# Sound Virality

- **Event:** [Built with Opus 4.6: a Claude Code hackathon](https://cerebralvalley.ai/e/claude-code-hackathon)
- **When:** Feb 10 at 12:00 PM – Feb 17 at 10:00 AM (EST)
- **Where:** Location TBA
- **Team:** [Daniel Merja](https://cerebralvalley.ai/u/danielmerja), [Sharon Chen](https://cerebralvalley.ai/u/sharon_shooky)
- **GitHub:** https://github.com/sychen23/music-virality-rater
- **Demo video:** https://youtube.com/shorts/HpvptMDJK1k
- **Gallery:** https://cerebralvalley.ai/e/claude-code-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/claude-code-hackathon/hackathon/gallery/187

A single viral moment on TikTok or Spotify can generate millions in streaming revenue, sync licensing deals, and touring demand — making the ability to predict a track's resonance before release one of the highest-leverage decisions in the music business.

Artists and music professionals have no scalable way to pressure-test a track's potential before release. Feedback today is gut-driven, unstructured, and trapped in small circles — making it nearly impossible to benchmark a song against its intended context.

SoundCheck is a music virality rater where artists upload tracks, select a target context (TikTok, Spotify, Radio, or Sync), and submit them for community evaluation. Raters score each track across four context-specific dimensions -- dimensions that change per context because what makes a song "good" depends on where it's going and what stage it’s at. A credit system keeps participation reciprocal: rating others earns credits to submit your own tracks.

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Markdown version of https://cerebralvalley.ai/e/claude-code-hackathon/hackathon/gallery/187. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt