Alternatives
Products that do what log.fail does
Millions of logs. One clear summary.
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Hi HN, I'm Robel. I built LogClaw because I was tired of paying for Datadog and still waking up to pages that said "something is wrong" with no context. LogClaw is an open-source log intelligence platform that runs on Kubernetes. It ingests logs via OpenTelemetry and detects anomalies using signal-based composite scoring — not simple threshold alerting. The system extracts 8 failure-type signals (OOM, crashes, resource exhaustion, dependency failures, DB deadlocks, timeouts, connection errors, auth failures), combines them with statistical z-score analysis, blast radius, error velocity, and…
Mar 2026 · logclaw.ai
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We built Gonzo to make log analysis faster and friendlier in the terminal. Think of it like k9s for logs — a TUI that can ingest JSON, text, or OpenTelemetry (OTLP) logs, highlight and boil up patterns, and even run AI models locally or via API to summarize logs. We’re still iterating, so ideas and contributions are welcome!
2025 · github.com
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For more background and technical details, I wrote this up as well: https://dmitryfrank.com/projects/nerdlog/article
2025 · github.com
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Hey HN! We're excited to introduce Logwise, our new AI-powered log analysis tool. (built by two devs who hate logs) Product page: https://logwise.framer.website/ Logwise makes debugging and incident response faster for developers. It uses natural language processing to automatically parse log data, surface insights, and detect anomalies. We built Logwise to eliminate the manual sifting of log analysis. Key features: - Search logs in plain English - no complex queries needed - Auto-generated alerts highlight potential issues - Contextual debugging advice speeds incident…
2023 · logwise.framer.website
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Hi HN, I'm Kaushik, and I built Rocketgraph. I believe that while other spaces have caught up to the AI wave, the observability space is still lagging behind, using the same tools and dashboards that we use to analyse logs from human-written code. But now the code is written and debugged by AI, so we need to rethink how we do observability where the observer itself is an AI. The problem that I run into is when an alert fires, I have to manually check the Grafana dashboards and write LogQL queries, which is pretty much like greping. But production usually breaks due to a schema mismatch, or a…
Jun 2026 · github.com
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2015 · github.com
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2021 · changelogs.gallery
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