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A parody app which shows how every ridiculous idea can be AI powered

Hacker News

A parody app which shows how every ridiculous idea can be AI powered

For the last year, everywhere I looked, I saw AI, even for minuscule stuff, it had AI slapped on it. So I joked around with my friends how the next big thing would be AI-powered toilet paper, and other similar ridiculous ideas. That's when I decided to make a simple satire app where every suggested idea, no matter how crazy, will "explain" how it can be AI-powered. I had fun making the app and then trying out random ideas with my friends just for laughs. If anyone wishes to try it, it is free to use on hyperai-frontend.vercel.app Any feedback is welcome :)

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Actual performance

3points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% 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.
Product HuntUnlikely to reach the leaderboard · Strong signals: plain · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released
26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
26%26% 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
14%14% 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.

Correct prediction on native model

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