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InnerSense – science-based daily mental care
Hacker NewsInnerSense – science-based daily mental care
We built InnerSense, an iOS app that addresses common mental health struggles like anxiety, burnout, and low self-esteem. Our approach is to get scientifically proven self-help practices from CBT and REBT and adapt them to the modern world. The app is entirely free at the moment. You can read more about InnerSense here: https://ikuznetsov.medium.com/innersense-daily-mental-care-p... Download app: https://apps.apple.com/id/app/innersense-mood-tracker-cbt/id... I will be glad to hear your feedback.
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TrustMRRFits verified-revenue profile · Strong signals: ios, apps · Missing: mobile apps, personal, entrepreneurs64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting56%56% 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 released47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: apple, apps · Missing: mac, agents, macos33%33% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue32%32% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
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