A developer encountered unexpectedly high costs from Anthropic's Claude Code, with 60% of their $312 bill attributed to a single, unseen retry loop. The issue stemmed from an agent repeatedly invoking tool calls with excessive input tokens, a problem not visible in standard logs or the UI. The developer identified the root cause by shipping logs to R2 and querying with DuckDB, pinpointing an 'ad-report-summarizer' worker as the primary token consumer. They also discovered that Claude Code's --verbose flag, when piped to jq, can reveal the full tool input/output JSON for debugging, and suggested using KV counters for multi-agent loops to prevent similar cost overruns. AI
IMPACT Highlights potential for unexpected costs in LLM applications and provides debugging strategies for developers.
RANK_REASON Developer shares a technical post-mortem about optimizing costs and debugging a specific AI product.
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