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Bookt – engaging and collaborative non-fiction reading

Hacker News

Bookt – engaging and collaborative non-fiction reading

Hey everyone, We're looking to change the approach to reading non-fiction. We've gamified and certified reading and now you read & engage with a community. I've created a community that will be reading 'The Almanack of Naval Ravikant'. Everything is free and you can sign up so long. The community will "commence" on Monday the 24th of April at 18:00 ET. The book will be read over 28 days, so you will read about 3.8% per day. https://bookt.app/join?id=6443debc19f3e8c66413c52c

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting
43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
42%42% 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
32%32% 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
20%20% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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