Simon Willison: Using LLMs for Python Development

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.

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

Show Links:

Simon Willison’s Weblog shot-scraper Matt’s Script Archive, Inc. - Free Perl CGI Scripts XR - my XML-RPC library, now in WordPress - GitHub Adrian Holovaty advertises for someone to join him working in Lawrence (May 2003) - Holovaty.com Datasette: An open source multi-tool for exploring and publishing data My SQLite tag page - Simon Willison Chatbot Arena: Free AI Chat to Compare & Test Best AI Chatbots DeepSeek v3 notes on Christmas day DeepSeek_V3 - PDF Simon Willison on code-interpreter Gemini - Google DeepMind Claude ChatGPT Canvas can make API requests now, but it’s complicated Welcome to Click — Click Documentation My first experience with Llama in March 2023 I can now run a GPT-4 class model on my laptop Using LLMs and Cursor to become a finisher GitHub Copilot - Your AI pair programmer In Finland, classes in recognizing fake news, disinformation - Sunday Morning CBS 404Media Podcast: Why We Cover AI the Way We Do Jason Koebler from 404Media - tags on simonwillison.net Building Python tools with a one-shot prompt using uv run and Claude Projects How I program with LLMs - crawshaw - 2025-01-06 pelican-riding-a-bicycle - tags on simonwillison.net Things we learned about LLMs in 2024 Pyodide Simon Willison on pyodide astral-sh/uv: An extremely fast Python package and project manager Simon Willison on uv Simon Willison’s Newsletter - Substack Semi-automating a Substack newsletter with an Observable notebook Simon Willison (@simonwillison.net) — Bluesky Simon Willison (@simon@simonwillison.net) - Mastodon Simon Willison (@simonw) - X

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