Drift, an open source HRV reader

Hacker News

It all started with me wanting to measure my HRV and I tried many different apps but wasn't really impressed, either they came with an expensive subscription or they had a very cluttered interface, and the PPG (photoplethysmography (using the camera)) wasn't that accurate IMHO. A wearable like a smartring or a wristwatch would probably been better but I didn't wanna buy one... So I started to build my own and it has been an interesting experience, I have been reading a lot about HRV, RR intervals and how to use the baevsky method for figuring out a stress index, fascinating reading. Drift helps you understand your current capacity by looking at BPM, HRV, SI and amount of sleep per night. Without a wearable one have to manually start tracking sleep but it has a smart feature and will stop tracking after first unlock in the morning (it waits 2 hours for late night scrollers to make sure you're sleeping) I didn't find any good open source apps for this so hopefully Drift gets some traction for the ones interested. I'm a strong believer in OSS and I think users will like the openness of being able to see what's going on, and the convenience of getting it at the Play Store for a low price (no subscriptions or hidden fees). GitHub (MIT open source): https://github.com/cladam/drift Play Store: https://play.google.com/store/apps/details?id=com.ilseon.dri... Thanks for checking it out.

Visit product View on Hacker NewsJan 27, 2026

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

87%87% 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: google, apps, user · Missing: mac, agents, macos

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, google, users · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

46%46% 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 · Strong signals: interface, users · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

20%20% 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.

Correct prediction on native model

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