I made a volumetric audio visualizer

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I'm developing Hyperstep[0], a spatial language for music production. I find using existing DAWs frustrating because they don't allow me to navigate and operate intuitively on the latent spaces behind my musical ideas. This is why I've decided to build my own set of "seeing tools".(Bret Victor)[1]. I'm also convinced that by framing music as processes and interactions in the 3D world, spatialization and mixing should become fairly pain-free. I'm still early in development and I would love to build this into an actual product that can be integrated into existing DAWs or even turn it into a musical framework itself for AR and VR experiences. If you're interested in working on it or if you simply want to know more, feel free to contact me. [0] https://github.com/a-sumo/hyperstep . [1] https://www.youtube.com/watch?v=klTjiXjqHrQ

Visit product View on Hacker NewsNov 1, 2022

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

75%75% 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: exist, existing, ide · Missing: https docs, excited, just released

69%69% 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: visual, using · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews

38%38% 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

13%13% 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.

Correct prediction on native model

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