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I built a database of successsful solopreneurs, it made $550 in 17 Days
Hacker NewsI built a database of successsful solopreneurs, it made $550 in 17 Days
I built OneManDB, a database of 1,000+ successful solopreneurs making $10K+/month. Why? I wanted to create a resource for indie hackers, founders, and makers to discover solopreneurs who are actually making money. What’s inside? Each entry includes their name, business, revenue, Twitter, and website links. Revenue so far: $550 in 17 days from early adopters.
Share cardActual performance
3points
1comments
Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting86%86% 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, 000 · Missing: https docs, excited, just released70%70% 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 HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos47%47% 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, intuitive32%32% 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, personal32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue · Missing: arr, mrr, profit26%26% 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, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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