AudioSort, a web app for sorting audio files

Hacker News

Hey HackerNews! I've been tossing around the idea of a granular audio sorting application for use in my own music and decided to finally make it. The codebase is a mess and it needs a cleanup pass, but I had too much fun playing around with it to keep to myself. AudioSort divides an audio buffer into chunks based on a window size, reduces the chunk into a single value for comparison, sorts the chunks, and then glues it all together again. It's fun to throw full songs into and sort, and you can get some really interesting textures. It was inspired by a concatenative synthesis plugin I discovered ages ago called SKataRT. https://forum.ircam.fr/projects/detail/skatart/ https://en.wikipedia.org/wiki/Concatenative_synthesis

Visit product View on Hacker NewsSep 20, 2026

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

77%77% 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 NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

48%48% 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: new, single, code · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

43%43% 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

34%34% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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