Beatsync – perfect audio sync across multiple devices

Hacker News

Hi HN! I made Beatsync, an open-source browser-based audio player that syncs audio with millisecond-level accuracy across many devices. Try it live right now: https://www.beatsync.gg/ The idea is that with no additional hardware, you can turn any group of devices into a full surround sound system. MacBook speakers are particularly good. Inspired by Network Time Protocol (NTP), I do clock synchronization over websockets and use the Web Audio API to keep audio latency under a few ms. You can also drag devices around a virtual grid to simulate spatial audio — it changes the volume of each device depending on its distance to a virtual listening source! I've been working on this project for the past couple of weeks. Would love to hear your thoughts and ideas!

Visit product View on Hacker NewsApr 29, 2025

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

81%81% 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 HuntOn track for Day 1 leaderboard · Strong signals: mac, open · Missing: agents, macos, agent

73%73% 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.

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

32%32% 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

28%28% 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

11%11% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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