Open-source code for Software Mistakes and Tradeoffs

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Some time ago, we published a book that focuses on mistakes, tradeoffs, and decisions that every software engineer needs to make in their day-to-day job. More info in this HN post: https://news.ycombinator.com/item?id=27842632 Today, I am glad to announce that code from this book is open-source and free and can be accessed at: https://github.com/tomekl007/manning_software_mistakes_and_t... It demonstrates the approach and some of the problems that the book is trying to tackle (not all problems are code-based). Feel free to fork, contributed, browse or create questions/issues :)

Visit product View on Hacker NewsMay 25, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

80%80% 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 · Strong signals: mistakes · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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

26%26% 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

16%16% 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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