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SLO-Tracker, an open-source tool to track SLO and Error budget

Hacker News

SLO-Tracker, an open-source tool to track SLO and Error budget

Hey all, We are a small team and always struggled to track our product SLO's and Error budgets. So we wrote a small tool to track our product SLO, Error-budget, False positive incidents, and SLO burning rate. It also provides some analytics into how the team might have spent their error budget over time. This tool saved us a lot of time which we would have otherwise spent in aggregating SLO metrics from multiple places. Hope this will be helpful for some. Currently integration with Prometheus, Pingdom, Grafana and Newrelic are supported. We are planning to add a few more integrations in the near future. We'll be happy to get your feedback, suggestions and patches to make this a better tool for the community :) Github repo: https://github.com/roshan8/slo-tracker Blog post: https://dev.to/squadcast/introducing-our-open-source-slo-tracker-a-simple-tool-to-track-slos-and-error-budget-4dp

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
72%72% 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.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
42%42% 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, io · Missing: https docs, excited, just released
41%41% 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
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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.

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