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DJ Claudify
I've been meaning to try this for ages, but kept being too lazy to bother. There's a website called Exportify that dumps your saved Spotify playlists into a csv. The setup is painless: approve access, no suspicious permissions, and you walk away with every track you've ever saved alongside metadata like timestamps and Spotify's own popularity scores.
I exported the lot. I don't really do dedicated playlists; my method is to encounter a song I like, save it on the spot, or, on rare occasions, find something decent on the recommended page. The result is a library in strict chronological order, where almost every track carries temporal and emotional baggage. Here's the song I was listening to during a breakup. That's the one that was playing when I first stepped off the plane in Scotland. Consistency and curation are for try-hards; if I don't like what's playing I queue something better, or jump down the list.
I fed the csv to Opus 4.7 and ChatGPT 5.5 Thinking and asked them to chart my mood and vibes over time. Both did remarkably well, well past reading tea leaves. Then I asked for recommendations, ideally things I hadn't heard before.
From Claude:
"176 tracks in your library have a Spotify popularity score of zero. 244 sit below 10. About a quarter of your library is essentially invisible to the algorithm. Names like Chikoi The Maid, Holy Fawn, Suave Punk, Caleb Bryant, Mokadelic show up there. Meanwhile your highest-popularity saves are exactly the global hits everyone agrees on (Blinding Lights, I Wanna Be Yours, Espresso, deja vu, Hey Ya, 505). That bimodal shape tells me you save the songs everyone agrees on and you save the songs almost no one knows, with a thinner middle than most people have."
Yeah Boi. I can out-hipster the hipsters, I listen to songs and genres you people have never even heard of. All while not predicating my personality around it, because I do listen to normal, human music. Sabrina Carpenter? You've heard of her.
Both delivered. Claude was a touch better, with something like a 50% hit rate by recommendation-to-save ratio. Better than any human I know. Better than Spotify's own recommendations, which is the part that surprised me. Spotify has spent over a decade and presumably a small country's GDP building a hybrid recommender that fuses collaborative filtering, raw audio analysis via convolutional nets, and NLP over reviews and metadata. They have every play, skip, save, and 30-second-bailout from half a billion users.
In other words, they try pretty hard.
And yet a model that has never heard a single waveform is beating them on my library, presumably because it's drawing on the entire written corpus of human music criticism: every RateYourMusic thread, every Pitchfork review, every breathless Reddit comment about a B-side. Collaborative filtering also has a pull toward the popular middle, which an LLM doesn't share, since it isn't trained on user-item interaction logs at all.
Easy chronology probably helps too. Spotify mostly sees a bag of vectors with timestamps; a language model can read your library as a story and notice when the vibe shifts.
You should try it. It helps to give honest, immediate feedback on what works and what doesn't. A sampler:
"Avril sounds like something they'd play in an aquarium that sprang a leak. It's definitely for someone, I'm not sure if that someone is me."
"Blink sounds like elevator music on ketamine. A maybe?"
"Loretta fucked my ear canal and got it pregnant. Instant add."
"Mona Lisa can stay in the museum."
"Vapour has a lot going on. Someone else can get it on, fucking hell, there are notes in there that are best appreciated by a cat."
"Catamaran sounds like it was recorded inside a toilet. From the adjacent stall. Miss."
"Mountains isn't bad, but it's for a very moody teen girl, or a middle aged woman going through a divorce/mid life crisis. Close but no cigar. Sigh. I'll add it anyway."
The whole experience was unreasonably pleasant, especially given that I'm not even sure Claude can ingest audio, or was meaningfully trained on music samples. The rest of you are getting mogged by a blind deaf entity that lives in a computer. I added something like 20 songs over the hour, which is a ridiculous number against my usual rate of about one a week.
Wow, an LLM told you you had unique and discerning taste? Incredible!
I had Claude write a simple set of scripts to interact with my Spotify library (fetch playlists, create playlists, look for songs) and set up a directory for it to keep track of different musical threads I asked it to help me explore. It updates files in the directories with my feedback on each playlist and creates a new one with the next batch of recommendations. The files let it maintain state and focus on particular themes and expand the "frontier" in directions I'm interested in. I've had some success with this setup.
Heh. Claude isn't sycophantic enough to do that. I declared that I have unique and discerning taste.
A sufficiently advanced intelligence can glaze without direct sycophancy.
It’s the secular guardian angel.
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