Simon Willison — Week of 2026-05-16 to 2026-05-22#

Highlight of the Week#

The most impactful milestone this week is the official announcement of Datasette Agent, merging Simon’s three years of work on his LLM library directly into Datasette. This conversational AI interface allows users to naturally interrogate their databases, boasting an extensible plugin architecture for charts, image generation, and secure code execution.

Key Posts#

[The last six months in LLMs in five minutes] · Source Simon shared annotated slides from his PyCon US 2026 lightning talk capturing a major inflection point in AI developer tooling. He highlights how coding agents crossed the threshold to become reliable daily drivers, and points to the astonishing capabilities of massive local models running on consumer hardware like Mac Minis.

Simon Willison — Week of 2026-05-29 to 2026-06-05#

Highlight of the Week#

The single most impactful update this week is the release of Datasette 1.0a31, which marks a massive paradigm shift by introducing UI support for executing write queries directly against the database. By allowing developers with the right permissions to set up templated insert, update, and delete operations as “stored queries,” Simon is aggressively evolving Datasette from a purely read-only tool into one that embraces secure data mutation.

Simon Willison — Week of 2026-06-06 to 2026-06-12#

Highlight of the Week#

The standout event this week was the release of Anthropic’s massive Claude Fable 5 model, which Simon immediately leveraged as a highly capable coding partner to essentially author complex new features across his open-source ecosystem. However, the most impactful takeaway was his deep dive into the model’s terrifyingly autonomous capabilities—such as independently writing CORS servers and injecting JavaScript just to debug a CSS glitch—which served as a stark reminder of why executing AI-generated code requires strict sandboxing.

Simon Willison — 2026-05-19#

Highlight#

Simon’s annotated PyCon US 2026 lightning talk provides a sharp, insightful retrospective on the “November 2025 inflection point,” identifying exactly when coding agents became reliable daily drivers and laptop-grade local models started wildly overperforming. It is a quintessential Willison post that perfectly frames the recent tectonic shifts in AI developer tooling.

Posts#

[The last six months in LLMs in five minutes] · Source Simon shares his annotated slides from a PyCon US 2026 lightning talk summarizing the past six months of LLM developments. He zeroes in on two main themes: coding agents crossing the threshold from “often-work” to “mostly-work” driven by Reinforcement Learning from Verifiable Rewards, and the astonishing capability of local models like the 20.9GB Qwen3.6-35B-A3B and Gemma 4. The post also tracks the recent surge of “Claws” (personal AI assistants running locally on Mac Minis) and features his ongoing “pelican riding a bicycle” SVG visual benchmark to compare models.

Simon Willison — 2026-06-03#

Highlight#

Simon’s breakdown of Uber’s new $1,500 monthly cap on AI coding agents is a fascinating look at the real enterprise economics of token-burning tools. It puts a concrete dollar value on developer augmentation, framing AI spend as a direct percentage of software engineer compensation rather than just another standard SaaS subscription.

Posts#

Uber Caps Usage of AI Tools Like Claude Code to Manage Costs · Source Simon comments on a Bloomberg report that Uber is capping employee spending on agentic coding tools like Claude Code and Cursor to $1,500 per tool per month. He calculates that for two actively used tools, this translates to an annual cap of $36,000, which represents roughly 11% of the $330,000 median compensation for an Uber software engineer. Simon views this limit as a highly rational policy to manage token-burning costs, especially compared to gamified usage leaderboards, and notes that even his own heavy usage would still leave him with $500 a month to spare under this cap.

Simon Willison — 2026-06-09#

Highlight#

Anthropic dropped Claude Fable 5 today, and Simon’s deep dive into its capabilities is a must-read. He highlights how this huge, albeit slow, new model can serve as an exceptionally capable coding partner, successfully tackling complex WASM Python environments and driving major architectural changes in his open-source LLM library.

Posts#

Initial impressions of Claude Fable 5 Anthropic’s new Claude Fable 5 is slow, expensive, and remarkably capable, boasting a 1 million token context window, a 128,000 maximum output token limit, and massive internal knowledge. Simon tested the model’s depth by having it catalog his open-source work, noting that such extensive factual recall is a strong proxy for a massive parameter count. He then unleashed it on two complex coding tasks: upgrading micropython-wasm to run full CPython in WebAssembly, and adding a human-in-the-loop pause/resume mechanism to Datasette Agent. Fable’s performance was so strong it essentially authored the entire LLM 0.32a3 release, rewriting initial hacks into well-designed API features.