I wrote a beginner SRE book, and it's free today

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Hello! I wrote an introductory SRE Book: Site Reliability Engineering Tidbits and it's free today! These are short fun chapters about SRE, monitoring, debugging, observability, and resiliency. 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. A couple chapters are hands on debugging exercises going through the process of debugging applications based on data. I'm excited because every chapter describes something that I've done in paying jobs, so it documents real life SRE in action at various size organizations, and not theoretical SRE concepts.

Visit product View on Hacker NewsMar 14, 2022

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

76%76% 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: excited, ide, io · Missing: https docs, just released, exist

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real life · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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