For images I use it for things like helping draft initial alt text for images, extracting tables from screenshots, translating photos of signs in languages I don't speak - and then really fun stuff like "invent a recipe to recreate this plate of food" or "my CSS renders like this, what should I change?" or "How do you think I turn on this oven?" (in an Airbnb).
I've recently started using the share-screen feature provided for Gemini by https://aistudio.google.com/live when I'm reading academic papers and I want help understanding the math. I can say "What does this symbol with the squiggle above it?" out loud and Gemini will explain it for me - works really well.
Just last night I was digging around in my basement, pulling apart my furnace, showing pics of the inside of it, having GPT explain how it works and what I needed to do to fix it.
Getting it to fix a mower now. It's surfacing some good YouTube vids.
I don't have 100% of the "common sense" knowledge about every field, but good LLMs probably have ~80% of that "common sense" baked in. Which makes them better at interpreting incomplete information than I am.
A couple of examples: a post on some investment forum mentions DCA. A cooking recipe tells me "boil the pasta until done".
I absolutely buy that feeding in a few photos of dusty half-complete manual pages found near my water heater would provide enough context for it to answer questions usefully.
I agree, though personally I'm liking the "big thing" as well. R1 is able to one-shot a lot of work for me, churning away in the background while I do other things.
> Multi-modal
IMO this is still early days and less reliable. What are some of your daily use cases?
> Context lengths
This is the biggest thing IMO (Models remaining coherent at > 32k contexts)
And whatever improvements have caused models like Qwen2.5 to be able to write valid code reliably vs the GPT-4 and earlier days.
There are a whole lot of useful smaller niche projects HF like extracting vocals/drums/piano from music, etc