're saying your model will have...

Swyx [00:32:57]: We're closing that gap.

Alessio [00:32:58]: But you think in the future, it's going to be months?

Lin [00:33:02]: So here's the thing that's happened. There's public benchmark. It is what it is. But in reality, open source models in certain dimensions are already on par or beat closed source models. So for example, in the coding space, open source models are really, really good. And in function calling, file function is also really, really good. So it's all a matter of whether you build one model to solve all the problems and you want to be the best of solving all the problems, or in the open source domain, it's going to specialize. All these different model builders specialize in certain narrow area. And it's logical that they can be really, really good in that very narrow area. And that's our prediction is with specialization, there will be a lot of expert models really, really good and even better than one-size-fits-all closed source models.

Swyx [00:33:55]: I think this is the core debate that I am still not 100% either way on in terms of compound AI versus normal AI. Because you're basically fighting the bitter lesson.

Lin [00:34:09]: Look at the human society, right? We specialize. And you feel really good about someone specializing doing something really well, right? And that's how our way evolved from ancient times. We're all journalists. We do everything. Now we heavily specialize in different domains. So my prediction is in the AI model space, it will happen also. Except for the bitter lesson.

Swyx [00:34:30]: You get short-term gains by having specialists, domain specialists, and then someone just needs to train like a 10x bigger model on 10x more inference, 10x more data, 10x more model perhaps, whatever the current scaling law is. And then it supersedes all the individual models because of some generalized intelligence slash world knowledge. I think that is the core insight of the GPTs, the GPT-123 networks. Right.

Lin [00:34:56]: But the training scaling law is because you have an increasing amount of data to train from. And you can do a lot of compute. So I think on the data side, we're approaching the limit. And the only data to increase that is synthetic generated data. And then there's like what is the secret sauce there, right? Because if you have a very good large model, you can generate very good synthetic data and then continue to improve quality. So that's why I think in OpenAI, they are shifting from the training scaling law into

Swyx [00:35:25]: inference scaling law.

Lin [00:35:25]: And it's the test time and all this. So I definitely believe that's the future direction. And that's where we are really good at, doing inference.

Swyx [00:35:34]: A couple of questions on that. Are you planning to share your reasoning choices?

Lin [00:35:39]: That's a very good question. We are still debating.

Swyx [00:35:43]: Yeah.

Lin [00:35:45]: We're still debating.

Swyx [00:35:46]: I would say, for example, it's interesting that, for example, SweetBench. If you want to be considered for ranking, you have to submit your reasoning choices. And that has actually disqualified some of our past guests. Cosign was doing well on SweetBench, but they didn't want to leak those results. So that's why you don't see O1 preview on SweetBench, because they don't submit their reasoning choices. And obviously, it's IP. But also, if you're going to be more open, then that's one way to be more open. So your model is not going to be open source, right? It's going to be an endpoint that you provide. Okay, cool. And then pricing, also the same as OpenAI, just kind of based on...

Lin [00:36:25]: Yeah, this is... I don't have, actually, information. Everything is going so fast, we haven't even thought about that yet. Yeah, I should be more prepared.

Swyx [00:36:33]: I mean, this is live. You know, it's nice to just talk about it as it goes live. Any other things that you want feedback on or you're thinking through? It's kind of nice to just talk about something when it's not decided yet. About this new model. It's going to be exciting. It's going to generate a lot of buzz. Right.

Lin [00:36:51]: I'm very excited to see how people are going to use this model. So there's already a Reddit discussion about it. And people are asking very deep, mathematical questions. And since the model got it right, surprising. And internally, we're also asking the model to generate what is AGI. And it generates a very complicated DAG thinking process. So we're having a lot of fun testing this internally. But I'm more curious, how will people use it? What kind of application they're going to try and test on it? And that's where we really like to hear feedback from the community. And also feedback to us. What works out well? What doesn't work out well? What works out well, but surprising them? And what kind of thing they think we should improve on? And those kind of feedback will be tremendously helpful.

Swyx [00:37:44]: Yeah. So I've been a production user of Preview and Mini since launch. I would say they're very, very obvious jobs in quality. So much so that they made clods on it. And they made the previous state-of-the-art look bad. It's really that stark, that difference. The number one thing, just feedback or feature requests, is people want control on the budget. Because right now, in 0.1, it kind of decides its own thinking budget. But sometimes you know how hard the problem is. And you want to actually tell the model, spend two minutes on this. Or spend some dollar amount. Maybe it's time you miss dollars. I don't know what the budget is. That makes a lot of sense.

Lin [00:38:27]: So we actually thought about that requirement. And it should be, at some point, we need to support that. Not initially. But that makes a lot of sense.

Swyx [00:38:38]: Okay. So that was a fascinating overview of just the things that you're working on. First of all, I realized that... I don't know if I've ever given you this feedback. But I think you guys are one of the reasons I agreed to advise you. Because I think when you first met me, I was kind of dubious. I was like... Who are you? There's Replicate. There's Together. There's Laptop. There's a whole bunch of other players. You're in very, very competitive fields. Like, why will you win? And the reason I actually changed my mind was I saw you guys shipping. I think your surface area is very big. The team is not that big. No. We're only 40 people. Yeah. And now here you are trying to compete with OpenAI and everyone else. What is the secret?

Lin [00:39:21]: I think the team. The team is the secret.

Swyx [00:39:23]: Oh boy. So there's no thing I can just copy. You just... No.

Lin [00:39:30]: I think we all come from a very aligned culture. Because most of our team came from meta.

Swyx [00:39:38]: Yeah.

Lin [00:39:38]: And many startups. So we really believe in results. One is result. And second is customer. We're very customer obsessed. And we don't want to drive adoption for the sake of adoption. We really want to make sure we understand we are delivering a lot of business values to the customer. And we really value their feedback. So we would wake up midnight and deploy some model for them. Shuffle some capacity for them. And yeah, over the weekend, no brainer.

Swyx [00:40:15]: So yeah.

Lin [00:40:15]: So that's just how we work as a team. And the caliber of the team is really, really high as well. So as plug-in, we're hiring. We're expanding very, very fast. So if we are passionate about working on the most cutting-edge technology in the general space, come talk with us. Yeah.

Swyx [00:40:38]: Let's talk a little bit about that customer journey. I think one of your more famous customers is Cursor. We were the first podcast to have Cursor on. And then obviously since then, they have blown up. Cause and effect are not related. But you guys especially worked on a fast supply model where you were one of the first people to work on speculative decoding in a production setting. Maybe just talk about what was the behind the scenes of working with Cursor?

Lin [00:41:03]: I will say Cursor is a very, very unique team. I think the unique part is the team has very high technical caliber. There's no question about it. But they have decided, although many companies building coding co-pilot, they will say, I'm going to build a whole entire stack because I can. And they are unique in the sense they seek partnership. Not because they cannot. They're fully capable, but they know where to focus. That to me is amazing. And of course, they want to find a bypass partner. So we spent some time working together. They are pushing us very aggressively because for them to deliver high caliber product experience, they need the latency. They need the interactive, but also high quality at the same time. So actually, we expanded our product feature quite a lot as we support Cursor. And they are growing so fast. And we massively scaled quickly across multiple regions. And we developed a pretty high intense inference stack, almost like similar to what we do for Meta. I think that's a very, very interesting engagement. And through that, there's a lot of trust being built. They realize, hey, this is a team they can really partner with. And they can go big with. That comes back to, hey, we're really customer obsessed. And all the engineers working with them, there's just enormous amount of time syncing together with them and discussing. And we're not big on meetings, but we are like stack channel always on. Yeah, so you almost feel like working as one team. So I think that's really highlighted.

Swyx [00:42:38]: Yeah. For those who don't know, so basically Cursor is a VS Code fork. But most of the time, people will be using closed models. Like I actually use a lot of SONET. So you're not involved there, right? It's not like you host SONET or you have any partnership with it. You're involved where Cursor is small, or like their house brand models are concerned, right?

Lin [00:42:58]: I don't know what I can say, but the things they haven't said.

Swyx [00:43:04]: Very obviously, the drop down is 4.0, but in Cursor, right? So I assume that the Cursor side is the Fireworks side. And then the other side, they're calling out the other. Just kind of curious. And then, do you see any more opportunity on the... You know, I think you made a big splash with 1,000 tokens per second. That was because of speculative decoding. Is there more to push there?

Lin [00:43:25]: We push a lot. Actually, when I mentioned Fire Optimizer, right? So as in, we have a unique automation stack that is one size fits one. We actually deployed to Cursor earlier on. Basically optimized for their specific workload. And that's a lot of juice to extract out of there. And we see success in that product. It actually can be widely adopted. So that's why we started a separate product line called Fire Optimizer. So speculative decoding is just one approach. And speculative decoding here is not static. We actually wrote a blog post about it. There's so many different ways to do speculative decoding. You can pair a small model with a large model in the same model family. Or you can have equal pads and so on. There are different trade-offs which approach you take. It really depends on your workload. And then with your workload, we can align the Eagle heads or Medusa heads or a small big model pair much better to extract the best latency reduction. So all of that is part of the Fire Optimizer offering.

Alessio [00:44:23]: I know you mentioned some of the other inference providers. I think the other question that people always have is around benchmarks. So you get different performance on different platforms. How should people think about... People are like, hey, Lama 3.2 is X on MMLU. But maybe using speculative decoding, you go down a different path. Maybe some providers run a quantized model. How should people think about how much they should care about how you're actually running the model? What's the delta between all the magic that you do and what a raw model...

Lin [00:44:57]: Okay, so there are two big development cycles. One is experimentation, where they need fast iteration. They don't want to think about quality, and they just want to experiment with product experience and so on. So that's one. And then it looks good, and they want to post-product market with scaling. And the quality is really important. And latency and all the other things are becoming important. During the experimentation phase, it's just pick a good model. Don't worry about anything else. Make sure you even generate the right solution to your product. And that's the focus. And then post-product market fit, then that's kind of the three-dimensional optimization curve start to kick in across quality, latency, cost, where you should land. And to me, it's purely a product decision. To many products, if you choose a lower quality, but better speed and lower cost, but it doesn't make a difference to the product experience, then you should do it. So that's why I think inference is part of the validation. The validation doesn't stop at offline eval. The validation will go through A-B testing, through inference. And that's where we offer various different configurations for you to test which is the best setting. So this is the traditional product evaluation. So product evaluation should also include your new model versions and different model setup into the consideration.

