Developer Used Claude AI to Cut 8,400 Weekly Errors Down to 11 Real Bugs

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A software developer built a 200-line Python pipeline that feeds structured error data and repository context into Claude Code to automatically triage production issues. Their error tracker was generating around 8,400 events per week across roughly 340 distinct issue groups, far exceeding what any engineer could manually review. The top errors by volume were largely noise — bot traffic, browser warnings, and user-aborted network requests — while genuine bugs were buried deep in the list. The AI agent evaluated each error cluster and returned a verdict, flagging 11 real bugs that had gone unnoticed for months, including a null dereference tied to a 2024 database schema change. The approach aimed not to fix bugs automatically, but to replicate the routine four-minute human triage pass at scale, so engineers only spend time on issues that truly matter.

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