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Autometrics – open-source observability stack

Hacker News

Autometrics – open-source observability stack

Hi HN, Today we’re launching Autometrics 1.0, an open source observability stack complete with auto-instrumentation, data visualisation and rich alerting via a customizable Slackbot. It currently has language support for Rust, TypeScript, Python & Go. We built Autometrics as we think metrics are an underappreciated observability primitive https://autometrics.dev/blog/you-might-only-need-metrics-a-c... Using the framework as a foundation we built charting and alerting tooling around it, that is intuitive to use. A more comprehensive explanation of what’s included can be found here: https://autometrics.dev/blog/announcing-the-autometrics-open... Our docs can be found here: https://docs.autometrics.dev/ We would love to hear your feedback! - Mies and the rest of the Autometrics team

Visit productView on Hacker NewsDec 5, 2023
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, visual, 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.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
81%81% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews
37%37% 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
33%33% 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
21%21% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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