I wrote a book on Site Reliability Engineering

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Hello Everyone! I finally published Site Reliability Engineering Tidbits on leanpub. https://leanpub.com/sretidbits This book is a collection of 28 chapters on SRE concepts such as observability, monitoring, Service Level Objectives (SLOs), alerting, resilience and debugging. This book aims to provide hands on examples of implementing a number of concepts described in Google's SRE books. It also describes how i've seen SRE concepts impact some of the organizations I've worked in. I previously announced this book here, but it was not on leanpub yet: https://news.ycombinator.com/item?id=30064570 Thank you for looking and I appreciate your feedback!

Visit product View on Hacker NewsSep 1, 2022

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

77%77% predicted probability of success on Indie Hackers, 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: ide, io · Missing: https docs, excited, just released

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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

Product HuntUnlikely to reach the leaderboard · Strong signals: google, new · Missing: mac, agents, macos

31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

14%14% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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