What are the current large language model (LLM) tools you can use to develop Python? What prompting techniques and strategies produce better results? This week on the show, we speak with Simon Willison about his LLM research and his exploration of writing Python code with these rapidly evolving tools.
Simon has been researching LLMs over the past two and a half years and documenting the results on his blog. He shares which models work best for writing Python versus JavaScript and compares coding tools and environments.
We discuss prompt engineering techniques and the first steps to take. Simon shares his enthusiasm for the usefulness of LLMs but cautions about the potential pitfalls.
Simon also shares how he got involved in open-source development and Django. He’s a proponent of starting a blog and shares how it opened doors for his career.
This episode is sponsored by Postman.
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Topics:
- 00:00:00 – Introduction
- 00:02:38 – How did you get involved in open source?
- 00:04:04 – Writing an XML-RPC library
- 00:04:40 – Working on Django in Lawrence, Kansas
- 00:05:31 – Started building open-source collection
- 00:06:52 – shot-scraper: taking automated screenshots of websites
- 00:08:09 – First experiences with LLMs
- 00:10:08 – 22 years of simonwillison.net
- 00:18:22 – Navigating the hype and criticism of LLMs
- 00:22:14 – Where to start with Python code and LLMs?
- 00:26:22 – Sponsor: Postman
- 00:27:13 – ChatGPT Canvas vs Code Interpreter
- 00:28:23 – Asking nicely, tricking the system, and tipping?
- 00:30:35 – More Code Interpreter and building a C extension
- 00:32:05 – More details on Canvas
- 00:36:55 – What is a workflow for developing using LLMs?
- 00:39:43 – Creating pieces of code vs a system
- 00:42:00 – Workout program for prompting and pitfalls
- 00:53:54 – Video Course Spotlight
- 00:55:14 – Why an SVG of a pelican riding a bicycle?
- 00:57:48 – Repeating a query and refining
- 01:03:00 – Working in an IDE or text editor
- 01:05:45 – David Crawshaw on writing code with LLMs
- 01:08:33 – Running an LLM locally to write code
- 01:14:02 – Staying out of the AGI conversation
- 01:16:07 – What are you excited about in the world of Python?
- 01:18:34 – What do you want to learn next?
- 01:19:53 – How can people follow your work online?
- 01:20:51 – Thanks and goodbye
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