'Rejection File' Take-Home Grades Candidates on Patches They Refused to Ship

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A proposed take-home exercise argues that hiring should weigh the AI-generated proposals a candidate rejects, not just the final code they submit. The Rejection File asks candidates to deliver a working patch alongside a rejections.json record of at least one rejected model proposal, backed by a repro command that fails if the rejection was correct. The format is designed to run in two to three hours on free-tier infrastructure and to separate engineers who verify AI output from those who simply relay it. Scoring weights rejection evidence most heavily at 30 percent, followed by patch correctness at 20 percent, with reproduction quality, scope discipline, budget discipline, and handoff notes making up the rest. The author frames the approach as a response to a recent debate over engineers acting as unverified intermediaries between requesters and AI tools.

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