It's a tool like any other. If you don't have any usecase for it, just don't use it.

I use it to summarize release notes and generate some minor descriptions for generic stuff in my TTRPG campaigns.

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.

playing dnd alone is pretty cool

I've used it to summarize long articles, news posts, or videos when the title/thumbnail looks interesting but I'm not sure if it's worth the 10+ minutes to read/watch.
There are other solutions, like a dedicated summarizer, but I've investigated into them and they only extract exact quotes from the original text, an LLM can also paraphrase making the summary a bit more informative IMO.
(For example, one article mentioned a quote from an expert talking about a company, the summarizer only extracted the quote and the flow of the summary made me believe the company said it, but the LLM properly stated the quote came from the expert)

This project https://github.com/goniszewski/grimoire has in it's road map a way to connect to an AI to summarize the bookmarks you make and generate at 3 tags.
I've seen the code, I don't remember what the exact status of the integration.

Also I have a few models dedicated for coding, so I've also asked a few pieces of code and configurations to just get started on a project, nothing too complicated.

I use the Continue VS Code plugin with Ollama to use a couple of different models (deepseek-coder-v2 & starcoder2) to recreate a local only Github Copilot type experience for coding. This is on an M1 Apple Silicon though. For autocomplete the generation needs to be pretty brisk - I'm not sure how that would go in a VM without a GPU.

https://github.com/hendkai/paperless_sort_low_quality_ollama let ai tag your paperless ngx files base on content.

Roleplay (text adventures), a (stupid but occasionally funny) dungeon master, translation and help with creativity. These are the use cases I found. If you don't need that, you might get rid of it.

I have a 4070 sitting around collecting dust that I got from a trade, I've been thinking about setting it up with whispr and TTS and having a way to talk to my house.

I have a couple of smart home integrations, mostly air conditioning, light switches, security, and doors.

What I would like would be to have a few speakers on the walls that can talk to my server where I can say something like, hey computer, turn on the lights in the dining room and the lights in the dining room would turn on without transmitting that information to Google or Amazon.

I use local AI for coding (more recently) and ML Photo storage facial recognition and security camera object detection (been using the later 2 for years now actually, don't want that kind of info out on someone else's cloud training on my images)

Wanting answers to things you don't want google to know that you don't know.

None

I use it for everything. I did move to ollama to podman and I replaced webUI with Alpaca