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Jamfound – Democratizing Music Collaboration Through Voting

Hacker News

Jamfound – Democratizing Music Collaboration Through Voting

As a musician, I was frustrated by online collaboration tools that either had no curation (everything gets mixed together) or were too restrictive (only the creator decides). So I built Jamfound: a democratic music collaboration platform where the community decides what sounds good. Here's how it works: 1. Someone uploads a base track (max 30 seconds, WAV) 2. Musicians contribute stems (bass, drums, vocals, etc.) 3. The community votes on their favorite contributions 4. The winning stems are automatically mixed into the final track It's like a musical version of Reddit's upvoting system, but for audio. Built with Flask + React, supports high-quality WAV files, includes BPM detection and automatic mixing. Try it: https://jamfound.com Code: https://github.com/Jamfound/Jamfound The voting system prevents the chaos of endless revisions while keeping the collaborative spirit alive.

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Actual performance

3points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
80%80% 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.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
67%67% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, 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
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
31%31% 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
15%15% 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
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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