Coroot – A Copilot for Application Performance Troubleshooting

Hacker News

Anton and Nik here - we're building an open-source observability tool that transforms telemetry data into actionable insights. While there are many excellent observability tools on the market, pinpointing the root cause of an incident often requires manual searching through a sea of metrics, logs, and traces. Coroot acts as a virtual assistant, conducting system audits just like an experienced engineer would: - It utilizes telemetry data collected through eBPF to construct a model of the distributed system and understand its topology. - It traverses the dependency graph and audits every relevant service to identify the root cause. Our journey has been long, but we've made great strides in learning how to build better models of distributed systems, and improving our agents to gather the right metrics. Coroot is an open-source product (Apache 2.0), so you can self-host it for free. We charge for a cloud version with AI-based Root Cause Analysis, RBAC, and premium support. Github repo: https://github.com/coroot/coroot Landing page: https://coroot.com Live demo: https://community-demo.coroot.com

Visit product View on Hacker NewsSep 21, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

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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.

Incorrect prediction on native model

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