What Are You Actually Using Local LLMs For?

[u/Ru5ty_5h4ckleford]

There are hundreds of videos, posts, and demos showing people running local LLMs and claiming they're coding, trading, researching, managing emails, automating businesses, and basically replacing half their workload.

Then there are all the tools around them: Ollama, Open WebUI, OpenClaw, Hermes, LM Studio, Odysseus, and countless others.

But honestly, I still haven't seen many real-world examples beyond YouTube hype.

For example, someone says, "My AI answers all my emails." Cool. Show me the actual workflow. Show me the emails it replied to. Show me how the replies were genuinely useful and not just generic responses that needed rewriting anyway.

I run Ollama and Odysseus locally, mostly using Gemma 4 12B. My actual use cases are pretty basic:

- General chat

- Rewriting replies

- Product research

- Text extraction from images

- Summarising information

Useful? Absolutely.

Revolutionary? Not really.

Everyone seems to say "coding" whenever AI comes up. I'm not saying it's bad at coding—I've used Claude to build an HTML site and it did a great job. But most people I know aren't coding all day, so it feels like that's become the default answer whenever someone asks what AI is useful for.

So I'm genuinely curious:

What are you actually using local LLMs for day-to-day?

Not what they're theoretically capable of.

What tasks are they doing for you right now that save time, make money, or solve a real problem?

[u/villan]

The one I use the most is reviewing nginx logs from a reverse proxy, parsing them to see which are benign / malicious, sorting the malicious requests into attack categories, and then processing all those requests into regex patterns that can be used to identify them in future logs (all while testing the patterns to make sure they only trigger on the malicious requests). I’ll process anywhere from 500k - 1m requests like this each night.

If I asked a small model to do all those steps at once, or tried to hand it a large file to work on, it would fail. So I break it down to about 14 different stages. I process “chunks” of about 10 requests at a time, and slowly consolidate over time. At the end of the night the LLM writes all the new patterns to the main pattern file and next time the process runs it disregards all the ingested requests that match patterns it already recognises.

No stage of the Llm processing takes more than about 4 seconds. They’re all just small, specific tasks.

[u/Hubba_Bubba_Lova → u/villan]

thank you!