Independent open source developer and creator of Datasette
Simon Willison
Simon Willison is an independent developer, Datasette creator, and Django co-creator who builds open source tools and writes about language models.
About Simon Willison
Simon Willison is an independent open source developer and the creator of Datasette, a tool for exploring and publishing data. He builds software for data journalism and maintains LLM, a Python library and command-line tool for working with large language models.
He is a co-creator of the Django web framework and previously worked as an engineering director at Eventbrite after its acquisition of Lanyrd, which he co-founded. His long-running blog documents programming, AI tools, and experiments with language models.
Mentioned in our coverage
47 articles name Simon Willison, excluding author credits.
Page 3 of 4
- MCP 2026-07-28: Stateless Core, Apps, Tasks, and Enterprise Auth →
The largest MCP update since launch: no more sticky sessions, formal extensions (Apps + Tasks), hardened enterprise auth, and 400M+ monthly SDK downloads — Claude is rolling support now.
- Token Relay Market: How Cheap Claude Access Gets Sold →
Relays sell Anthropic-class usage at ~97% off list via pooled keys and OpenAI-compatible gateways. explainx.ai decodes the supply chain, V2EX jargon, and why KYC only moves abuse downstream.
- Cloudflare AI Traffic: Search, Agent, Training →
Cloudflare’s Content Independence Day update gives every plan Search / Agent / Training controls. explainx.ai covers the Sept 15 defaults, multi-purpose crawler traps, BotBase, content-use signals, and the HN Googlebot debate.
- Claude Cookbook: What to Read (and Ignore) in 2026 →
The Claude Cookbook isn’t a food blog — it’s Anthropic’s practical notebook index from RAG to async multi-agents. explainx.ai maps the 2025–2026 must-reads against the HN “prompt theatre” debate and CLAUDE.md minimalism.
- Are AI Labs "Pelicanmaxxing"? A 1,008-SVG Study Says Probably Not →
Simon Willison's "SVG of a pelican riding a bicycle" prompt has become AI's most famous informal benchmark, and the obvious suspicion is that labs quietly train on it. Dylan Castillo tested the hypothesis directly — generating 1,008 SVGs across 8 animals x 6 vehicles x 7 models and running a difficulty-adjusted regression. The result: no statistically significant pelican-specific or bicycle-specific boost at any lab.