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trackmyage
TrustMRRtrackmyage is a subscription web app for personal health tracking. It connects to a user's fitness watch and reads their meal photos and health-check results, then shows simple derived metrics (Body A
trackmyage is a subscription web app for personal health tracking. It connects to a user's fitness watch and reads their meal photos and health-check results, then shows simple derived metrics (Body Age and Aging Speed) and long-term trends. Sold as an annual subscription at trackmyage.com.
Share cardActual performance
Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting71%71% 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.
TrustMRRFits verified-revenue profile · Strong signals: personal, fitness · Missing: mobile apps, ios, entrepreneurs57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos50%50% 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, crypto40%40% predicted probability of success on BetaList, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue30%30% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Incorrect prediction on native model
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