Simon Willison — 2026-05-01#

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Simon demonstrates the power of mobile AI-assisted development by building a complete, multi-component tracking application entirely on his phone while camping using Claude Code for web. It’s a perfect example of chaining small, sharp tools—Python CLIs, Git scraping, and AI-generated static frontends—into a highly practical personal utility.

Posts#

[iNaturalist Sightings] · Source Simon wanted to consolidate and view his iNaturalist observations across multiple accounts, grouped by when and where they occurred. To solve this, he used Claude Code for web to write inaturalist-clumper, a Python CLI that groups sightings within a 2-hour and 5km radius. He then set up a Git scraping repository to regularly run the tool and generate a clumps.json file hosted via GitHub. Finally, he prompted an AI against his tools repository to build a static HTML frontend that fetches the CORS-friendly JSON and displays the sightings in a gallery with lazy-loaded thumbnails and full-size modal images.

The AI Reality Check: Agents, Economics, and Egos — 2026-05-03#

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Today’s discourse reveals a deepening fracture between the hype of AGI and the grueling reality of deployment and economics. While critics spotlight crumbling ROI and growing public backlash against generative models, builders are waking up to the massive, unglamorous infrastructure work required to force AI agents into enterprise workflows. The industry is shifting from a phase of speculative awe into a period of hard infrastructural reckoning and ideological defectors.

Simon Willison — 2026-05-04#

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Simon’s WASM-compiled Redis Array Playground is today’s standout, showcasing how quickly we can now spin up interactive sandboxes for in-flight C pull requests using AI agents like Claude Code.

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Redis Array Playground Salvatore Sanfilippo recently submitted a PR adding a new array data type to Redis. To try out the newly proposed commands, including a server-side ARGREP powered by the vendored TRE regex library, Simon utilized Claude Code to build an interactive WASM playground that runs a subset of Redis directly in the browser. The post also points to Salvatore’s own write-up on the AI-assisted development process behind the new array type.

Simon Willison — 2026-05-11#

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Today’s dispatches heavily focus on the macro consequences of the “agentic era” on the software industry, exploring everything from how coding agents are forcing massive corporate restructurings at GitLab to the stark mathematical reality of AI-generated codebase maintenance debt.

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GitLab Act 2 · Source Simon unpacks GitLab’s recent workforce reduction and structural flattening, which reorganizes their R&D into roughly 60 independent, empowered teams tailored for the agentic era. He highlights GitLab’s Jevons-paradox-inspired outlook: as AI agents collapse the cost and time of producing software, the overall market demand for software—and the builders who make it—will radically multiply. However, Simon pragmatically notes that GitLab has a strong financial incentive to project this optimism, given a recent 50% drop in their stock price and a business model heavily reliant on growing seat-based licenses.

Simon Willison — 2026-05-14#

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The single most interesting theme today is the changing paradigm of programming languages from being a permanent “lock-in” to fungible, replaceable assets, driven by AI coding agents. Simon highlights this shift through Mitchell Hashimoto’s commentary on Bun’s recent language rewrite and a real-world anecdote of agent-assisted mobile app migration.

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[Not so locked in any more] · Source Expanding on thoughts about modern software architecture, Simon shares an anecdote from a recent conference about a tech company that used coding agents to rewrite their legacy iPhone and Android apps into React Native. The development team wasn’t overly concerned about committing to React Native, reasoning that if it turned out to be the wrong choice, the lowered cost of agent-driven development means they could just port it back to native code later. This underscores a major industry shift where programming language choices are increasingly no longer the permanent lock-in they once were.

Simon Willison — 2026-05-15#

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Simon’s latest AI-assisted project is a lightweight QR code generator built entirely with the help of Claude. It perfectly highlights his ongoing exploration of “vibe-coding” to quickly spin up practical, small-scoped utilities for everyday tasks.

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[QR code generator] · Source Simon used Claude to write a custom tool for instantly generating QR codes. The utility gracefully handles standard text and URL inputs, and also features a dedicated mode for generating QR codes that connect mobile devices to WiFi networks. It serves as another practical demonstration of using generative AI to rapidly build, iterate, and ship helpful little tools.

Simon Willison — 2026-05-20#

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Simon takes a critical look at Google I/O’s Gemini Spark announcement, digging into the opaque “Antigravity” stack and questioning how Google plans to mitigate prompt injection risks for a tool with deep access to user data. This highlights the growing industry tension between powerful workspace AI agents and fundamental security vulnerabilities.

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[Google I/O, Gemini Spark, Antigravity] · Source Sticking to his rule of only reviewing generally available tools, Simon breaks down the announcement of Gemini Spark, Google’s new OpenClaw competitor that natively integrates with Workspace apps. He notes a strange FAQ detail claiming Spark runs on “Antigravity”—a moniker applied to a desktop app, a Go-based CLI, and a VS Code fork. Crucially, Simon questions whether Google’s isolated VM approach and Agent Gateway will actually be enough to prevent an “agent security challenger disaster” when handling sensitive data via prompt injection. He also highlights that Google is deprecating its open-source Gemini CLI on June 18th in favor of a closed-source Antigravity CLI.

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.