YDrip – An open source water meter that detects leaks

Hacker News

Hi HN, I'm developing an open-source water meter for Home Assistant and thought the HN community might be interested. I've been using some version of it after a few costly toilet leaks that my city meter missed. Recently, I decided to polish it up so others could use it. It might also function as a gas meter reader ( https://youtu.be/9Dso4EBAoiE ), but this is still experimental. The device runs on 3 AA batteries and utilizes an ESP32-S3. It works by measuring the spinning magnet inside the brass housing. It stays in deep sleep until it needs to transmit usage or leak information. It's not an ideal CPU for battery-powered devices, but I chose it because it has a lot of support in the home automation community. To compensate for the power-hungry CPU, I am handling rotation counting and leak detection in programmable hardware. Currently, it consumes 50-75uA while the ESP32 is sleeping. The programmable hardware makes it an interesting platform for other use cases like electricity meter monitoring. I'm planning on selling the hardware and potentially offering a hosted solution for those who don't want to set up Home Assistant if there is demand. There is still a lot to do, like designing a waterproof case and completing the software. If you're interested in the details, I have a project log on Hackaday. Feedback, feature requests, and contributors are always welcome. Website: https://y-drip.com Github: https://github.com/YDrip Short demo: https://youtu.be/7eXRLN7OWA0

Visit product View on Hacker NewsSep 27, 2023

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

76%76% 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.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: using, open · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

12%12% 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, crypto

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

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