A browser based binaural beats generator

Hacker News

This is a side project I've been working on for a week or so - a PWA that generates binaural beats entirely in the browser. I've been using binaural beats to help with sleep for over a year now, and have found them effective for concentration too - but the existing apps trend towards ad laden bloatware, with crappy UX. My goal with this was to create a super fast app that I love using, free from the burdens of ad ware and tracking. I built it with Astro, a sprinkling of React and Million.js - all the tones are generated client side with the Web Audio API, using a Media Session to allow for lock screen controls. It's just a toy really, scratching my own itch - but hopefully others find some use in it.

Visit product View on Hacker NewsOct 17, 2023

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

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

70%70% 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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% 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

18%18% 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.

Incorrect prediction on native model

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