What Is This?

A personal experiment in tracking how our musical tastes evolve over time.

I used to love rating my music in iTunes. I'd spend hours going through my library, assigning stars, and then filtering my collection based on those ratings. It was this perfect little system that let me rediscover songs and see how my mood affected what I wanted to hear.

But here's the thing about those ratings—they were never permanent. A song I rated 2 stars on a Tuesday morning might suddenly become a 5-star favorite on a Friday night. Music is incredibly fluid and tied to our emotions, memories, and experiences in ways that a single static rating just can't capture.

Since moving to Spotify, I realized that I had lost all of that. I could go back to managing my own music library, and at some point I hope I do, but even modern music apps seem to have a pretty mediocre rating systems. And I started thinking: what if we could do something much more interesting with the concept of rating music?

That's where this app comes in. I want to track how my relationship with songs changes over time. I've other ideas that extend the general concept: what if I could create playlists based on filters like "songs that went from 2 stars to 4 stars" or "tracks I loved in winter but hate in summer"?

I have so many ideas for where this could go. Maybe instead of just stars, we could use mood boards. Maybe we could correlate music taste changes with other factors—what we ate that day, how we were feeling, the weather, or what was happening in our lives.

Music is temporal and emotional and constantly shifting, just like we are. This app is my attempt to capture and play with that fluidity in a way that static ratings never could.

If you have any questions or recommendations, please feel free to send a message on Mastodon or Bluesky.

Currently working on:

  • • Real-time Spotify integration for rating currently playing tracks
  • • Historical rating tracking and visualization
  • • Smart playlist generation based on rating changes
  • • Mood and context correlation features