Pipetrics – platform for GitHub Actions performance tracking

Hacker News

I worked as DevOps Engineer at multiple companies of different sizes and almost everywhere at some point I had to work with CI/CD optimizations to make developers life better. The key problem I had was tracking down which pipeline was the problematic one and how could I see if my changes worked long-term. Some time ago I built first version of Pipetrics based on typescript, github action cron job and mongodb used as datasource in Grafana. This approach had a lot of limitations, especially in Grafana integration, thus I decided to rework the project and make it available to everyone as SaaS platform. Pipetrics in the form that I wanted to share with you today, is a first step in my new story. Today the platform is able to gather new and archival workflows from github and show it in the web app and Grafana dashboard. My plan for the next weeks is to implement advanced cost analysis with AI driven suggestions of optimizations (like using the cache or fixing its key).

Visit product View on Hacker NewsSep 26, 2025

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Actual performance

5points

1comments

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using · Missing: mac, agents, macos

89%89% 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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

47%47% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

19%19% 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.

Correct prediction on native model

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