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People Are Pissed Off and Ready to Own Their Shit (www.wired.com)
submitted 1 month ago by to c/technology@lemmy.world
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[–] 2 points 1 month ago (8 children)

This does sound very attractive. I’ve been trying to get a few family members of of Amazon Alexa’s and this is one of the biggest arguments they give me against it. I’d be curious to hear how far your evolution has come with this. I do have a rather large collection of music mostly from the olden days of piracy but would be interested in some doing some ‘updating’ to the library and the possibility of getting some family further away from Amazon.

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  • [–] 3 points 1 month ago (6 children)

    How "techy" are you? Do you have a couple of Raspberry Pis or similar laying around basically unused? Can you clone a git repo, open an LLM coding agent and get reasonable results out of the chat interface?

    The ultimate goal is to get this 100% AI/LLM free, but for now the LLMs are still doing a lot of the heavy lifting of configuring the target devices to install the player.

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  • [–] 1 point 1 month ago (1 child)

    The ultimate goal is to get this 100% AI/LLM free, but for now the LLMs are still doing a lot of the heavy lifting of configuring the target devices to install the player.

    why don't you use ansible for that, or even just some bash script? you could generate it even if you want

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  • [–] 1 point 1 month ago

    So far, every target is different - like the one with the 3.5" touch display built in, that has its own display driver module. Then there's Windoze vs Ubuntu vs Raspbian OS, and the Pi Zero 2W has a few different considerations than, say, a Pi 4 or 5, and basically every place I have deployed this so far has had something unique that needed custom attention. Once that's all worked out, sure, ansible or whatever, but customizing for each target isn't something "last year's tools" did as easily as the new agents.

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  • [–] 1 point 1 month ago* (last edited 1 month ago) (3 children)

    Techy enough. I self host a bunch of stuff, have a few servers and am more or less comfortable cloning a git repo. I actually currently have 2 rasp pi’s begging for a job.

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  • [–] 2 points 1 month ago (2 children)

    I'm just about to run my first test of the "echo mode" where each player plays its own local files, synchronized to one player's play list (a few issues of delay/clock rate synchronization to deal with in that approach). After that, I discovered I need to do a project rename (I've duplicated another project name from 2021, so... time for a different name - on 100K lines of stuff...) then remind me and I'll give you a pointer to the (new, renamed) repo.

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  • [–] 1 point 1 month ago (1 child)

    100k lines? are you sure you couldn't outsource more tasks to existing tools?

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  • [–] 1 point 1 month ago*

    45K of those lines are documents, specifications, testing results... It's an overbloated mess bull-headedly pursuing a couple of arbitrarty goals of mine that not many other people have pursued (use of Disk At Once (DAO) audio files with passages defined as sub-sets of the file, 71 dimensioal passage "flavor" determination for next track selection using the now offline AcousticBrainz algorithms, deployment on 5-6 so far various hardware/OS platforms adds a bit as well...)

    On the bright side: synchronization of 2 independent players is "coming together" - got some decent performance in the steady state case last night, now chasing the edge cases for faster convergence at startup, etc.

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  • [–] 2 points 1 month ago

    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.

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