[IanCal]

Recommendation algorithms are, frankly, weird as a user. Something watches your tiny interactions and, with zero concept of anything happening, gets surprisingly good at predicting your next interactions.

It’d be like eating and having an alien with no concept of food or taste staring at how much your facial muscles change and making notes. Then recommending places to you based on the other people they’ve been studying.

> how is AI (or a human for that matter) supposed to know what “woodworking-appropriate music is”?

Well someone is going to be concentrating, but it’s a long period, with loud activity. maybe you’d guess at decently active music, not relying on careful complex listening, probably fairly steady rather than sudden dramatic changes.

Maybe it could save previous playlists and what you’ve said about them, or ask questions like “so like, really calm or techno or what?”. Adding just a bit of common sense makes an enormous difference with this kind of thing.

[tptacek]

They are weird. But stuff like this solves one of my great challenges in life, which is that I've got many tens of thousands of songs in my library but only manage to hear tiny repetitive epicycles of them. I don't want a tight rotation of the same 200 songs, and I don't literally want to randomly walk my whole library, and I don't want to allocate lots of time curating exactly what I'm going to listen to. LLMs are great at this.

(Actually I totally do want to allocate lots of time curating exactly what I'm going to listen to, but laying out playlists is its own hobby, and woodworking time is for making wood shoot across my basement when I pinch the saw blade, not for making mixtapes.)

[IanCal]

Oh I mean LLMs are the opposite, they’re not the same blind optimisation thing weirdly watching you with no core understanding. They’re great for this kind of thing.