Fi

First calendar app using health data from wearables (submitted to F24)

Hacker News

First calendar app using health data from wearables (submitted to F24)

My cofounders and I have developed the first calendar app that integrates health data from wearable devices to help you plan their schedules around you peak productivity times. We calculate your energy levels throughout the day using your sleep data, circadian rhythm, subjective feelings, and many other metrics that we'll be adding in the future! It’s now available on the App Store, Play Store, and even as a Chrome extension. [App Store]: https://apps.apple.com/us/app/lifestack-calendar-and-health/... [Play Store]: https://play.google.com/store/apps/details?id=com.demind.lif... [Chrome Extension]: https://chromewebstore.google.com/detail/lifestack-extension... We've also submitted it to F24! We hope you'll give it a try, and we're excited to hear your feedback!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%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.
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, google · Missing: mobile apps, ios, personal
61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% 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
28%28% 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: excited, io · Missing: https docs, just released, exist
26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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