Free AI Eval Endpoints Are Probes, Not Production Gates, Developers Warned

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Software developers are being cautioned against treating free AI model endpoints as reliable production-readiness checks, as passing evaluations on scratch machines does not guarantee the same results on real deployment environments. The core issue, described as an 'eval-transfer bug,' arises when teams measure agent performance on one machine but draw conclusions applicable to another. Factors such as unpinned model IDs, shared scratch servers with leftover files, and inconsistent tool schema hashes can silently corrupt evaluation results. Experts recommend freezing a precise 'eval identity' — including the model endpoint, server image, worktree path, and tool schema hash — before running any replay. Additionally, free compute time is better spent testing failure cases like timeouts and malformed responses rather than polished demo scenarios.

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