I've tried using Spotify and similar services that try to track your preferences but they're just, I don't know, boring. I much prefer the challenge of a human-picked DJ set.
I usually listen to dublab (los Angeles, cologne, and Barcelona) and nts1 (usually London) and nts2 (location rotates). They have 1 or 2 hour DJ sessions (live or recorded) and your hear some music that you normally wouldn't be exposed to and sometimes you hate it but usually not.
I've tried using Spotify and similar services that try to track your preferences but they're just, I don't know, boring. I much prefer the challenge of a human-picked DJ set.
The significant problem that AI faces in automatically curating something is that the input data is usually pretty terrible. It's based on either similarity of the thing being curated which doesn't work because people don't want things to be too similar or to dissimilar, or it's randomness which doesn't work because it's too discordant, or it's based on patterns in the data (people who listened to X listened to Y, so recommend Y to people who listen to X) which works but only if the listener's taste aligns with the majority. If you introduce multiple sources of patterns in the data you quickly lose any variation and things stop being interesting.
This is a hard problem. No one has ever really solved it, despite Spotify, Netflix, YouTube, etc investing hundreds of millions into the space. Humans are probably just too fickle to accept that an algorithm can choose for us. It lacks the social proof that a tastemaker like a DJ brings.
> the input data is usually pretty terrible. It's based on either similarity
I found luck just using a LLM to chat about my tastes, what I like, what kinds of songs I want to discover ... it does a good job and is able to also give me background.
I think what you're describing is what people working with recommender systems call serendipity. Maximizing serendipity, while maintaining relatively high relevance/recommendation success rate, is supposedly a pretty difficult problem to solve. I'm not sure if LLMs have changed that.
These are great, thank you so much for sharing the recommendations. I tuned in to NTS and casually just kept on listening for a very long time. If anyone else has good recommendations, I'm all ears. Thank you.
Check out mixes by Blackest Ever Black label (now defunct) from NTS and Berlin Community Radio, listening to them literally feels like a journey. Funny part, sometimes they use a contrasting tune to end a mix, which creates a feeling similar to movie credits roll in the end.
Spotify should have twitch-style, human hosted, radio shows (intentionally no video) with live chat. with full access to the catalog, it would easily bring back communities built around good music.
That would be good experimnent and could actually work.
I would love to try it however they would have to solve "global song availability" and "Sponsored songs only Stations".
But if they did try there is the chance of some niche communities forming.
It wouldn't even need to be live to begin with. A narrated playlist with a DJ and basic control functionality such as fading into songs or a voice over.
Not trivial but doable and I wonder why they never tried that.
It has been tried. I don't remember its name, but I remember that they have changed names at least once. It's a pretty obvious "app" for Spotify's API which they opened up a few years ago.
you have to do your own search and play, but some of the stuff by unknowns and famous artists giving back is profound, they KNOW when they hit it, all live, mostly acoustic and all useing musicians, no tape, no sequencers.
listen to one such performance, and maybe you dont need anything else for a week.
I usually listen to dublab (los Angeles, cologne, and Barcelona) and nts1 (usually London) and nts2 (location rotates). They have 1 or 2 hour DJ sessions (live or recorded) and your hear some music that you normally wouldn't be exposed to and sometimes you hate it but usually not.