Today, we're joined by Simon Willison, independent researcher and creator of Datasette to discuss the many ways software developers and engineers can take advantage of large language models (LLMs) to boost their productivity. We dig into Simon’s own workflows and how he uses popular models like ChatGPT and Anthropic’s Claude to write and test hundreds of lines of code while out walking his dog. We review Simon’s favorite prompting and debugging techniques, his strategies for sidestepping the limitations of contemporary models, how he uses Claude’s Artifacts feature for rapid prototyping, his thoughts on the use and impact of vision models, the role he sees for open source models and local LLMs, and much more.
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📖 CHAPTERS
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00:00 - Introduction
6:44 - Generative AI tools
12:30 - ChatGPT Code Interpreter
18:28 - Modes of programming
21:52 - IDE Plugins
26:38 - LLM mental model
30:28 - Handling LLM limitations
34:32 - Vision models
41:53 - Visual prompting
43:00 - Agents and agentic systems
48:15 - RAG
53:50 - Ecosystem of open source models
59:49 - Prompting techniques
1:03:35 - Scraping
1:06:09 - Recap
1:08:21 - Future directions
🔗 LINKS & RESOURCES
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Simon Willison’s Weblog - https://simonwillison.net/
Datasette.io - https://datasette.io/
TWIML Generative AI Study Group - https://twimlai.com/community/
Shot-scraper - https://github.com/simonw/shot-scraper
ChatGPT - https://openai.com/chatgpt/
Claude AI - https://claude.ai/
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