LLM Author Simon Willison: Using AI to Triage AI-Assisted PRs, Treating Them as Bug Reports with Attached Examples
Simon Willison, author of the open-source tool llm, shares his approach in the v0.37 release notes for handling a flood of AI-assisted pull requests: using Opus 5.5 or GPT-6 Astra for triage screening, and cognitively reframing these PRs as bug reports with attached examples—sometimes merging them directly, sometimes rewriting the fix himself.
Many open source maintainers complain about the influx of AI-assisted pull requests (PRs): large language models are generating code submissions that flood repositories, significantly increasing the review burden. Simon Willison, author of the open-source tool llm, discusses his practical experience with this issue in the release notes for version 0.37—he states that llm is the project most affected by this trend, but he does not consider it a problem.
His response operates on two levels. On the tooling level, he uses Opus 5.5 or GPT-6 Astra to assist with triage, sifting through numerous submissions to identify the most valuable fixes. On the cognitive level, he no longer views AI-assisted PRs as code submissions requiring line-by-line review; instead, he treats them as bug reports with attached examples—sometimes landing them directly, sometimes implementing his own fix.
This release includes fixes from 12 contributors, almost all of which were AI-assisted. Willison notes that the review burden was far less than he had anticipated.