Fake-Fast Detector
Indie HackersCalls the “fake fast” bluff in one click.
Because stakeholders believe pretty dashboards and “best practices,” while users believe load bars and broken scroll. Tools should side with users.
Share cardActual performance
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released83%83% predicted probability of success on Hacker News, 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 · Strong signals: users, calls · Missing: plus, platform, intuitive60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos33%33% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto23%23% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
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