Swyx [00:46:22]: I want to specifically talk about what happens a few months ago with some of your major competitors. I mean, all of this is public. What is your take on what happens? And maybe you want to set the record straight on how Fireworks does quantization because I think a lot of people may have outdated perceptions or they didn't read the clarification post on your approach to quantization.

Lin [00:46:44]: First of all, it's always a surprise to us that without any notice, we got called out.

Swyx [00:46:51]: Specifically by name, which is normally not what...

Lin [00:46:54]: Yeah, in a public post. And have certain interpretation of our quality. So I was really surprised. And it's not a good way to compete, right? We want to compete fairly. And oftentimes when one vendor gives out results, the interpretation of another vendor is always extremely biased. So we actually refrain ourselves to do any of those. And we happily partner with third parties to do the most fair evaluation. So we're very surprised. And we don't think that's a good way to figure out the competition landscape. So then we react. I think when it comes to quantization, the interpretation, we wrote actually a very thorough blog post. Because again, no one says it's all. We have various different quantization schemes. We can quantize very different parts of the model from ways to activation to cross-TPU communication. They can use different quantization schemes or consistent across the board. And again, it's a trade-off. It's a trade-off across this three-dimensional quality, latency, and cost. And for our customer, we actually let them find the best optimized point. And we have a very thorough evaluation process to pick that point. But for self-serve, there's only one point to pick. There's no customization available. So of course, it depends on what we talk with many customers. We have to pick one point. And I think the end result, like AA published, later on AA published a quality measure. And we actually looked really good. So that's why what I mean is, I will leave the evaluation of quality or performance to third party and work with them to find the most fair benchmark. And I think that's a good approach, a methodology. But I'm not a part of an approach of calling out specific names

Swyx [00:48:55]: and critique other competitors in a very biased way. Databases happens as well. I think you're the more politically correct one. And then Dima is the more... Something like this. It's you on Twitter.

Lin [00:49:11]: It's like the Russian... We partner. We play different roles.

Swyx [00:49:20]: Another one that I wanted to... I'm just the last one on the competition side. There's a perception of price wars in hosting open source models. And we talked about the competitiveness in the market. Do you aim to make margin on open source models? Oh, absolutely, yes.

Lin [00:49:38]: So, but I think it really... When we think about pricing, it's really need to coordinate with the value we're delivering. If the value is limited, or there are a lot of people delivering the same value, there's no differentiation. There's only one way to go. It's going down. So through competition. If I take a big step back, there is pricing from... We're more compared with close model providers, APIs, right? The close model provider, their cost structure is even more interesting because we don't bear any training costs. And we focus on inference optimization, and that's kind of where we continue to add a lot of product value. So that's how we think about product. But for the close source API provider, model provider, they bear a lot of training costs. And they need to amortize the training costs into the inference. So that created very interesting dynamics of, yeah, if we match pricing there, and I think how they are going to make money is very, very interesting.

Swyx [00:50:37]: So for listeners, opening eyes 2024, $4 billion in revenue, $3 billion in compute training, $2 billion in compute inference, $1 billion in research compute amortization, and $700 million in salaries. So that is like...

Swyx [00:50:59]: I mean, a lot of R&D.

Lin [00:51:01]: Yeah, so I think matter is basically like, make it zero. So that's a very, very interesting dynamics we're operating within. But coming back to inference, so we are, again, as I mentioned, our product is, we are a platform. We're not just a single model as a service provider as many other inference providers, like they're providing a single model. We have our optimizer to highly customize towards your inference workload. We have a compound AI system where significantly simplify your interaction to high quality and low latency, low cost. So those are all very different from other providers.

Alessio [00:51:38]: What do people not know about the work that you do? I guess like people are like, okay, Fireworks, you run model very quickly. You have the function model. Is there any kind of like underrated part of Fireworks that more people should try?

Lin [00:51:51]: Yeah, actually, one user post on x.com, he mentioned, oh, actually, Fireworks can allow me to upload the LoRa adapter to the service model at the same cost and use it at same cost. Nobody has provided that. That's because we have a very special, like we rolled out multi-LoRa last year, actually. And we actually have this function for a long time. And many people has been using it, but it's not well known that, oh, if you find your model, you don't need to use on demand. If you find your model is LoRa, you can upload your LoRa adapter and we deploy it as if it's a new model. And then you use, you get your endpoint and you can use that directly, but at the same cost as the base model. So I'm happy that user is marketing it for us. He discovered that feature, but we have that for last year. So I think to feedback to me is, we have a lot of very, very good features, as Sean just mentioned. I'm the advisor to the company,

Swyx [00:52:57]: and I didn't know that you had speculative decoding released.

Lin [00:53:02]: We have prompt catching way back last year also. We have many, yeah. So I think that is one of the underrated feature. And if they're developers, you are using our self-serve platform, please try it out.

Swyx [00:53:16]: The LoRa thing is interesting because I think you also, the reason people add additional costs to it, it's not because they feel like charging people. Normally in normal LoRa serving setups, there is a cost to dedicating, loading those weights and dedicating a machine to that inference. How come you can't avoid it?

Lin [00:53:36]: Yeah, so this is kind of our technique called multi-LoRa. So we basically have many LoRa adapters share the same base model. And basically we significantly reduce the memory footprint of serving. And the one base model can sustain a hundred to a thousand LoRa adapters. And then basically all these different LoRa adapters can share the same, like direct the same traffic to the same base model where base model is dominating the cost. So that's how we advertise that way. And that's how we can manage the tokens per dollar, million token pricing, the same as base model.

Swyx [00:54:13]: Awesome. Is there anything that you think you want to request from the community or you're looking for model-wise or tooling-wise that you think like someone should be working on in this?

Lin [00:54:23]: Yeah, so we really want to get a lot of feedback from the application developers who are starting to build on JNN or on the already adopted or starting about thinking about new use cases and so on to try out Fireworks first. And let us know what works out really well for you and what is your wishlist and what sucks, right? So what is not working out for you and we would like to continue to improve. And for our new product launches, typically we want to launch to a small group of people. Usually we launch on our Discord first to have a set of people use that first. So please join our Discord channel. We have a lot of communication going on there. Again, you can also give us feedback. We'll have a starting office hour for you to directly talk with our DevRel and engineers to exchange more long notes.

Alessio [00:55:17]: And you're hiring across the board?

Lin [00:55:18]: We're hiring across the board. We're hiring front-end engineers, infrastructure cloud, infrastructure engineers, back-end system optimization engineers, applied researchers, like researchers who have done post-training, who have done a lot of fine-tuning and so on.

Swyx [00:55:34]: That's it. Thank you. Thanks for having us.

Alessio will be at AWS re:Invent next week and hosting a casual coffee meetup on Wednesday, RSVP here! And subscribe to our calendar for our Singapore, NeurIPS, and all upcoming meetups!

We are still taking questions for our next big recap episode! Submit questions and messages on Speakpipe here for a chance to appear on the show!

If you've been following the AI agents space, you have heard of Lindy AI; while founder Flo Crivello is hesitant to call it "blowing up," when folks like Andrew Wilkinson start obsessing over your product, you're definitely onto something.

In our latest episode, Flo walked us through Lindy's evolution from late 2022 to now, revealing some design choices about agent platform design that go against conventional wisdom in the space.

The Great Reset: From Text Fields to Rails

Remember late 2022? Everyone was "LLM-pilled," believing that if you just gave a language model enough context and tools, it could do anything. Lindy 1.0 followed this pattern:

* Big prompt field ✅

* Bunch of tools ✅

* Prayer to the LLM gods ✅

Fast forward to today, and Lindy 2.0 looks radically different. As Flo put it (~17:00 in the episode): "The more you can put your agent on rails, one, the more reliable it's going to be, obviously, but two, it's also going to be easier to use for the user."

Instead of a giant, intimidating text field, users now build workflows visually:

* Trigger (e.g., "Zendesk ticket received")

* Required actions (e.g., "Check knowledge base")

* Response generation

This isn't just a UI change - it's a fundamental rethinking of how to make AI agents reliable. As Swyx noted during our discussion: "Put Shoggoth in a box and make it a very small, minimal viable box. Everything else should be traditional if-this-then-that software."

The Surprising Truth About Model Limitations

Here's something that might shock folks building in the space: with Claude 3.5 Sonnet, the model is no longer the bottleneck. Flo's exact words (~31:00): "It is actually shocking the extent to which the model is no longer the limit. It was the limit a year ago. It was too expensive. The context window was too small."

Some context: Lindy started when context windows were 4K tokens. Today, their system prompt alone is larger than that. But what's really interesting is what this means for platform builders:

* Raw capabilities aren't the constraint anymore

* Integration quality matters more than model performance

* User experience and workflow design are the new bottlenecks

The Search Engine Parallel: Why Horizontal Platforms Might Win

One of the spiciest takes from our conversation was Flo's thesis on horizontal vs. vertical agent platforms. He draws a fascinating parallel to search engines (~56:00):

"I find it surprising the extent to which a horizontal search engine has won... You go through Google to search Reddit. You go through Google to search Wikipedia... search in each vertical has more in common with search than it does with each vertical."

His argument: agent platforms might follow the same pattern because:

* Agents across verticals share more commonalities than differences

* There's value in having agents that can work together under one roof

* The R&D cost of getting agents right is better amortized across use cases

This might explain why we're seeing early vertical AI companies starting to expand horizontally. The core agent capabilities - reliability, context management, tool integration - are universal needs.

