Dreamy - AI Dream Interpretation

TrustMRR

Dreamy is an AI-powered dream interpretation and journaling app that helps users record dreams, uncover hidden meanings, and spot patterns over time. It combines advanced AI analysis with dream statistics, soothing sleep music, and a community of dreamers to deepen insight into subconscious themes and improve sleep habits. Available on iOS and Android.

Visit product View on TrustMRRJul 31, 2024Mobile Apps Mobile App B2C

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Actual performance

59customers

$185MRR/mo

Did not reach leaderboard

Traction signals

MRR growth 30d-9.5%

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

85%85% 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: ios, users · Missing: mobile apps, personal, entrepreneurs

69%69% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos

61%61% 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

23%23% predicted probability of success on BetaList, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

21%21% 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

17%17% predicted probability of success on Acquire.com, 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

16%16% predicted probability of success on Hacker News, based on ML models trained on real launch data.

Incorrect prediction on native model

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