ML condenses billions of logs into a tiny snapshot your LLM can debug
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…
ML condenses billions of logs into a tiny snapshot your LLM can debug launched on June 17, 2026 with 13 votes, #1,084 of 22,647 launches that month and more than 90% of that year's launches.
In plain words
Rocketgraph is an observability tool that compresses large volumes of production logs into concise snapshots suitable for AI-powered debugging. It fingerprints logs into patterns and uses machine learning to anomaly-score them by various features, helping developers quickly identify root causes like schema mismatches, connection issues, and unfamiliar error patterns. Built for teams using AI-generated code, it replaces manual dashboard checking and query writing with automated log analysis.
written from the facts on this page · September 2026
From the sources
Agent layer for observability. Contribute to Rocketgraph/rocketgraph development by creating an account on GitHub.
Self-hosted log clustering and streaming anomaly detection that drops in next to the observability stack you already run. Your monitoring tool tells you what you searched for . It rarely tells you what's unusual right now . Rocketgraph sits next to whatever you already pay for — Datadog, New Relic, Loki, CloudWatch, Sentry, ClickHouse — pulls a window of logs, mines structural templates, and flags the anomalous ones. It runs entirely inside your network. Your logs never leave your VPC. There's no SaaS tier to pay for. git clone https://github.com/Rocketgraph/rocketgraph cd rocketgraph/ml cp .env.example .env # fill in whichever sources you have docker compose up --build # →…from github.com
In the maker’s words, at launch
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…
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