What This Means for Builders

If you're building in the AI agents space, here are the key takeaways:

* Constrain First: Rather than maximizing capabilities, focus on reliable execution within narrow bounds

* Integration Quality Matters: With model capabilities plateauing, your competitive advantage lies in how well you integrate with existing tools

* Memory Management is Key: Flo revealed they actively prune agent memories - even with larger context windows, not all memories are useful

* Design for Discovery: Lindy's visual workflow builder shows how important interface design is for adoption

The Meta Layer

There's a broader lesson here about AI product development. Just as Lindy evolved from "give the LLM everything" to "constrain intelligently," we might see similar evolution across the AI tooling space. The winners might not be those with the most powerful models, but those who best understand how to package AI capabilities in ways that solve real problems reliably.

Full Video Podcast

Flo’s talk at AI Engineer Summit

Chapters

* 00:00:00 Introductions

* 00:04:05 AI engineering and deterministic software

* 00:08:36 Lindys demo

* 00:13:21 Memory management in AI agents

* 00:18:48 Hierarchy and collaboration between Lindys

* 00:21:19 Vertical vs. horizontal AI tools

* 00:24:03 Community and user engagement strategies

* 00:26:16 Rickrolling incident with Lindy

* 00:28:12 Evals and quality control in AI systems

* 00:31:52 Model capabilities and their impact on Lindy

* 00:39:27 Competition and market positioning

* 00:42:40 Relationship between Factorio and business strategy

* 00:44:05 Remote work vs. in-person collaboration

* 00:49:03 Europe vs US Tech

* 00:58:59 Testing the Overton window and free speech

* 01:04:20 Balancing AI safety concerns with business innovation

Show Notes

* Lindy.ai

* Rick Rolling

* Flo on X

* TeamFlow

* Andrew Wilkinson

* Dust

* Poolside.ai

* SB1047

* Gathertown

* Sid Sijbrandij

* Matt Mullenweg

* Factorio

* Seeing Like a State

Transcript

Alessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.

Swyx [00:00:12]: Hey, and today we're joined in the studio by Florent Crivello. Welcome.

Flo [00:00:15]: Hey, yeah, thanks for having me.

Swyx [00:00:17]: Also known as Altimore. I always wanted to ask, what is Altimore?

Flo [00:00:21]: It was the name of my character when I was playing Dungeons & Dragons. Always. I was like 11 years old.

Swyx [00:00:26]: What was your classes?

Flo [00:00:27]: I was an elf. I was a magician elf.

Swyx [00:00:30]: Well, you're still spinning magic. Right now, you're a solo founder and CEO of Lindy.ai. What is Lindy?

Flo [00:00:36]: Yeah, we are a no-code platform letting you build your own AI agents easily. So you can think of we are to LangChain as Airtable is to MySQL. Like you can just pin up AI agents super easily by clicking around and no code required. You don't have to be an engineer and you can automate business workflows that you simply could not automate before in a few minutes.

Swyx [00:00:55]: You've been in our orbit a few times. I think you spoke at our Latent Space anniversary. You spoke at my summit, the first summit, which was a really good keynote. And most recently, like we actually already scheduled this podcast before this happened. But Andrew Wilkinson was like, I'm obsessed by Lindy. He's just created a whole bunch of agents. So basically, why are you blowing up?

Flo [00:01:16]: Well, thank you. I think we are having a little bit of a moment. I think it's a bit premature to say we're blowing up. But why are things going well? We revamped the product majorly. We called it Lindy 2.0. I would say we started working on that six months ago. We've actually not really announced it yet. It's just, I guess, I guess that's what we're doing now. And so we've basically been cooking for the last six months, like really rebuilding the product from scratch. I think I'll list you, actually, the last time you tried the product, it was still Lindy 1.0. Oh, yeah. If you log in now, the platform looks very different. There's like a ton more features. And I think one realization that we made, and I think a lot of folks in the agent space made the same realization, is that there is such a thing as too much of a good thing. I think many people, when they started working on agents, they were very LLM peeled and chat GPT peeled, right? They got ahead of themselves in a way, and us included, and they thought that agents were actually, and LLMs were actually more advanced than they actually were. And so the first version of Lindy was like just a giant prompt and a bunch of tools. And then the realization we had was like, hey, actually, the more you can put your agent on Rails, one, the more reliable it's going to be, obviously, but two, it's also going to be easier to use for the user, because you can really, as a user, you get, instead of just getting this big, giant, intimidating text field, and you type words in there, and you have no idea if you're typing the right word or not, here you can really click and select step by step, and tell your agent what to do, and really give as narrow or as wide a guardrail as you want for your agent. We started working on that. We called it Lindy on Rails about six months ago, and we started putting it into the hands of users over the last, I would say, two months or so, and I think things really started going pretty well at that point. The agent is way more reliable, way easier to set up, and we're already seeing a ton of new use cases pop up.

Swyx [00:03:00]: Yeah, just a quick follow-up on that. You launched the first Lindy in November last year, and you were already talking about having a DSL, right? I remember having this discussion with you, and you were like, it's just much more reliable. Is this still the DSL under the hood? Is this a UI-level change, or is it a bigger rewrite?

Flo [00:03:17]: No, it is a much bigger rewrite. I'll give you a concrete example. Suppose you want to have an agent that observes your Zendesk tickets, and it's like, hey, every time you receive a Zendesk ticket, I want you to check my knowledge base, so it's like a RAG module and whatnot, and then answer the ticket. The way it used to work with Lindy before was, you would type the prompt asking it to do that. You check my knowledge base, and so on and so forth. The problem with doing that is that it can always go wrong. You're praying the LLM gods that they will actually invoke your knowledge base, but I don't want to ask it. I want it to always, 100% of the time, consult the knowledge base after it receives a Zendesk ticket. And so with Lindy, you can actually have the trigger, which is Zendesk ticket received, have the knowledge base consult, which is always there, and then have the agent. So you can really set up your agent any way you want like that.

Swyx [00:04:05]: This is something I think about for AI engineering as well, which is the big labs want you to hand over everything in the prompts, and only code of English, and then the smaller brains, the GPU pours, always want to write more code to make things more deterministic and reliable and controllable. One way I put it is put Shoggoth in a box and make it a very small, the minimal viable box. Everything else should be traditional, if this, then that software.

Flo [00:04:29]: I love that characterization, put the Shoggoth in the box. Yeah, we talk about using as much AI as necessary and as little as possible.

Alessio [00:04:37]: And what was the choosing between kind of like this drag and drop, low code, whatever, super code-driven, maybe like the Lang chains, auto-GPT of the world, and maybe the flip side of it, which you don't really do, it's like just text to agent, it's like build the workflow for me. Like what have you learned actually putting this in front of users and figuring out how much do they actually want to add it versus like how much, you know, kind of like Ruby on Rails instead of Lindy on Rails, it's kind of like, you know, defaults over configuration.

Flo [00:05:06]: I actually used to dislike when people said, oh, text is not a great interface. I was like, ah, this is such a mid-take, I think text is awesome. And I've actually come around, I actually sort of agree now that text is really not great. I think for people like you and me, because we sort of have a mental model, okay, when I type a prompt into this text box, this is what it's going to do, it's going to map it to this kind of data structure under the hood and so forth. I guess it's a little bit blackmailing towards humans. You jump on these calls with humans and you're like, here's a text box, this is going to set up an agent for you, do it. And then they type words like, I want you to help me put order in my inbox. Oh, actually, this is a good one. This is actually a good one. What's a bad one? I would say 60 or 70% of the prompts that people type don't mean anything. Me as a human, as AGI, I don't understand what they mean. I don't know what they mean. It is actually, I think whenever you can have a GUI, it is better than to have just a pure text interface.

Alessio [00:05:58]: And then how do you decide how much to expose? So even with the tools, you have Slack, you have Google Calendar, you have Gmail. Should people by default just turn over access to everything and then you help them figure out what to use? I think that's the question. When I tried to set up Slack, it was like, hey, give me access to all channels and everything, which for the average person probably makes sense because you don't want to re-prompt them every time you add new channels. But at the same time, for maybe the more sophisticated enterprise use cases, people are like, hey, I want to really limit what you have access to. How do you kind of thread that balance?

Flo [00:06:35]: The general philosophy is we ask for the least amount of permissions needed at any given moment. I don't think Slack, I could be mistaken, but I don't think Slack lets you request permissions for just one channel. But for example, for Google, obviously there are hundreds of scopes that you could require for Google. There's a lot of scopes. And sometimes it's actually painful to set up your Lindy because you're going to have to ask Google and add scopes five or six times. We've had sessions like this. But that's what we do because, for example, the Lindy email drafter, she's going to ask you for your authorization once for, I need to be able to read your email so I can draft a reply, and then another time for I need to be able to write a draft for them. We just try to do it very incrementally like that.

Alessio [00:07:15]: Do you think OAuth is just overall going to change? I think maybe before it was like, hey, we need to set up OAuth that humans only want to kind of do once. So we try to jam-pack things all at once versus what if you could on-demand get different permissions every time from different parts? Do you ever think about designing things knowing that maybe AI will use it instead of humans will use it? Yeah, for sure.

Flo [00:07:37]: One pattern we've started to see is people provisioning accounts for their AI agents. And so, in particular, Google Workspace accounts. So, for example, Lindy can be used as a scheduling assistant. So you can just CC her to your emails when you're trying to find time with someone. And just like a human assistant, she's going to go back and forth and offer other abilities and so forth. Very often, people don't want the other party to know that it's an AI. So it's actually funny. They introduce delays. They ask the agent to wait before replying, so it's not too obvious that it's an AI. And they provision an account on Google Suite, which costs them like $10 a month or something like that. So we're seeing that pattern more and more. I think that does the job for now. I'm not optimistic on us actually patching OAuth. Because I agree with you, ultimately, we would want to patch OAuth because the new account thing is kind of a clutch. It's really a hack. You would want to patch OAuth to have more granular access control and really be able to put your sugar in the box. I'm not optimistic on us doing that before AGI, I think. That's a very close timeline.

Swyx [00:08:36]: I'm mindful of talking about a thing without showing it. And we already have the setup to show it. Why don't we jump into a screen share? For listeners, you can jump on the YouTube and like and subscribe. But also, let's have a look at how you show off Lindy. Yeah, absolutely.

