Mpump – browser groovebox where grooves are shareable links

Hacker News

I built a groovebox for making loops: techno, house, acid, anything electronic that works in loops. No install, no account. The idea is simple: a groove is a URL. The full pattern, tempo, and sounds live in the link. You open it, hear it, change it, send it back different. Has drums, bass, synth, Euclidean rhythms, live jam sessions. Would love feedback, especially from people who make this kind of music. https://mpump.live Built with Claude Code. Source on GitHub (AGPL-3.0). https://github.com/gdamdam/mpump

Visit product View on Hacker NewsMar 31, 2026

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

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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 · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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