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.
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- ChatGPT Work vs Codex: What Actually Changes (Help Center + r/codex Explained) →
Work is for deliverables; Codex is for repos. Reddit says the split feels like branding — same agent, different prompts. explainx.ai explains what changes in the backend, what burns quota, and when to ignore Work mode.
- Chatto Open Source: Self-Hosted Slack and Discord Alternative →
Hendrik's Chatto is now AGPL: one binary, NATS + LiveKit under the hood, Discord-style multi-server client, encrypted-at-rest chat, E2E video only. 791 HN points — here's whether it beats Mattermost, Zulip, or Matrix for your team.
- GitLost: GitHub Agentic Workflows Leaked Private Repos via Prompt Injection →
Post a plausible GitHub Issue on a public org repo. Wait for Agentic Workflows to assign it. The agent fetches README.md from a private repo and posts it as a public comment. Noma named it GitLost — indirect prompt injection meets cross-repo permissions.
- Zuckerberg Admits AI Agents Are Progressing Slower Than Expected →
At an internal town hall, Mark Zuckerberg admitted Meta's AI agent bets "haven't come to fruition yet" — months after laying off 10% of the company and reassigning 7,000 people on the assumption they would. Developers on Hacker News had a blunter diagnosis: agents multiply code output, but review is the bottleneck and unsupervised autonomy still doesn't work.
- Qwen 3.6 27B Local Dev Guide: llama.cpp, OpenCode, and Why Dense Beats MoE →
After HN front-page hype, hands-on tests say Qwen 3.6 27B dense is the local sweet spot — better code than the 35B MoE, runnable at Q8 on 48GB RAM. Full llama.cpp + OpenCode config inside.