Flo [00:08:51]: I'll give an example of a very simple Lindy and then I'll graduate to a much more complicated one. A super simple Lindy that I have is, I unfortunately bought some investment properties in the south of France. It was a really, really bad idea. And I put them on a Holydew, which is like the French Airbnb, if you will. And so I received these emails from time to time telling me like, oh, hey, you made 200 bucks. Someone booked your place. When I receive these emails, I want to log this reservation in a spreadsheet. Doing this without an AI agent or without AI in general is a pain in the butt because you must write an HTML parser for this email. And so it's just hard. You may not be able to do it and it's going to break the moment the email changes. By contrast, the way it works with Lindy, it's really simple. It's two steps. It's like, okay, I receive an email. If it is a reservation confirmation, I have this filter here. Then I append a row to this spreadsheet. And so this is where you can see the AI part where the way this action is configured here, you see these purple fields on the right. Each of these fields is a prompt. And so I can say, okay, you extract from the email the day the reservation begins on. You extract the amount of the reservation. You extract the number of travelers of the reservation. And now you can see when I look at the task history of this Lindy, it's really simple. It's like, okay, you do this and boom, appending this row to this spreadsheet. And this is the information extracted. So effectively, this node here, this append row node is a mini agent. It can see everything that just happened. It has context over the task and it's appending the row. And then it's going to send a reply to the thread. That's a very simple example of an agent.

Swyx [00:10:34]: A quick follow-up question on this one while we're still on this page. Is that one call? Is that a structured output call? Yeah. Okay, nice. Yeah.

Flo [00:10:41]: And you can see here for every node, you can configure which model you want to power the node. Here I use cloud. For this, I use GPT-4 Turbo. Much more complex example, my meeting recorder. It looks very complex because I've added to it over time, but at a high level, it's really simple. It's like when a meeting begins, you record the meeting. And after the meeting, you send me a summary and you send me coaching notes. So I receive, like my Lindy is constantly coaching me. And so you can see here in the prompt of the coaching notes, I've told it, hey, you know, was I unnecessarily confrontational at any point? I'm French, so I have to watch out for that. Or not confrontational enough. Should I have double-clicked on any issue, right? So I can really give it exactly the kind of coaching that I'm expecting. And then the interesting thing here is, like, you can see the agent here, after it sent me these coaching notes, moves on. And it does a bunch of other stuff. So it goes on Slack. It disseminates the notes on Slack. It does a bunch of other stuff. But it's actually able to backtrack and resume the automation at the coaching notes email if I responded to that email. So I'll give a super concrete example. This is an actual coaching feedback that I received from Lindy. She was like, hey, this was a sales call I had with a customer. And she was like, I found your explanation of Lindy too technical. And I was able to follow up and just ask a follow-up question in the thread here. And I was like, why did you find too technical about my explanation? And Lindy restored the context. And so she basically picked up the automation back up here in the tree. And she has all of the context of everything that happened, including the meeting in which I was. So she was like, oh, you used the words deterministic and context window and agent state. And that concept exists at every level for every channel and every action that Lindy takes. So another example here is, I mentioned she also disseminates the notes on Slack. So this was a meeting where I was not, right? So this was a teammate. He's an indie meeting recorder, posts the meeting notes in this customer discovery channel on Slack. So you can see, okay, this is the onboarding call we had. This was the use case. Look at the questions. How do I make Lindy slower? How do I add delays to make Lindy slower? And I was able, in the Slack thread, to ask follow-up questions like, oh, what did we answer to these questions? And it's really handy because I know I can have this sort of interactive Q&A with these meetings. It means that very often now, I don't go to meetings anymore. I just send my Lindy. And instead of going to like a 60-minute meeting, I have like a five-minute chat with my Lindy afterwards. And she just replied. She was like, well, this is what we replied to this customer. And I can just be like, okay, good job, Jack. Like, no notes about your answers. So that's the kind of use cases people have with Lindy. It's a lot of like, there's a lot of sales automations, customer support automations, and a lot of this, which is basically personal assistance automations, like meeting scheduling and so forth.

Alessio [00:13:21]: Yeah, and I think the question that people might have is memory. So as you get coaching, how does it track whether or not you're improving? You know, if these are like mistakes you made in the past, like, how do you think about that?

Flo [00:13:31]: Yeah, we have a memory module. So I'll show you my meeting scheduler, Lindy, which has a lot of memories because by now I've used her for so long. And so every time I talk to her, she saves a memory. If I tell her, you screwed up, please don't do this. So you can see here, oh, it's got a double memory here. This is the meeting link I have, or this is the address of the office. If I tell someone to meet me at home, this is the address of my place. This is the code. I guess we'll have to edit that out. This is not the code of my place. No dogs. Yeah, so Lindy can just manage her own memory and decide when she's remembering things between executions. Okay.

Swyx [00:14:11]: I mean, I'm just going to take the opportunity to ask you, since you are the creator of this thing, how come there's so few memories, right? Like, if you've been using this for two years, there should be thousands of thousands of things. That is a good question.

Flo [00:14:22]: Agents still get confused if they have too many memories, to my point earlier about that. So I just am out of a call with a member of the Lama team at Meta, and we were chatting about Lindy, and we were going into the system prompt that we sent to Lindy, and all of that stuff. And he was amazed, and he was like, it's a miracle that it's working, guys. He was like, this kind of system prompt, this does not exist, either pre-training or post-training. These models were never trained to do this kind of stuff. It's a miracle that they can be agents at all. And so what I do, I actually prune the memories. You know, it's actually something I've gotten into the habit of doing from back when we had GPT 3.5, being Lindy agents. I suspect it's probably not as necessary in the Cloud 3.5 Sunette days, but I prune the memories. Yeah, okay.

Swyx [00:15:05]: The reason is because I have another assistant that also is recording and trying to come up with facts about me. It comes up with a lot of trivial, useless facts that I... So I spend most of my time pruning. Actually, it's not super useful. I'd much rather have high-quality facts that it accepts. Or maybe I was even thinking, were you ever tempted to add a wake word to only memorize this when I say memorize this? And otherwise, don't even bother.

Flo [00:15:30]: I have a Lindy that does this. So this is my inbox processor, Lindy. It's kind of beefy because there's a lot of different emails. But somewhere in here,

Swyx [00:15:38]: there is a rule where I'm like,

Flo [00:15:39]: aha, I can email my inbox processor, Lindy. It's really handy. So she has her own email address. And so when I process my email inbox, I sometimes forward an email to her. And it's a newsletter, or it's like a cold outreach from a recruiter that I don't care about, or anything like that. And I can give her a rule. And I can be like, hey, this email I want you to archive, moving forward. Or I want you to alert me on Slack when I have this kind of email. It's really important. And so you can see here, the prompt is, if I give you a rule about a kind of email, like archive emails from X, save it as a new memory. And I give it to the memory saving skill. And yeah.

Swyx [00:16:13]: One thing that just occurred to me, so I'm a big fan of virtual mailboxes. I recommend that everybody have a virtual mailbox. You could set up a physical mail receive thing for Lindy. And so then Lindy can process your physical mail.

Flo [00:16:26]: That's actually a good idea. I actually already have something like that. I use like health class mail. Yeah. So yeah, most likely, I can process my physical mail. Yeah.

Swyx [00:16:35]: And then the other product's idea I have, looking at this thing, is people want to brag about the complexity of their Lindys. So this would be like a 65 point Lindy, right?

Flo [00:16:43]: What's a 65 point?

Swyx [00:16:44]: Complexity counting. Like how many nodes, how many things, how many conditions, right? Yeah.

Flo [00:16:49]: This is not the most complex one. I have another one. This designer recruiter here is kind of beefy as well. Right, right, right. So I'm just saying,

Swyx [00:16:56]: let people brag. Let people be super users. Oh, right.

Flo [00:16:59]: Give them a score. Give them a score.

Swyx [00:17:01]: Then they'll just be like, okay, how high can you make this score?

Flo [00:17:04]: Yeah, that's a good point. And I think that's, again, the beauty of this on-rails phenomenon. It's like, think of the equivalent, the prompt equivalent of this Lindy here, for example, that we're looking at. It'd be monstrous. And the odds that it gets it right are so low. But here, because we're really holding the agent's hand step by step by step, it's actually super reliable. Yeah.

Swyx [00:17:22]: And is it all structured output-based? Yeah. As far as possible? Basically. Like, there's no non-structured output?

Flo [00:17:27]: There is. So, for example, here, this AI agent step, right, or this send message step, sometimes it gets to... That's just plain text.

Swyx [00:17:35]: That's right.

Flo [00:17:36]: Yeah. So I'll give you an example. Maybe it's TMI. I'm having blood pressure issues these days. And so this Lindy here, I give it my blood pressure readings, and it updates a log that I have of my blood pressure that it sends to my doctor.

Swyx [00:17:49]: Oh, so every Lindy comes with a to-do list?

Flo [00:17:52]: Yeah. Every Lindy has its own task history. Huh. Yeah. And so you can see here, this is my main Lindy, my personal assistant, and I've told it, where is this? There is a point where I'm like, if I am giving you a health-related fact, right here, I'm giving you health information, so then you update this log that I have in this Google Doc, and then you send me a message. And you can see, I've actually not configured this send message node. I haven't told it what to send me a message for. Right? And you can see, it's actually lecturing me. It's like, I'm giving it my blood pressure ratings. It's like, hey, it's a bit high. Here are some lifestyle changes you may want to consider.

Alessio [00:18:27]: I think maybe this is the most confusing or new thing for people. So even I use Lindy and I didn't even know you could have multiple workflows in one Lindy. I think the mental model is kind of like the Zapier workflows. It starts and it ends. It doesn't choose between. How do you think about what's a Lindy versus what's a sub-function of a Lindy? Like, what's the hierarchy?

Flo [00:18:48]: Yeah. Frankly, I think the line is a little arbitrary. It's kind of like when you code, like when do you start to create a new class versus when do you overload your current class. I think of it in terms of like jobs to be done and I think of it in terms of who is the Lindy serving. This Lindy is serving me personally. It's really my day-to-day Lindy. I give it a bunch of stuff, like very easy tasks. And so this is just the Lindy I go to. Sometimes when a task is really more specialized, so for example, I have this like summarizer Lindy or this designer recruiter Lindy. These tasks are really beefy. I wouldn't want to add this to my main Lindy, so I just created a separate Lindy for it. Or when it's a Lindy that serves another constituency, like our customer support Lindy, I don't want to add that to my personal assistant Lindy. These are two very different Lindys.

