MacroVibe Refinement

The idea

Genre is a sales category. It was created to organize shelves and sell records. How a song feels is a different question. MacroVibe Refinement puts that question to a crowd: what happens when people sort music by feel instead of genre?

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 the totals become real playlists.

How it works

The interface is inspired by Macro Data Refinement from the Apple TV show Severance, with one core difference. The workers in the show sort numbers by feel. Numbers don’t have a feel. Music does.

Sixty-four cells float on a grid, each displaying a random four-character code. Hover over a cell and you hear a clip from an Audius track. No artist name. No track title. Only the audio.

Six bins sit at the bottom: Grit, Halo, Static, Heat, Brine, Vellum. Not genres, not moods. Texture words. You listen, you feel something, you drop the song where it feels right. Then you do it sixty-three more times.

All sixty-four tracks are playing in the background from the moment the session opens. There’s a single clock running and the audio aligns to it. Hovering doesn’t start a track. It tunes you in to wherever the track currently is. The effect is closer to flipping radio stations than hitting the skip button.

When you place the last song in a bin, the session ends with an Alignment Report. Per-bin bars show how close your choices were to the crowd consensus.

The playlists

Every sort is tallied. Each track belongs to whichever bin has the most votes, and a sync process pushes results to real playlists on a dedicated Audius account every twelve hours.

What you end up with is a set of playlists no single person curated. Each one has its own through-line, even though there’s no agreement between users about what fits where: Grit, Halo, Static, Heat, Brine, and Vellum.