How Software Fails-A book about complex system failures(sample chapter)

Hacker News

Based on Richard Cook's research on complex system failures, I wrote a book exploring why disasters like Knight Capital's $440M loss happen when every component functions correctly. Free sample chapters: https://leanpub.com/how-software-fails The book explores patterns behind complex system failures—from cosmic rays flipping bits in voting machines to the Therac-25 radiation overdoses. Key insight: traditional "root cause analysis" fundamentally misunderstands how these systems actually fail—they emerge from interactions between components that were individually functioning correctly. Feel free to grab the sample chapter and give it a try.

Visit product View on Hacker NewsOct 23, 2025

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Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

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

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

34%34% 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

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

18%18% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

15%15% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

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