Alessio [00:19:31]: And you can call a Lindy from within another Lindy. That's right. You can kind of chain them together.

Flo [00:19:36]: Lindys can work together, absolutely.

Swyx [00:19:38]: A couple more things for the video portion. I noticed you have a podcast follower. We have to ask about that. What is that?

Flo [00:19:46]: So this one wakes me up every... So wakes herself up every week. And she sends me... So she woke up yesterday, actually. And she searches for Lenny's podcast. And she looks for like the latest episode on YouTube. And once she finds it, she transcribes the video and then she sends me the summary by email. I don't listen to podcasts as much anymore. I just like read these summaries. Yeah.

Alessio [00:20:09]: We should make a latent space Lindy. Marketplace.

Swyx [00:20:12]: Yeah. And then you have a whole bunch of connectors. I saw the list briefly. Any interesting one? Complicated one that you're proud of? Anything that you want to just share? Connector stories.

Flo [00:20:23]: So many of our workflows are about meeting scheduling. So we had to build some very open unity tools around meeting scheduling. So for example, one that is surprisingly hard is this find available times action. You would not believe... This is like a thousand lines of code or something. It's just a very beefy action. And you can pass it a bunch of parameters about how long is the meeting? When does it start? When does it end? What are the meetings? The weekdays in which I meet? How many time slots do you return? What's the buffer between my meetings? It's just a very, very, very complex action. I really like our GitHub action. So we have a Lindy PR reviewer. And it's really handy because anytime any bug happens... So the Lindy reads our guidelines on Google Docs. By now, the guidelines are like 40 pages long or something. And so every time any new kind of bug happens, we just go to the guideline and we add the lines. Like, hey, this has happened before. Please watch out for this category of bugs. And it's saving us so much time every day.

Alessio [00:21:19]: There's companies doing PR reviews. Where does a Lindy start? When does a company start? Or maybe how do you think about the complexity of these tasks when it's going to be worth having kind of like a vertical standalone company versus just like, hey, a Lindy is going to do a good job 99% of the time?

Flo [00:21:34]: That's a good question. We think about this one all the time. I can't say that we've really come up with a very crisp articulation of when do you want to use a vertical tool versus when do you want to use a horizontal tool. I think of it as very similar to the internet. I find it surprising the extent to which a horizontal search engine has won. But I think that Google, right? But I think the even more surprising fact is that the horizontal search engine has won in almost every vertical, right? You go through Google to search Reddit. You go through Google to search Wikipedia. I think maybe the biggest exception is e-commerce. Like you go to Amazon to search e-commerce, but otherwise you go through Google. And I think that the reason for that is because search in each vertical has more in common with search than it does with each vertical. And search is so expensive to get right. Like Google is a big company that it makes a lot of sense to aggregate all of these different use cases and to spread your R&D budget across all of these different use cases. I have a thesis, which is, it's a really cool thesis for Lindy, is that the same thing is true for agents. I think that by and large, in a lot of verticals, agents in each vertical have more in common with agents than they do with each vertical. I also think there are benefits in having a single agent platform because that way your agents can work together. They're all like under one roof. That way you only learn one platform and so you can create agents for everything that you want. And you don't have to like pay for like a bunch of different platforms and so forth. So I think ultimately, it is actually going to shake out in a way that is similar to search in that search is everywhere on the internet. Every website has a search box, right? So there's going to be a lot of vertical agents for everything. I think AI is going to completely penetrate every category of software. But then I also think there are going to be a few very, very, very big horizontal agents that serve a lot of functions for people.

Swyx [00:23:14]: That is actually one of the questions that we had about the agent stuff. So I guess we can transition away from the screen and I'll just ask the follow-up, which is, that is a hot topic. You're basically saying that the current VC obsession of the day, which is vertical AI enabled SaaS, is mostly not going to work out. And then there are going to be some super giant horizontal SaaS.

Flo [00:23:34]: Oh, no, I'm not saying it's either or. Like SaaS today, vertical SaaS is huge and there's also a lot of horizontal platforms. If you look at like Airtable or Notion, basically the entire no-code space is very horizontal. I mean, Loom and Zoom and Slack, there's a lot of very horizontal tools out there. Okay.

Swyx [00:23:49]: I was just trying to get a reaction out of you for hot takes. Trying to get a hot take.

Flo [00:23:54]: No, I also think it is natural for the vertical solutions to emerge first because it's just easier to build. It's just much, much, much harder to build something horizontal. Cool.

Swyx [00:24:03]: Some more Lindy-specific questions. So we covered most of the top use cases and you have an academy. That was nice to see. I also see some other people doing it for you for free. So like Ben Spites is doing it and then there's some other guy who's also doing like lessons. Yeah. Which is kind of nice, right? Yeah, absolutely. You don't have to do any of that.

Flo [00:24:20]: Oh, we've been seeing it more and more on like LinkedIn and Twitter, like people posting their Lindys and so forth.

Swyx [00:24:24]: I think that's the flywheel that you built the platform where creators see value in allying themselves to you. And so then, you know, your incentive is to make them successful so that they can make other people successful and then it just drives more and more engagement. Like it's earned media. Like you don't have to do anything.

Flo [00:24:39]: Yeah, yeah. I mean, community is everything.

Swyx [00:24:41]: Are you doing anything special there? Any big wins?

Flo [00:24:44]: We have a Slack community that's pretty active. I can't say we've invested much more than that so far.

Swyx [00:24:49]: I would say from having, so I have some involvement in the no-code community. I would say that Webflow going very hard after no-code as a category got them a lot more allies than just the people using Webflow. So it helps you to grow the community beyond just Lindy. And I don't know what this is called. Maybe it's just no-code again. Maybe you want to call it something different. But there's definitely an appetite for this and you are one of a broad category, right? Like just before you, we had Dust and, you know, they're also kind of going after a similar market. Zapier obviously is not going to try to also compete with you. Yeah. There's no question there. It's just like a reaction about community. Like I think a lot about community. Lanespace is growing the community of AI engineers. And I think you have a slightly different audience of, I don't know what.

Flo [00:25:33]: Yeah. I think the no-code tinkerers is the community. Yeah. It is going to be the same sort of community as what Webflow, Zapier, Airtable, Notion to some extent.

Swyx [00:25:43]: Yeah. The framing can be different if you were, so I think tinkerers has this connotation of not serious or like small. And if you framed it to like no-code EA, we're exclusively only for CEOs with a certain budget, then you just have, you tap into a different budget.

Flo [00:25:58]: That's true. The problem with EA is like, the CEO has no willingness to actually tinker and play with the platform.

Swyx [00:26:05]: Maybe Andrew's doing that. Like a lot of your biggest advocates are CEOs, right?

Flo [00:26:09]: A solopreneur, you know, small business owners, I think Andrew is an exception. Yeah. Yeah, yeah, he is.

Swyx [00:26:14]: He's an exception in many ways. Yep.

Alessio [00:26:16]: Just before we wrap on the use cases, is Rick rolling your customers? Like a officially supported use case or maybe tell that story?

Flo [00:26:24]: It's one of the main jobs to be done, really. Yeah, we woke up recently, so we have a Lindy obviously doing our customer support and we do check after the Lindy. And so we caught this email exchange where someone was asking Lindy for video tutorials. And at the time, actually, we did not have video tutorials. We do now on the Lindy Academy. And Lindy responded to the email. It's like, oh, absolutely, here's a link. And we were like, what? Like, what kind of link did you send? And so we clicked on the link and it was a recall. We actually reacted fast enough that the customer had not yet opened the email. And so we reacted immediately. Like, oh, hey, actually, sorry, this is the right link. And so the customer never reacted to the first link. And so, yeah, I tweeted about that. It went surprisingly viral. And I checked afterwards in the logs. We did like a database query and we found, I think, like three or four other instances of it having happened before.

Swyx [00:27:12]: That's surprisingly low.

Flo [00:27:13]: It is low. And we fixed it across the board by just adding a line to the system prompt that's like, hey, don't recall people, please don't recall.

Swyx [00:27:21]: Yeah, yeah, yeah. I mean, so, you know, you can explain it retroactively, right? Like, that YouTube slug has been pasted in so many different corpuses that obviously it learned to hallucinate that.

Alessio [00:27:31]: And it pretended to be so many things. That's the thing.

Swyx [00:27:34]: I wouldn't be surprised if that takes one token. Like, there's this one slug in the tokenizer and it's just one token.

Flo [00:27:41]: That's the idea of a YouTube video.

Swyx [00:27:43]: Because it's used so much, right? And you have to basically get it exactly correct. It's probably not. That's a long speech.

Flo [00:27:52]: It would have been so good.

Alessio [00:27:55]: So this is just a jump maybe into evals from here. How could you possibly come up for an eval that says, make sure my AI does not recall my customer? I feel like when people are writing evals, that's not something that they come up with. So how do you think about evals when it's such like an open-ended problem space?

Flo [00:28:12]: Yeah, it is tough. We built quite a bit of infrastructure for us to create evals in one click from any conversation history. So we can point to a conversation and we can be like, in one click we can turn it into effectively a unit test. It's like, this is a good conversation. This is how you're supposed to handle things like this. Or if it's a negative example, then we modify a little bit the conversation after generating the eval. So it's very easy for us to spin up this kind of eval.

Alessio [00:28:36]: Do you use an off-the-shelf tool which is like Brain Trust on the podcast? Or did you just build your own?

Flo [00:28:41]: We unfortunately built our own. We're most likely going to switch to Brain Trust. Well, when we built it, there was nothing. Like there was no eval tool, frankly. I mean, we started this project at the end of 2022. It was like, it was very, very, very early. I wouldn't recommend it to build your own eval tool. There's better solutions out there and our eval tool breaks all the time and it's a nightmare to maintain. And that's not something we want to be spending our time on.

