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Coroot – AI powered Root Cause Analysis using eBPF-based telemetry

Hacker News

Coroot – AI powered Root Cause Analysis using eBPF-based telemetry

Hey HN, We launched Coroot a while ago as an open source observability tool that collects complete telemetry using eBPF. Today we are adding a major new capability: Coroot enterprise now includes AI-powered Root Cause Analysis. When an incident happens (like an SLO violation), Coroot: - Automatically kicks off an RCA - Summarizes what went wrong in plain English - Suggests immediate fixes - Shows the full investigation using metrics, logs, traces, and profiles With most tools, the quality of root cause analysis depends on how well the system is instrumented. Coroot takes a different approach: it uses eBPF to collect all the critical signals automatically, even from uninstrumented or third party services. That gives the AI a much more complete picture to work with. We are keeping it simple and accessible at $1 per monitored CPU core per month. Our core tool is also completely open source at: https://github.com/coroot/coroot You can try it for free or view a demo with the link connected to this thread. We'd love to hear your thoughts!

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, open · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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