How I Built AI-Powered Log Triage in Go (and Made It 100x Cheaper with Fingerprinting)
Dev.to / 6/16/2026
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Key Points
- The author built LogSense to perform AI root-cause analysis for production errors by letting users “drop in an API key,” avoiding dashboard overload and runaway LLM token costs.
- A key design principle is deduplication: “the same error 1000 times = 1 LLM call,” so repeated incidents share a single analysis.
- The system architecture (Go, Gin, RabbitMQ, Kubernetes) ingests logs via API, normalizes and fingerprints error signatures, groups duplicates within a time window, runs the LLM once per unique fingerprint, then fans out the RCA to all grouped events.
- Fingerprinting reduces both cost and noise by hashing stable aspects of errors (e.g., message and stack shape) after stripping unstable fields like timestamps, UUIDs, and dynamic IDs.
- The product is positioned as significantly cheaper and more reliable than naive AI log analysis pipelines that process each log line or batch independently, and early access is available via a waitlist.
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