Swyx [00:29:04]: I was going to ask that basically because I think my first conversations with you about Lindy was that you had a strong opinion that everyone should build their own tools. And you were very proud of your evals. You're kind of showing off to me like how many evals you were running, right?

Flo [00:29:16]: Yeah, I think that was before all of these tools came around. I think the ecosystem has matured a fair bit.

Swyx [00:29:21]: What is one thing that Brain Trust has nailed that you always struggled to do?

Flo [00:29:25]: We're not using them yet, so I couldn't tell. But from what I've gathered from the conversations I've had, like they're doing what we do with our eval tool, but better.

Swyx [00:29:33]: And like they do it, but also like 60 other companies do it, right? So I don't know how to shop apart from brand. Word of mouth.

Flo [00:29:41]: Same here.

Swyx [00:29:42]: Yeah, like evals or Lindys, there's two kinds of evals, right? Like in some way, you don't have to eval your system as much because you've constrained the language model so much. And you can rely on open AI to guarantee that the structured outputs are going to be good, right? We had Michelle sit where you sit and she explained exactly how they do constraint grammar sampling and all that good stuff. So actually, I think it's more important for your customers to eval their Lindys than you evaling your Lindy platform because you just built the platform. You don't actually need to eval that much.

Flo [00:30:14]: Yeah. In an ideal world, our customers don't need to care about this. And I think the bar is not like, look, it needs to be at 100%. I think the bar is it needs to be better than a human. And for most use cases we serve today, it is better than a human, especially if you put it on Rails.

Swyx [00:30:30]: Is there a limiting factor of Lindy at the business? Like, is it adding new connectors? Is it adding new node types? Like how do you prioritize what is the most impactful to your company?

Flo [00:30:41]: Yeah. The raw capabilities for sure are a big limit. It is actually shocking the extent to which the model is no longer the limit. It was the limit a year ago. It was too expensive. The context window was too small. It's kind of insane that we started building this when the context windows were like 4,000 tokens. Like today, our system prompt is more than 4,000 tokens. So yeah, the model is actually very much not a limit anymore. It almost gives me pause because I'm like, I want the model to be a limit. And so no, the integrations are ones, the core capabilities are ones. So for example, we are investing in a system that's basically, I call it like the, it's a J hack. Give me these names, like the poor man's RLHF. So you can turn on a toggle on any step of your Lindy workflow to be like, ask me for confirmation before you actually execute this step. So it's like, hey, I receive an email, you send a reply, ask me for confirmation before actually sending it. And so today you see the email that's about to get sent and you can either approve, deny, or change it and then approve. And we are making it so that when you make a change, we are then saving this change that you're making or embedding it in the vector database. And then we are retrieving these examples for future tasks and injecting them into the context window. So that's the kind of capability that makes a huge difference for users. That's the bottleneck today. It's really like good old engineering and product work.

Swyx [00:31:52]: I assume you're hiring. We'll do a call for hiring at the end.

Alessio [00:31:54]: Any other comments on the model side? When did you start feeling like the model was not a bottleneck anymore? Was it 4.0? Was it 3.5? 3.5.

Flo [00:32:04]: 3.5 Sonnet, definitely. I think 4.0 is overhyped, frankly. We don't use 4.0. I don't think it's good for agentic behavior. Yeah, 3.5 Sonnet is when I started feeling that. And then with prompt caching with 3.5 Sonnet, like that fills the cost, cut the cost again. Just cut it in half. Yeah.

Swyx [00:32:21]: Your prompts are... Some of the problems with agentic uses is that your prompts are kind of dynamic, right? Like from caching to work, you need the front prefix portion to be stable.

Flo [00:32:32]: Yes, but we have this append-only ledger paradigm. So every node keeps appending to that ledger and every filled node inherits all the context built up by all the previous nodes. And so we can just decide, like, hey, every X thousand nodes, we trigger prompt caching again.

Swyx [00:32:47]: Oh, so you do it like programmatically, not all the time.

Flo [00:32:50]: No, sorry. Anthropic manages that for us. But basically, it's like, because we keep appending to the prompt, the prompt caching works pretty well.

Alessio [00:32:57]: We have this small podcaster tool that I built for the podcast and I rewrote all of our prompts because I noticed, you know, I was inputting stuff early on. I wonder how much more money OpenAN and Anthropic are making just because people don't rewrite their prompts to be like static at the top and like dynamic at the bottom.

Flo [00:33:13]: I think that's the remarkable thing about what we're having right now. It's insane that these companies are routinely cutting their costs by two, four, five. Like, they basically just apply constraints. They want people to take advantage of these innovations. Very good.

Swyx [00:33:25]: Do you have any other competitive commentary? Commentary? Dust, WordWare, Gumloop, Zapier? If not, we can move on.

Flo [00:33:31]: No comment.

Alessio [00:33:32]: I think the market is,

Flo [00:33:33]: look, I mean, AGI is coming. All right, that's what I'm talking about.

Swyx [00:33:38]: I think you're helping. Like, you're paving the road to AGI.

Flo [00:33:41]: I'm playing my small role. I'm adding my small brick to this giant, giant, giant castle. Yeah, look, when it's here, we are going to, this entire category of software is going to create, it's going to sound like an exaggeration, but it is a fact it is going to create trillions of dollars of value in a few years, right? It's going to, for the first time, we're actually having software directly replace human labor. I see it every day in sales calls. It's like, Lindy is today replacing, like, we talk to even small teams. It's like, oh, like, stop, this is a 12-people team here. I guess we'll set up this Lindy for one or two days, and then we'll have to decide what to do with this 12-people team. And so, yeah. To me, there's this immense uncapped market opportunity. It's just such a huge ocean, and there's like three sharks in the ocean. I'm focused on the ocean more than on the sharks.

Swyx [00:34:25]: So we're moving on to hot topics, like, kind of broadening out from Lindy, but obviously informed by Lindy. What are the high-order bits of good agent design?

Flo [00:34:31]: The model, the model, the model, the model. I think people fail to truly, and me included, they fail to truly internalize the bitter lesson. So for the listeners out there who don't know about it, it's basically like, you just scale the model. Like, GPUs go brr, it's all that matters. I think it also holds for the cognitive architecture. I used to be very cognitive architecture-filled, and I was like, ah, and I was like a critic, and I was like a generator, and all this, and then it's just like, GPUs go brr, like, just like let the model do its job. I think we're seeing it a little bit right now with O1. I'm seeing some tweets that say that the new 3.5 SONNET is as good as O1, but with none of all the crazy...

Swyx [00:35:09]: It beats O1 on some measures. On some reasoning tasks. On AIME, it's still a lot lower. Like, it's like 14 on AIME versus O1, it's like 83.

Flo [00:35:17]: Got it. Right. But even O1 is still the model. Yeah.

Swyx [00:35:22]: Like, there's no cognitive architecture on top of it.

Flo [00:35:23]: You can just wait for O1 to get better.

Alessio [00:35:25]: And so, as a founder, how do you think about that, right? Because now, knowing this, wouldn't you just wait to start Lindy? You know, you start Lindy, it's like 4K context, the models are not that good. It's like, but you're still kind of like going along and building and just like waiting for the models to get better. How do you today decide, again, what to build next, knowing that, hey, the models are going to get better, so maybe we just shouldn't focus on improving our prompt design and all that stuff and just build the connectors instead or whatever? Yeah.

Flo [00:35:51]: I mean, that's exactly what we do. Like, all day, we always ask ourselves, oh, when we have a feature idea or a feature request, we ask ourselves, like, is this the kind of thing that just gets better while we sleep because models get better? I'm reminded, again, when we started this in 2022, we spent a lot of time because we had to around context pruning because 4,000 tokens is really nothing. You really can't do anything with 4,000 tokens. All that work was throwaway work. Like, now it's like it was for nothing, right? Now we just assume that infinite context windows are going to be here in a year or something, a year and a half, and infinitely cheap as well, and dynamic compute is going to be here. Like, we just assume all of these things are going to happen, and so we really focus, our job to be done in the industry is to provide the input and output to the model. I really compare it all the time to the PC and the CPU, right? Apple is busy all day. They're not like a CPU wrapper. They have a lot to build, but they don't, well, now actually they do build the CPU as well, but leaving that aside, they're busy building a laptop. It's just a lot of work to build these things. It's interesting because, like,

Swyx [00:36:45]: for example, another person that we're close to, Mihaly from Repl.it, he often says that the biggest jump for him was having a multi-agent approach, like the critique thing that you just said that you don't need, and I wonder when, in what situations you do need that and what situations you don't. Obviously, the simple answer is for coding, it helps, and you're not coding, except for, are you still generating code? In Indy? Yeah.

Flo [00:37:09]: No, we do. Oh, right. No, no, no, the cognitive architecture changed. We don't, yeah.

Swyx [00:37:13]: Yeah, okay. For you, you're one shot, and you chain tools together, and that's it. And if the user really wants

Flo [00:37:18]: to have this kind of critique thing, you can also edit the prompt, you're welcome to. I have some of my Lindys, I've told them, like, hey, be careful, think step by step about what you're about to do, but that gives you a little bump for some use cases, but, yeah.

Alessio [00:37:30]: What about unexpected model releases? So, Anthropic released computer use today. Yeah. I don't know if many people were expecting computer use to come out today. Do these things make you rethink how to design, like, your roadmap and things like that, or are you just like, hey, look, whatever, that's just, like, a small thing in their, like, AGI pursuit, that, like, maybe they're not even going to support, and, like, it's still better for us to build our own integrations into systems and things like that. Because maybe people will say, hey, look, why am I building all these API integrations

Flo [00:38:02]: when I can just do computer use and never go to the product? Yeah. No, I mean, we did take into account computer use. We were talking about this a year ago or something, like, we've been talking about it as part of our roadmap. It's been clear to us that it was coming, My philosophy about it is anything that can be done with an API must be done by an API or should be done by an API for a very long time. I think it is dangerous to be overly cavalier about improvements of model capabilities. I'm reminded of iOS versus Android. Android was built on the JVM. There was a garbage collector, and I can only assume that the conversation that went down in the engineering meeting room was, oh, who cares about the garbage collector? Anyway, Moore's law is here, and so that's all going to go to zero eventually. Sure, but in the meantime, you are operating on a 400 MHz CPU. It was like the first CPU on the iPhone 1, and it's really slow, and the garbage collector is introducing a tremendous overhead on top of that, especially a memory overhead. For the longest time, and it's really only been recently that Android caught up to iOS in terms of how smooth the interactions were, but for the longest time, Android phones were significantly slower

Swyx [00:39:07]: and laggier

Flo [00:39:08]: and just not feeling as good as iOS devices. Look, when you're talking about modules and magnitude of differences in terms of performance and reliability, which is what we are talking about when we're talking about API use versus computer use, then you can't ignore that, right? And so I think we're going to be in an API use world for a while.

