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I built a website to figure out how startups make money online
Hacker NewsI built a website to figure out how startups make money online
Hey HN, I previously used to read a newspaper, where they every Sunday used to have a tiny section where they feature an entrepreneur. I were obsessed about those sections and wanted to study what they were doing and how they launched their company. Therefore I have started RealNtrepreneur. I will interview founders that are making up to 20k/month, so you can apply the same tactics to scale your startup. //Fuad
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Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting92%92% 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: io · Missing: https docs, excited, just released64%64% 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 · Strong signals: new, tiny · Missing: mac, agents, macos63%63% 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, intuitive37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal37%37% 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, revenue21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: make money · Missing: web3, chat, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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