I Used Claude Code to Debug a Nightmare (blendingbits.io)

A developer debugging an async-first Django app that streams LLM responses (via server-sent events) encountered a freeze: rapid navigation caused the whole server to hang until a restart. After four days and 1,427 lines of investigation — assisted by Claude Code — the root cause turned out to be simple but subtle: a security check performed at stream start grabbed a database connection and never released it for the stream’s lifetime. Rapidly opening ~10 streams consumed the entire pool and blocked subsequent requests. The three-word fix was to force the DB connection to close immediately after the check (i.e., don’t hold a sync DB handle for the duration of an async stream). The case highlights two big lessons for the AI/ML community. First, async/sync boundaries and resource lifetimes are debugging landmines — ensure sync libraries don’t inadvertently hold async resources (DB connections, threads) longer than needed. Second, AI-assisted debugging (Claude Code) can massively speed hypothesis testing, exhaustive checks, and documentation, but it needs careful orchestration: human intuition for steering, manual reproductions or reliable UI automation, and disciplined context engineering. LLMs still struggle with temporal reasoning, nuanced root-cause prioritization, and granular documentation, so human-in-the-loop workflows and better tooling (AI-native test drivers, clearer context artifacts) are essential to make agents truly effective in complex production debugging.