Swyx [00:39:27]: O1 doesn't have API use today. It will have it at some point, and it's on the roadmap. There is a future in which OpenAI goes much harder after your business, your market, than it is today. Like, ChatGPT, it's its own business. All they need to do is add tools to the ChatGPT, and now they're suddenly competing with you. And by the way, they have a GPT store where a bunch of people have already configured their tools to fit with them. Is that a concern?

Flo [00:39:56]: I think even the GPT store, in a way, like the way they architect it, for example, their plug-in systems are actually grateful because we can also use the plug-ins. It's very open. Now, again, I think it's going to be such a huge market. I think there's going to be a lot of different jobs to be done. I know they have a huge enterprise offering and stuff, but today, ChatGPT is a consumer app. And so, the sort of flow detail I showed you, this sort of workflow, this sort of use cases that we're going after, which is like, we're doing a lot of lead generation and lead outreach and all of that stuff. That's not something like meeting recording, like Lindy Today right now joins your Zoom meetings and takes notes, all of that stuff.

Swyx [00:40:34]: I don't see that so far

Flo [00:40:35]: on the OpenAI roadmap.

Swyx [00:40:36]: Yeah, but they do have an enterprise team that we talk to You're hiring GMs?

Flo [00:40:42]: We did.

Swyx [00:40:43]: It's a fascinating way to build a business, right? Like, what should you, as CEO, be in charge of? And what should you basically hire

Flo [00:40:52]: a mini CEO to do? Yeah, that's a good question. I think that's also something we're figuring out. The GM thing was inspired from my days at Uber, where we hired one GM per city or per major geo area. We had like all GMs, regional GMs and so forth. And yeah, Lindy is so horizontal that we thought it made sense to hire GMs to own each vertical and the go-to market of the vertical and the customization of the Lindy templates for these verticals and so forth. What should I own as a CEO? I mean, the canonical reply here is always going to be, you know, you own the fundraising, you own the culture, you own the... What's the rest of the canonical reply? The culture, the fundraising.

Swyx [00:41:29]: I don't know,

Flo [00:41:30]: products. Even that, eventually, you do have to hand out. Yes, the vision, the culture, and the foundation. Well, you've done your job as a CEO. In practice, obviously, yeah, I mean, all day, I do a lot of product work still and I want to keep doing product work for as long as possible.

Swyx [00:41:48]: Obviously, like you're recording and managing the team. Yeah.

Flo [00:41:52]: That one feels like the most automatable part of the job, the recruiting stuff.

Swyx [00:41:56]: Well, yeah. You saw my

Flo [00:41:59]: design your recruiter here. Relationship between Factorio and building Lindy. We actually very often talk about how the business of the future is like a game of Factorio. Yeah. So, in the instance, it's like Slack and you've got like 5,000 Lindys in the sidebar and your job is to somehow manage your 5,000 Lindys. And it's going to be very similar to company building because you're going to look for like the highest leverage way to understand what's going on in your AI company and understand what levels do you have to make impact in that company. So, I think it's going to be very similar to like a human company except it's going to go infinitely faster. Today, in a human company, you could have a meeting with your team and you're like, oh, I'm going to build a facility and, you know, now it's like, okay,

Swyx [00:42:40]: boom, I'm going to spin up 50 designers. Yeah. Like, actually, it's more important that you can clone an existing designer that you know works because the hiring process, you cannot clone someone because every new person you bring in is going to have their own tweaks

Flo [00:42:54]: and you don't want that. Yeah.

Swyx [00:42:56]: That's true. You want an army of mindless drones

Flo [00:42:59]: that all work the same way.

Swyx [00:43:00]: The reason I bring this, bring Factorio up as well is one, Factorio Space just came out. Apparently, a whole bunch of people stopped working. I tried out Factorio. I never really got that much into it. But the other thing was, you had a tweet recently about how the sort of intentional top-down design was not as effective as just build. Yeah. Just ship.

Flo [00:43:21]: I think people read a little bit too much into that tweet. It went weirdly viral. I was like, I did not intend it as a giant statement online.

Swyx [00:43:28]: I mean, you notice you have a pattern with this, right? Like, you've done this for eight years now.

Flo [00:43:33]: You should know. I legit was just hearing an interesting story about the Factorio game I had. And everybody was like, oh my God, so deep. I guess this explains everything about life and companies. There is something to be said, certainly, about focusing on the constraint. And I think it is Patrick Collison who said, people underestimate the extent to which moonshots are just one pragmatic step taken after the other. And I think as long as you have some inductive bias about, like, some loose idea about where you want to go, I think it makes sense to follow a sort of greedy search along that path. I think planning and organizing is important. And having older is important.

Swyx [00:44:05]: I'm wrestling with that. There's two ways I encountered it recently. One with Lindy. When I tried out one of your automation templates and one of them was quite big and I just didn't understand it, right? So, like, it was not as useful to me as a small one that I can just plug in and see all of. And then the other one was me using Cursor. I was very excited about O1 and I just up front

Flo [00:44:27]: stuffed everything

Swyx [00:44:28]: I wanted to do into my prompt and expected O1 to do everything. And it got itself into a huge jumbled mess and it was stuck. It was really... There was no amount... I wasted, like, two hours on just, like, trying to get out of that hole. So I threw away the code base, started small, switched to Clouds on it and build up something working and just add it over time and it just worked. And to me, that was the factorial sentiment, right? Maybe I'm one of those fanboys that's just, like, obsessing over the depth of something that you just randomly tweeted out. But I think it's true for company building, for Lindy building, for coding.

Flo [00:45:02]: I don't know. I think it's fair and I think, like, you and I talked about there's the Tuft & Metal principle and there's this other... Yes, I love that. There's the... I forgot the name of this other blog post but it's basically about this book Seeing Like a State that talks about the need for legibility and people who optimize the system for its legibility and anytime you make a system... So legible is basically more understandable. Anytime you make a system more understandable from the top down, it performs less well from the bottom up. And it's fine but you should at least make this trade-off with your eyes wide open. You should know, I am sacrificing performance for understandability, for legibility. And in this case, for you, it makes sense. It's like you are actually optimizing for legibility. You do want to understand your code base but in some other cases it may not make sense. Sometimes it's better to leave the system alone and let it be its glorious, chaotic, organic self and just trust that it's going to perform well even though you don't understand it completely.

Swyx [00:45:55]: It does remind me of a common managerial issue or dilemma which you experienced in the small scale of Lindy where, you know, do you want to organize your company by functional sections or by products or, you know, whatever the opposite of functional is. And you tried it one way and it was more legible to you as CEO but actually it stopped working at the small level. Yeah.

Flo [00:46:17]: I mean, one very small example, again, at a small scale is we used to have everything on Notion. And for me, as founder, it was awesome because everything was there. The roadmap was there. The tasks were there. The postmortems were there. And so, the postmortem was linked

Swyx [00:46:31]: to its task.

Flo [00:46:32]: It was optimized for you. Exactly. And so, I had this, like, one pane of glass and everything was on Notion. And then the team, one day,

Swyx [00:46:39]: came to me with pitchforks

Flo [00:46:40]: and they really wanted to implement Linear. And I had to bite my fist so hard. I was like, fine, do it. Implement Linear. Because I was like, at the end of the day, the team needs to be able to self-organize and pick their own tools.

Alessio [00:46:51]: Yeah. But it did make the company slightly less legible for me. Another big change you had was going away from remote work, every other month. The discussion comes up again. What was that discussion like? How did your feelings change? Was there kind of like a threshold of employees and team size where you felt like, okay, maybe that worked. Now it doesn't work anymore. And how are you thinking about the future

Flo [00:47:12]: as you scale the team? Yeah. So, for context, I used to have a business called TeamFlow. The business was about building a virtual office for remote teams. And so, being remote was not merely something we did. It was, I was banging the remote drum super hard and helping companies to go remote. And so, frankly, in a way, it's a bit embarrassing for me to do a 180 like that. But I guess, when the facts changed, I changed my mind. What happened? Well, I think at first, like everyone else, we went remote by necessity. It was like COVID and you've got to go remote. And on paper, the gains of remote are enormous. In particular, from a founder's standpoint, being able to hire from anywhere is huge. Saving on rent is huge. Saving on commute is huge for everyone and so forth. But then, look, we're all here. It's like, it is really making it much harder to work together. And I spent three years of my youth trying to build a solution for this. And my conclusion is, at least we couldn't figure it out and no one else could. Zoom didn't figure it out. We had like a bunch of competitors. Like, Gathertown was one of the bigger ones. We had dozens and dozens of competitors. No one figured it out. I don't know that software can actually solve this problem. The reality of it is, everyone just wants to get off the darn Zoom call. And it's not a good feeling to be in your home office if you're even going to have a home office all day. It's harder to build culture. It's harder to get in sync. I think software is peculiar because it's like an iceberg. It's like the vast majority of it is submerged underwater. And so, the quality of the software that you ship is a function of the alignment of your mental models about what is below that waterline. Can you actually get in sync about what it is exactly fundamentally that we're building? What is the soul of our product? And it is so much harder to get in sync about that when you're remote. And then you waste time in a thousand ways because people are offline and you can't get a hold of them or you can't share your screen. It's just like you feel like you're walking in molasses all day. And eventually, I was like, okay, this is it. We're not going to do this anymore.

