RFC 406i proposes a standard rejection link for low-effort AI pull requests
Original: A standard protocol to handle and discard low-effort, AI-Generated pull requests View original →
Hacker News discussion: https://news.ycombinator.com/item?id=47267947
Primary source: RFC 406i / 406.fail
Hacker News pushed a humorous but pointed document to the front page this week: RFC 406i, “The Rejection of Artificially Generated Slop.” The page proposes a standardized link that maintainers can drop into closed pull requests, bug reports, and forum threads when a submission looks like low-effort AI output. The joke format is exaggerated on purpose, but the complaint underneath it is very real.
What RFC 406i is reacting to
- Confident but unverified fixes that do not match the codebase.
- Hallucinated APIs, fake libraries, and generic boilerplate.
- Long explanations and polished tone that make review slower instead of easier.
The document argues that the cost is asymmetric. Generating a speculative AI patch is cheap; proving that it is wrong still consumes maintainer time. That is why the page frames rejection as boundary-setting rather than debate. Instead of asking reviewers to clean up machine output, it pushes the responsibility back to the submitter to read the actual code, reproduce the problem, and validate the change manually.
Even if you ignore the satire, the engineering lesson is useful. AI-assisted contribution is only credible when the human operator can explain the architecture, narrow the bug, and show evidence that the fix works. Otherwise, repositories turn into unpaid validation queues for generated text. That is the tension this HN thread captured so well: open source wants help, but not at the cost of replacing contribution with review spam.
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HN cared because this was not an abstract AI ethics fight; it was a maintainer workflow problem with licensing risk attached. SDL merged PR #15353 on April 15, adding an AGENTS.md that tells contributors not to use LLMs to generate code.
HN reacted because fake stars are no longer just platform spam; they distort how AI and LLM repos look credible. The thread converged on a practical answer: read commits, issues, code, and real usage instead of treating stars as proof.
Why it matters: open models rarely arrive with both giant context claims and deployable model splits. DeepSeek put hard numbers on the release with a 1M-context design, a 1.6T/49B Pro model, and a 284B/13B Flash variant.
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