r/YoutubeMusic

Hey,

for those who still don't know Sonic Oracle: it's a music discovery tool for Tidal and Qobuz. You type in an artist, pick a depth (Essential, Balanced, or Adventurous), and it builds a permanent playlist of connected artists saved directly to your library. Not a station. Not an algorithm pushing what a platform wants you to hear. Real discoveries based on what listeners with similar taste enjoy.

Big round of updates this week. Here's what shipped:

My Discoveries is new. Your search history is now saved. Every discovery you've made, all in one place. Tap any to revisit it.

Click-to-discover lets you click any artist in your results to start a new search from them. Chain discoveries together until you land on something you never knew existed.

Track variety is improved. Playlists now feature different tracks each time, even for the same artist. Run the same search twice, get fresh songs both times.

Speed. Discovery results are near-instant. Repeat playlists create in about 1 second.

Deeper database. Coverage expanded to nearly 3 million artists and over 3 million albums.

Better niche results. Obscure seeds that previously returned too few matches now pull back stronger results.

K-pop, J-pop, C-pop. Full genre coverage for Asian pop including city pop, visual kei, and more.

Landing page refresh. Better mobile layout and updated social previews when sharing links.

More coming. As always, let me know what's working and what's not.

I built this because I personally needed it and nothing like it existed. Still a solo developer, still shipping improvements every week.

Free to try at https://sonicoracle.music/ Three playlists, no credit card needed.

Alessandro

They raised the price, yet the experience is as terrible as ever. Having to relaunch the app a dozen times a day is unbelievable

I got student pricing but even 9 bucks per month feels steep for that crappy experience

It keeps feeding you songs you already know over and over again, or ones that sound mostly the same:
"Listen Again", "Forgotten Favorites", "Quick Picks": the experience will be like this for a few hours until you're sick of it: [song you know], [song that sounds... just like the last one?].

Can we fix it? Well probably not as I do not have influence over Google, and I have no idea how a company worth trillions shipped something *this* bad.... buuut we can build our own variety!

First I need a test subject, I chose myself:
We will be downloading a list of tracks to use for this experiment; I chose my music playlist — 676 tracks in my case — for processing.
This will take hours depending on the size...
[...]
Now that we have them:
We will split the tracks into 5 second slices and run them all through a specialized music embedding model, a aMuQ-MuLan (mediocre for the sake of demonstration), that, in simple terms, projects every track into latent vector space.
With this data we can actually see which songs and genres cluster together.

Great! We have all this data, so what can we do with it? :
Firstly, we can now have queues that are actually diverse! Either fresh relative to what's been playing, or a gradual change across different genres if you'd prefer. Both are possible.
Secondly, ask for anything: "Romantic", "Chill techno". Because it works with natural language, you can even hook it up to your AI agent and have it manage things for you.
Let's say you've had a rough week and don't want any slow songs that will ruin your mood for the next few days, you've had enough classical music for today but your playlist is full of it, or you have 3 albums but don't want to be stuck with them.
Thirdly, actual discovery: not more of what you already have, but tracks you don't have yet that land close to your taste. Think of it like auto-expanding your queue or discovering on Spotify.
Fourthly, you can use the data however you want:
Compare it with friends, collect data on thousands of people and use machine learning to develop a whole new recommendation algorithm, so you never have to see [song you know], [song that sounds the same] again.

To demonstrate all of this, I have had the courtesy to create a live visualization of my (quite diverse, I'll have you know) library: