MacroVibe Refinement

The idea

Genre is useful for organizing music, but it doesn’t describe how most people actually experience a song. MacroVibe starts with a different question: what if we sorted music by feel instead?

The model is something like Twitch Plays Pokemon, but for curation. No single person decides where a song belongs. Everyone who participates drops songs into abstract bins based on gut feeling, and real playlists emerge from the aggregate.

How it works

The interface is inspired by Macro Data Refinement from Severance. Elements float on screen, and hovering over one plays a clip from an Audius track. All the songs play along a continuous looping timeline in the background, so moving between them feels like flipping radio stations. You never just get the beginning of a track.

At the bottom are six bins: Grit, Halo, Static, Heat, Brine, and Vellum. Not genres, not moods, just texture words that make you sort by feel. You listen, you feel something, you drop the song where it feels right. That’s it.

What comes out

Every sort is tallied in a database. Each song belongs to whichever bin has the most votes, and a sync process pushes results to real playlists on a dedicated Audius account. The playlists are a living curation experiment. As more people sort, songs shift between bins based on the majority opinion.

What you end up with is a set of playlists that no single person curated, organized around something more intuitive than genre: Grit, Halo, Static, Heat, Brine, and Vellum.