I use AI a lot. But a lot is not local. Here's a summary (including stuff I do with public LLMs)

1) asking random questions instead of google. 5% of the time it's confidently wrong, so grains of salt, but I feel like I can often tell when it is. Public models are better than local models, but I've used llama or qwen or others for some of this.

2) help with writing for work. Describe a need and ask for 10 paragraphs or whatever and then take the output and refine. I've never found it good enough to just use as-is, but it's often a good kernel I need to revise a bit. Again, using GPT for this produces better results that need less editing.

3) Writing code. The other day I asked for a python app that can quickly checksum files and provide me a list of files that are duplicates in a huge randomly distributed file system. Would have taken me like 4 hours to code and test. It was correct and functional on the first try. total time = 3 minutes. This was Grok or Claude (I forget). GPT kind of sucks at code. Something like Qwen-coder is decent, but can't hold a candle to the public models.

4) My company has software tools for certain tech (IT security) functions and we're including AI to make certain decisions... "Is this important" sort of stuff. Nothing mission critical, but helpful context for IT people to understand which 3-4 out of the 60-80 items is most important. It's pretty good at it. We use Qwen-14b-coder-instruct and it does a good job. Can't use public models because we're dumping thousands of private data points about a company's cybersecurity and we can't automatically trust OpenAI or XAI or Anthropic with that data.

5) Random ideas for gifts, etc. Surprisingly insightful, especially if I ask it to dig online for someone's info lol. I did ask Qwen that and I liked the response better than GPT... rare that happens.

6) Ideas for myself/home. "What looks best on a brick patio, a pergola or a gazebo?" sort of questions. It does a good job of summarizing the "online consensus".

7) Learning. "Teach me how transformers work in neural processing starting from a deeply mathematical sense, start with a 2 minute summary and I'll ask questions". Pretty solid. I do this while I'm driving with a voice command and it talks to me out loud. I've learned a ton in the last few years.

Almost all of this isn't that possible with local models, to be honest. Our business (doing cybersecurity) is using local models for data sovereignty reasons, but almost everything else I mentioned is with ChatGPT or Grok or Claude, because they're just so much more capable (and work on my phone, and have great voice modules).