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SHOW HN: Good Books – book recommendations from successful people

Hacker News

SHOW HN: Good Books – book recommendations from successful people

Hey everyone Excited to launch a side project I've been working on: https://www.goodbooks.io Good Books is a curated collection of book recommendations from some of the most successful, influential and interesting people around the world. Let me know who you'd love book recommendations from and I'll add them to the list. This project started about 6-months ago when I started to keep track of all the books that I wanted to read and who recommended them. I then got (a little) obsessed with the project and ended up accumulating over 15,000 book recommendations from about 1,250 people... I built Good Books to share these book recommendations with the world, organising them into simple categories and industries so it's easy to find your next read. What's next for Good Books? - 100s more recommendations every month - Adding more interesting people (who would you like to see?) - Writing some curated recommendation lists - Planning a weekly newsletter with recommendations - Adding sources to book recommendations Thanks!

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist
75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
47%47% 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
43%43% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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