A place for asynchronous book clubs

Hacker News

Hi there, I really enjoy reading and discussing books with other people. But sometimes it's hard to get together more than a couple of people to actually read and finish a book and contribute to meaningful discussion. I set up https://www.kookbooknook.com/ in an effort to solve that. It's a discourse forum that I'm building into a community for running async book clubs. To start, I'm running a book club on the book Release It! by Michael Nygard. I'm Steve. I'm a software engineer. I like books. Come read with me: https://www.kookbooknook.com/t/new-book-club-release-it-by-m... .

Visit product View on Hacker NewsApr 23, 2025

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Actual performance

2points

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Hacker NewsStrong engagement from HN community · Strong signals: 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.

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

62%62% 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

44%44% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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

20%20% 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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