Tunes – Rust audio synthesis/playback (100x realtime, SIMD, GPU, WASM)

Hacker News

Hello everyone! I built Tunes by accident while building an audio engine for my own game engine. It's a "batteries included" approach to music composition, audio synthesis, and sample playback. I built tunes to solve my own issues; I wanted it to have a bunch of capabilities while being simple to use and relatively performant (what else is new lol). So I spent a lot of time trying to optimize things that probably didn't need it. I ended up with a kitchen sink full of fun tools for audio. I wanted to share this with everyone because I feel like it's in a good enough position to help other people with their own audio issues now. It's easy to use and performant enough for most use cases. I'd love to get feedback and discuss any parts of it with you guys. Thanks.

Visit product View on Hacker NewsNov 20, 2025

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2points

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

85%85% 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: ide, game engine, io · 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% 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

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

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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