Timbre – An app that finds the best sounds for your sleep

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Hey all, I'm currently a solo iOS developer who just released my first app, Timbre, onto the App Store (iPhone only for now). Here's a demo to show you how it works: https://www.youtube.com/watch?v=ALoPAFb31Bk . Timbre originally came about because I wanted to build a sleep sounds & sleep tracker app. I had used Headspace, Calm, Pillow, etc. and figured that instead of paying $70 / year I could just build my own app and pay $100 / year (not a great plan in retrospect). As I started building Timbre, I realized that one of the biggest issues I had with these other apps is that I could never really see which sounds genuinely helped me sleep. With that in mind, I pivoted on my original idea and decided to really hone in on connecting sleep quality and sounds. Using a custom ML model, Apple's CoreML framework and HealthKit's sleep stage data, I made it so that Timbre could calculate a personal sleep quality score. Therefore, when you record a sleep session while listening to a sound, the app can rank the sounds you've listened to based on your sleep quality scores over time. Of course, the app also has a smart alarm, offline support, sleep analysis and more, but I'd love to get some feedback & suggestions from the demo video. Also feel free to ask me any questions and I'd be happy to answer them!

Visit product View on Hacker NewsOct 7, 2023

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

95%95% 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.

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

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, personal, apps · Missing: mobile apps, entrepreneurs, month

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: just released, ide, io · Missing: https docs, excited, exist

41%41% 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, intuitive

37%37% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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