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Uses for local AI? (lemmy.mtate.me.uk)
submitted 1 month ago by [email protected] to c/[email protected]

Im using Ollama on my server with the WebUI. It has no GPU so its not quick to reply but not too slow either.

Im thinking about removing the VM as i just dont use it, are there any good uses or integrations into other apps that might convince me to keep it?

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[-] [email protected] 25 points 1 month ago

Think of LLMs like a stupid office worker. You wouldn't rely on them to make critical decisions, but they're valuable for tedious stuff.

For example, my calendar changed the way to enter new events breaking my workflow. Now I just type out a skeletal schedule and have LLM convert that into a .csv that I import.

I'm thinking of Ripping my CD collection again. I'm researching a way to use a LLM to tidy up the metadata.

I had a folder full of random stuff I've saved for years. Had a LLM organize and categorize it for me. I had to tweak the prompt enough that this was a medium difficulty task, but still way easier than doing it manually.

[-] [email protected] 3 points 1 month ago

for the metadata, LLMs may not prove so great. Use MusicBrainz Picard or Beets

[-] [email protected] 3 points 1 month ago

I'm thinking of Ripping my CD collection again. I'm researching a way to use a LLM to tidy up the metadata.

If you ever figure out how to use AI to determine the genre(s) of a song, let me know. Have been looking for something like that for quite a while.

[-] [email protected] 3 points 1 month ago

Nextcloud Recognize is supposed to do that, but I haven't tried it. You might try looking down that road.

[-] [email protected] 3 points 1 month ago

Thanks for the tip! I took a look and it seems like Recognize uses this: https://github.com/jordipons/musicnn

Last update was 4 years ago but will give it a try this weekend.

[-] [email protected] 2 points 1 month ago

Can you share some info on how you did that folder organization? Did you provide the AI with a list of files?

this post was submitted on 24 Jul 2024
53 points (90.8% liked)

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