Think Book – Ask One Question a Day

Hacker News

One year ago, Think Newsletter ( https://thinknewsletter.com ) was created. The idea was simple: one question a day to spark new thoughts. One question led to another, and soon enough, we had a unique question for each day of the year. That's when I realized it would be perfect as a book! ( https://thinknewsletter.com/book ). The book is a beautiful artifact that people can carry and journal with — or even share with others. This is a collection of 366 thought-provoking questions. These questions are designed to challenge conventional thinking, generate ideas, and guide decision-making. I hope you love it!

Visit product View on Hacker NewsDec 26, 2024

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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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

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

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

17%17% 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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