Launch Intel
Ritual75

Ritual75

TrustMRR

Ritual75 is a 75-day discipline reset app designed to help you build consistency, track your daily rituals, and become the strongest version of yourself. Log your workouts, water intake, steps, readin

Ritual75 is a 75-day discipline reset app designed to help you build consistency, track your daily rituals, and become the strongest version of yourself. Log your workouts, water intake, steps, reading, progress photos, and reflections — all in one simple challenge tracker.

Visit productView on TrustMRRHealth & FitnessMobile AppB2C
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Actual performance

51customers
$236MRR/mo
Did not reach leaderboard

Traction signals

MRR growth 30d-46.2%

Launch Intel predictions

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TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
65%65% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
15%15% 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, crypto
14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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