I made a simple, open source, chromatic tuner mobile app

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I'm a musician by night, software developer by day, and one of the minor frustrations I've always had was trying to find a simple tuner app for my phone. Looking at both the iOS and Android app stores, you'll find a lot of the top tuner apps are rather bloated with ads or subscription callouts. I thought I'd create a dead-simple minimalistic mobile chromatic tuner the way I would like it - open the app and tune my guitar. My work is in web with NodeJS and React, so I chose React Native since it was the easiest to understand coming from React. Flutter was also an option since the company I work for has a team working in that, but I thought it would be a bit too much effort to learn Dart. I learned a bit more about React rendering with this project as well. The main issue I had was trying to have a React state variable be constantly updated with any frequencies detected from the mic input, but that ended up causing substantial slowdown due to so many rerenders, so I opted to use a React ref to maintain any detections, and then only update the React state when there was an actual note. I feel like sometimes the UI has trouble updating occasionally when my iPhone is in battery saving mode, which I guess might be related to throttling the audio sample rate somehow, though I'm not entirely sure. Friends and family haven't noticed this, so it may be due to my old 2016 iPhone SE :P. The apps you can find on the both the iOs App Store and Google Play (free!). https://apps.apple.com/ca/app/jons-tuner/id1641018281 https://play.google.com/store/apps/details?id=com.orsi.jonst... Would love any feedback!

Visit product View on Hacker NewsDec 31, 2022

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

94%94% 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

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

66%66% 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: occasional · Missing: plus, platform, intuitive

34%34% 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

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: audio · 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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