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The raw truth about self-publishing first technical book

Hacker News

The raw truth about self-publishing first technical book

I finally wrote about my experience of self-publishing a software architecture book. It took 850 hours, two mental breakdowns, and taught me a lot about what really happens when you write a tech book. I wrote about everything: - Why I picked self-publishing - How I set the price - What worked and what didn't - Real numbers and time spent - The whole process from start to finish If you are thinking about writing a book, this might help you avoid some of my mistakes. Feel free to ask questions here, I will try to answer all.

Visit product View on Hacker NewsJan 18, 2025

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

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

71%71% 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 · Missing: mac, agents, macos

44%44% 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

41%41% 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.

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

13%13% 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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