Iteratively
Indie HackersThe best way to track your product analytics.
We help teams who rely on data to capture clean, consistent product analytics they can trust.
Share cardActual performance
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Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal49%49% 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, podcasting24%24% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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 · Missing: web3, chat, crypto20%20% predicted probability of success on BetaList, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive11%11% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Incorrect prediction on native model
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