Swyx [00:49:03]: Yeah. I think that is the current builder San Francisco consensus here. Yeah. But I still have a big... One of my big heroes as a CEO is Sid Subban from GitLab.

Flo [00:49:14]: Mm-hmm.

Swyx [00:49:15]: Matt Mullenweg

Flo [00:49:16]: used to be a hero.

Swyx [00:49:17]: But these people run thousand-person remote businesses. The main idea is that at some company size, your company is remote anyway. Yeah. Because if you go from one building to two buildings, congrats, you're now remote from the other building. If you want to go from one city office to two city offices, they're remote from each other.

Flo [00:49:35]: But the teams are co-located. Every time anyone talks about remote success stories, they always talk about this real force. Yeah. It's always GitLab and WordPress and Zapier. Zapier. It used to be Envision. And I will point out that in every one of these examples, you have a co-located counterfactual that is sometimes orders of magnitude bigger. Look, I like Matt Mullenweg a lot, but WordPress is a commercial failure. They run 60% of the internet and they're like a fraction of the size of even Substack. Right?

Swyx [00:50:05]: They're trying to get more money.

Flo [00:50:07]: Yeah, that's my point, right? Look, GitLab is much smaller than GitHub. Envision, you know, is no more. And Figma, like, completely took off. And Figma was like very in-person. So, I think if you're optimizing for productivity, if you really know, hey, this is a support ticket, right, and I want to have my support ticket for a buck 50 per support ticket and next year I want it for a buck 20, then sure, send your support ticket team to offshore, like the Philippines or whatever, and just optimize for cost. If you're optimizing for cost, absolutely be remote. If you're optimizing for creativity, which I think that software and product building is a creative endeavor, if you're optimizing for creativity, it's kind of like you have to be in person and hear the music to do that.

Swyx [00:50:52]: Yeah. Maybe the line is that all jobs that can be remote should be AI or Lindy's and all jobs that are not remote are in person. Like, there's a very,

Flo [00:51:04]: very clear separation of jobs. Sure. Well, I think over the long term,

Swyx [00:51:09]: every job is going to be AI anyway. It would be curious to break down what you think is creativity in coding and in product defining and how to express that for sure. You're definitely what I call a temperature zero use case of LLMs. You want it to be reliable, predictable, small. And then there's other use cases of LLMs that are more for creativity and engines. Right? I haven't checked, but I'm pretty sure no one uses Lindy for brainstorming. Actually,

Flo [00:51:36]: probably they do. I use Lindy for brainstorming

Swyx [00:51:38]: a lot, actually. Yeah, yeah. But you want to have something that's anti-fragile to hallucination. Hallucinations are good.

Flo [00:51:45]: By creativity, I mean, is it about direction or magnitude? If it is about direction, like decide what to do, then it's a creative endeavor. If it is about magnitude and just do it as fast as possible, as cheap as possible, then it's magnitude. And so sometimes, you know, software companies are not necessarily creative. Sometimes you know what you're doing. And I'll say that it's going to come across the wrong way, but linear. I look up to a huge amount, like such amazing product builders, but they know what they're building. They're building a I don't mean to throw shade at them. Like, good for them.

Swyx [00:52:20]: I think they're aware that they're not like They recently got s**t for saying that they have work-life balance on their job description.

Flo [00:52:26]: They're like, what do you mean by this? We're building a new kind of product that no one's ever built before. And so we're just scratching our heads all day trying to get in sync about like, what exactly is it

Swyx [00:52:37]: that we're building? What does it consist of? Inherently creative struggle. Yeah. Dare we ask about San Francisco? And there's a whole bunch of tough stuff in here. Probably the biggest one I would just congratulate you on is becoming American, right? Very French, but your heart was sort of in the U.S. You eventually found your way here. What are your takes for founders? A few years ago, you wrote this post on Go West, young man. And now you've basically completed that journey, right? You're now here and up to the point where you're kind of mystified by how Europe has been so decel.

Flo [00:53:11]: In a way, though, I feel vindicated because I was making the prediction that Europe was over 14 years ago or something like that. I think it's been a walking corpse for a long time. I think it is only now becoming obvious that it is paying the consequences of its policies from 10, 20, 30 years ago. I think at this point, I wish I could rewrite the Go West, young man article but really even more extreme. I think at this point, if you are in tech, especially in AI, but if you're in tech and you're not in San Francisco, you either lack judgment or you lack ambition. It's funny, I recently told that to someone and they were like, oh, not everyone wants to be like a unicorn founder. And I was like, like I said, judgment or ambition. It's fine to not have ambition. It's fine to want to prioritize other things than your company in life or your career in life. That's perfectly okay. But know that that's the trade-off you're making. If you prioritize your career, you've got to be here.

Alessio [00:54:03]: As a fellow European escapist, I grew up in Rome.

Flo [00:54:05]: Yeah, how do you feel?

Swyx [00:54:06]: We never talk about your feelings about Europe.

Alessio [00:54:08]: Yeah, I've been in the U.S. now six years. Well, I started my first company in Europe 10 years ago, something like that. Yeah, you can tell nobody really wants to do much. And then you're like, okay. It's funny, I was looking back through some old tweets and I was sending all these tweets to Marc Andreessen like 15 years ago like trying to like learn more about why are you guys putting money in these things that most people here would say you're like crazy to like even back. And eventually, you know, I started doing venture six, five years ago. And I think just like so many people in Europe reach out and ask, hey, can you like talk to our team and they just cannot comprehend like the risk appetite that people have here. It's just like so foreign to people, at least in Italy and like in some parts of Europe. I'm sure there's some great founders in Europe, but like the average European founders, like why would I leave my job at the post office to go work on the startup that could change everything and become very successful but might go out of business instead in the U.S. You have like, you know, we host a hackathon and it's like 400 people and it's like, where can I go work that it's like no job security, you know? It's just like completely different and there's no incentives from the government to change that. There's no way you can like change such a deep-rooted culture of like, you know, going and wine and April spritz

Flo [00:55:27]: and all of that

Alessio [00:55:28]: early in the afternoon.

Flo [00:55:29]: So, I don't really know how it's going to change.

Alessio [00:55:32]: It's quality of life. Yeah, totally. That's why I left. The quality is so high that I left. But again, I think it's better to move here and just, if you want to do this job and do this, you should be here. If you don't want to, that's fine.

Flo [00:55:47]: But like,

Alessio [00:55:48]: don't copium. Don't be like, oh no, you can also be successful doing this and knees or like whatever. No, probably not, you know? So,

Flo [00:55:59]: yeah,

Alessio [00:56:00]: I've already done my N400

Flo [00:56:01]: so I should get my U.S. citizenship interview soon. Yeah. And I think to be fair, I think what's happening right now to Europe and they've said no to capitalism. They've decided to say no to capitalism a long time ago. They've like completely over-regulated. Taxation is much too high and so forth. But I also think some of this is a little bit of a self-fulfilling prophecy or it's a self-perpetuating phenomenon because, look, to your point, like once there is a network effect that's just so incredibly powerful, they can't be broken, really. And we tried with San Francisco. I tried with San Francisco. Like during COVID,

Swyx [00:56:35]: there was a movement of people moving to Miami.

Flo [00:56:38]: How did that pan out? You can't break the network effect,

Swyx [00:56:41]: you know? It's so annoying because first principles wise, tech should not be here. Like tech should be in Miami because it's just a better city.

Flo [00:56:48]: San Francisco does not want tech to be here.

Swyx [00:56:50]: San Francisco hates tech.

Flo [00:56:51]: 100%.

Swyx [00:56:52]: This is the thing I actually wrote down.

Alessio [00:56:54]: San Francisco hates tech. It is true. I think the people that are in San Francisco that were here before, tech hated it and then there's kind of like this passed down thing. But I would say people in Miami would hate it too if there were too much of it. You know? The Mickey Beach crowd would also not gel.

Swyx [00:57:08]: They're just rich enough and chill enough to not care.

Flo [00:57:10]: Yeah, I think so too.

Swyx [00:57:11]: They're like, oh, crypto kids.

Flo [00:57:13]: Okay, cool. Yeah. Miami celebrates success which is one thing

Swyx [00:57:17]: I loved about it.

Flo [00:57:18]: A little bit too much.

Swyx [00:57:19]: Maybe the last thing I'll mention, I just wanted a little bit of EUAC talk. I think that's good. I'll maybe carve out that I think the UK has done really well. That's an argument for the UK not being part of Europe is that, you know, the AI institutions there at least have done very well. Right?

Flo [00:57:34]: Sure. I think a lot of Britain is in the gutter. Yeah, exactly.

Swyx [00:57:38]: They've been stagnating at best. And then France has a few wins.

Flo [00:57:41]: Who?

Swyx [00:57:42]: Mistral.

Flo [00:57:43]: Who uses Mistral?

Swyx [00:57:44]: Hugging face.

Flo [00:57:45]: A few wins.

Swyx [00:57:46]: I'm just saying. They disappointed their first AI minister. You know the meme with the guy

Flo [00:57:51]: who's celebrating with his trophy and then he's like, no, that's France. Right? To me, that's France. It's like, aha, look, we've got Mistral! It's like champagne! It's like maybe 1% of market share. And by the way, and it's not a critic of them, it's a critic of France and of Europe. And by the way, I think I've heard that the Mistral guys were moving to the US. They're opening an office here. They're opening an office here. But, I mean,

Swyx [00:58:15]: they're very French, right?

Flo [00:58:16]: Right.

Swyx [00:58:17]: You can't really avoid it. There's one interesting counter move which is Jason Warner and ISOCAT moving to Paris for poolside. I don't know. It remains to be seen how that move is going. Maybe the last thing I'll say, you know, that's the Europe talk. We try not to do politics so much, but you're here. One thing that you do a lot is you test your overturned windows. Right? Like far more than any founder I know. You know it's not your job. Someone, for sure, you're just indulging. But also, I think you consciously test. And I just want to see what drives you there and why do you keep doing it? Because you treat very spicy stuff, especially for like the San Francisco sort of liberal dynasty.