Developer Finds 9 Critical Bugs That Made a Broken AI System Appear Functional

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A developer building AgentSelfEdit, an open-source AI tool designed to rewrite its own system prompts based on execution feedback, discovered 31 bugs in a single session, nine of which were fundamental flaws. The most dangerous error involved a statistical gate using p < 0.95 instead of the correct p < 0.05, meaning nearly all prompt changes were incorrectly approved as improvements. Other critical bugs included an A/B test that compared a prompt against itself, a scoring system that accepted any non-empty response as valid, and a CLI that communicated with a mock rather than a real language model. Several components silently failed or measured the wrong outputs entirely, making the system appear to function correctly while producing unreliable results. The developer documented all nine bugs to highlight how the most harmful failures in AI systems are those that generate plausible-looking output rather than obvious crashes.

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