Mastodawn

spotify isn’t really a discovery tool anymore — it’s a taste feedback loop

once it “learns” you, it mostly just serves safer, adjacent versions of what you already like

the best music i’ve found lately has come from doing the opposite:
digging through labels, bandcamp, forums, and actual human recommendations

slower, messier, way better results

i tried to map out some of these non-algorithm paths here:
https://mycelm.com/music-discovery-without-spotify/

how are people here finding new music these days?

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eishiya

@the_bogolepov I never got into music streaming so my ways of discovering music wouldn't change if they all disappeared tomorrow:
- friends' recommendations
- digging through label and artist catalogues and sampler albums (which has gotten easier thanks to sites like Bandcamp)
- paying attention to music credits in films, games, etc
- (rare) Reading about genres and related bands on Wikipedia (books would also be an option, but I'm not that interested)
- (very rare) physical store browsing

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eishiya 6d ago

@the_bogolepov In the 00s, people used to make little websites where they shared MP3s on a rotating basis. The downloads were illegal, but the highly visual bespoke layouts reflecting the band's aesthetic, 2-3 songs to try, and links to similar rotation sites made it a fun way to spend an afternoon, and I found more than a few great bands that way. The temporary/rotation and link aspects made it feel like exploration, made it memorable. Magazines were never as enjoyable (or relevant) for me.

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gene sophia bogolepov 5d ago

@eishiya thank you very much for sharing! And yes, I remember the excitement of the early internet days when artists created extremely cool websites that were feeling very special! 😌 It was kind of a ritual to go and check the sites of fave bands regularly waiting for new snippets, photos or tour diaries - whatever, it was just so much fun to do!

I’m trying to recreate this feeling these days, that’s why I’m questioning current systems and trying to find a solution at least on a local scale