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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