In theory, it speaks for itself. In practice, this has been an evolving project and I believe my current automatic song end finder for fade outs is not as good as a previous generation that I built most of my collection with. You can edit each passage’s lead in / lead out points separately and it’s not a half bad editor, but it’s still clumsy and very time consuming.
If I were introducing this to a new music collection, I’d fire up an LLM agent, have it read the project documentation, tell it where the player (Pi) is on the network (give it ssh key access, and have the music files in-place on the Pi for it to find), and tell it to go through the database induction / setup process. That may take most of 24 hours on a collection with ~10,000 songs…
Once it’s setup, you should be able to define the kind of music you want to hear at particular times of day by choosing 5 to 8 representative songs, the “Program Director” will then queue up similar songs automatically and play them through.
I’ve just barely got the “multi-room” setup going, it has several rough edges, but once it gets synced up it appears to be working pretty well.
If you like to play with LLMs for coding, this is an interesting project to try them on. I find it’s right about at the threshold of what a model like Claude Opus 5 can handle / make forward progress with. If anything about how it’s working bothers you, feel free to ask your LLM to smooth those rough edges in ways that make sense to you, you can at least maintain your own branch and maybe we can merge together a “best of both” situation someday.
So, the music player rename migration is done - I don’t think too much broke in the process.
The source repo is here: https://github.com/MangoCats/Lempi
In theory, it speaks for itself. In practice, this has been an evolving project and I believe my current automatic song end finder for fade outs is not as good as a previous generation that I built most of my collection with. You can edit each passage’s lead in / lead out points separately and it’s not a half bad editor, but it’s still clumsy and very time consuming.
If I were introducing this to a new music collection, I’d fire up an LLM agent, have it read the project documentation, tell it where the player (Pi) is on the network (give it ssh key access, and have the music files in-place on the Pi for it to find), and tell it to go through the database induction / setup process. That may take most of 24 hours on a collection with ~10,000 songs…
Once it’s setup, you should be able to define the kind of music you want to hear at particular times of day by choosing 5 to 8 representative songs, the “Program Director” will then queue up similar songs automatically and play them through.
I’ve just barely got the “multi-room” setup going, it has several rough edges, but once it gets synced up it appears to be working pretty well.
If you like to play with LLMs for coding, this is an interesting project to try them on. I find it’s right about at the threshold of what a model like Claude Opus 5 can handle / make forward progress with. If anything about how it’s working bothers you, feel free to ask your LLM to smooth those rough edges in ways that make sense to you, you can at least maintain your own branch and maybe we can merge together a “best of both” situation someday.