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Willpayforthis.com – Ideas people will pay for

Hacker News

Willpayforthis.com – Ideas people will pay for

Ah, there's a dumb easy hack to figure out what ideas people will pay for. Search "I'd pay for" on Twitter and you'll find hundreds of posts from people talking about pain points and products they'd pay for to solve them. Do this enough and you realize you have to filter through a lot of slop. slop. slop. I created willpayforthis.com to accumulate high signal, high quality posts and save you some time. I love thoughts from the community on how I can make it better, save you time, and help you work on the best ideas.

Visit product View on Hacker NewsFeb 22, 2025

Actual performance

41points

68comments

Made the leaderboard

Launch Intel predictions

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

85%85% 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.

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

61%61% 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 HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

59%59% 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

38%38% 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

18%18% 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

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.

Correct prediction on native model

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