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Dropout. – The uniform of those who walked out and still won

Hacker News

Dropout. – The uniform of those who walked out and still won

I built dropout. – premium minimalist apparel for founders who took the path less traveled. The brand celebrates the rebels who defied the traditional system and still won. Steve Jobs. Mark Zuckerberg. Larry Ellison. You. Launching Black Friday. For those who know greatness doesn't need permission. https://dropout.lifestyle Would love feedback from this community. What's your dropout story?

Visit product View on Hacker NewsNov 28, 2025

Actual performance

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Did not reach leaderboard

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

52%52% 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 · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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

38%38% predicted probability of success on TrustMRR, 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

28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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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