Not OP but one example is that recent VL models are more than sufficient for analyzing your local photo albums/images for creating metadata / descriptions / captions to help better organize your library.
The easiest way to get started is probably to use something like Ollama and use the `qwen3-vl:8b` 4‑bit quantized model [1].
It's a good balance between accuracy and memory, though in my experience, it's slower than older model architectures such as Llava. Just be aware Qwen-VL tends to be a bit verbose [2], and you can’t really control that reliably with token limits - it'll just cut off abruptly. You can ask it to be more concise but it can be hit or miss.
What I often end up doing and I admit it's a bit ridiculous is letting Qwen-VL generate its full detailed output, and then passing that to a different LLM to summarize.
For me, receipt scanning and tagging documents and parts of speech in my personal notes. It's a lot of manual labour and I'd like to automate it if possible.
I use local models for auto complete in simple coding tasks, cli auto complete, formatter, grammarly replacement, translation (it/de/fr -> en), ocr, simple web research, dataset tagging, file sorting, email sorting, validating configs or creating boilerplates of well known tools and much more basically anything that I would have used the old mini models of OpenAI for.