Irrespective of the tool itself, which feels like just another "some hidden prompt" tool (sorry author!), one of the things I can't stand about these tools (there was a recent movie recommendation one shared here with the same behaviour) is the almost cloyingly patronizing response noise:

- "Ah, great taste my friend."

- "Ah, great pick to start with."

- "Ah, a lovely choice..."

You're absolutely right! But I don't need you to tell me that. ;)

Music recommendation is such a hard problem. There are all these seemingly obvious relationships you can map between bands to create a big graph that looks good but that almost never captures what goes on when a human with deep music knowledge recommends music. Often the best recommendations have no obvious relationships to the band you like.

I played around with this tool a bit and didn't find it any better then other more traditional music discovery tools, that is to say not very effective.

For example, I entered John Zorn and was recommended a bunch of John Zorn's bands. I entered The Residents and got The Pixies :/

I think its more effective to click around on Music Brainz and Wikipedia.

To me it always feels like music classification and recommendation efforts, open source and commercial, are too focused on music distributed in albums and singles. In the long history of human music, albums have only been a fleeting moment, an App Store of music if you will. This would never recommend me concert performances (even those on YouTube), covers on YouTube, DJ sets on SoundCloud, or game soundtrack without an official release. Listenbrainz is the closest I've found because it just has Title and Artist, but even that can be fuzzy for covers and live performances. Maybe no one other than Google can build this right now.

My goto is https://www.music-map.com. Some of my best finds ever came from there.

I did try out the OP's recommender. It seems to misunderstand which genres strongly fit the band.

Guadalupe Plata is super-gritty mex rockabilly but the AI slotted it as delta blues.

Darla Farmer's only album is story based prog - like The Dear Hunter w/ a Diablo Swing Orchestra tone. AI called it a "hazy, intimate vibe".

All Them Witches scored better but the recommendations were all bands I know (Colour Haze, King Gizzard, King Buffalo).

Coincidentally, I was asking Claude today if something existed that could identify the key, chord progression, tempo, etc from a playlist of my favorite songs to see if there was any pattern that stood out so I could find similar songs with that vibe. Like a more music theory approach to discovering new songs versus the "people who liked this song, also liked these songs" way.

Even more coincidental, earlier today my wife was saying we should take our "Skylight Calendar" screen device that is hardwired into our wall with us when we move. I said I could just make a DIY one... and then I open HN and see the top post: https://news.ycombinator.com/item?id=47113728

Spooky.

Oh by the way, all of the "Open on Bandcamp" links I clicked were 404 pages.

Music discovery is a perennial AI tech application. The first one I followed was Firefly, out of MIT Media Lab around 1995. I think the startup was originally called 'Agents', which was a hot term in AI at the time. Thirty years ago.

https://en.wikipedia.org/wiki/Firefly_(website)

Weird. It hallucinated one for me, and recommended an album that doesn't exist. Flashbacks of 2023. If this is your app, you might want to consider adding a validation layer that performs a review before publishing an output.

Tried out a few prompts and the suggestions that came back didn't even seem in the ballpark of what I would've expected. Interesting idea nonetheless.

https://everynoise.com/ is still my favourite for discovering new artists, especially in fairly esoteric genres.

I entered "if tipper and lsdream had a child".

4 of the 5 links didn't even work. The other sounded nothing like what I described.

Thank you for this - I've just discovered something I'll be listening to all week. Is there any chance I could prompt it again with the bands I liked and didn't like from the list, possibly asking for something more refined?

Great job. This works really well.

> "lofi home recordings with no electronic elements and harmonically complex"

Yielded a bunch of things I would have expected but after 5-6 'dig deeper' clicks there were lots of interesting artists I hadn't heard of before.

> "contemporary classical with no electronic elements and small chamber arrangements"

Lots of interesting results I hadn't heard of before.

> "obscure indie bands associated with Olympia, WA formed in the 1980s"

Very good list. Does the time/location-bound results very well.

I think it's a good companion to sites like RYM as a middle ground between 'best of' ratings lists and personally curated lists.

When I put in a specific song by an artist that's not similar to the artist's typical output, it completely ignores the specific song.

Which is not surprising considering it's run by an LLM, but makes it not very useful as a music recommendation engine. There's already tons of places to ask "what artists are like this other artist"?

Cool app. One small complaint is the chatty tone of the recommender engine. In particular, I find it a bit disingenous to have an LLM tell me "Ah, I love <X>!".

EDIT: I also notice the recommendations are totally different when making the same query in a different session. I'm not sure if that's intentional? I expected at least some overlap with the previous time I asked.

I really want this to work! But maybe my requests are too niche. It also hallucinated an EP for me. And the bandcamp links never work?

Great idea though! I got inspired to listen to some stuff by it, even though it wasn't really what I wanted to find.

"Like Veruca Salt" give me a few bands which sound rather like Veruca Salt, not bad at all ...

does anyone actually want to discover music like this, by typing in what mood you're in or what kind of music you like?

if so, what about something more visual like movies and tv shows?

Recs weren’t bad!

I’d be curious about the data source here though. Custom curated? Relying on LLM World Knowledge with some prompting?

As someone who's big into UK Bass who finds new music mainly through a mix of Spotify, Beatport and Reddit, I found this recommender quite good actually! It seems to respond better to descriptions of the kind of music than to "Find tracks like these: <list>" which is what Spotify is good at.

Heads up that the site doesn’t resolve without www on the domain name. Just going to secondtrack.co won’t work.

undefined

FM Radio is the best method to discover music, especially for getting out of your bubble.