non-text based problems
I don't know enough about the specifics of what you're doing to really give detailed suggestions, but I have been surprised by models like qwen3.5/3.6 giving reasonable results to questions about images -- like answering questions based on comparing a few images. For example:
- Which of these images were taken from the same place?
- Which of these images were taken in the AM and which in the PM?
- Is the third image more like the first or the second? Answer "1" or "2" only indicating which of the first two images it is most similar to.
- What sort of tags would you attach to these images? Provide a list of tags as JSON as the only response.
Those prompts actually worked for imagery from camera systems I have to deal with for my job -- which, frankly, shocked me. The AM/PM one in particular read timestamps in the image (i.e. did OCR automatically) and converted from a 24 hour clock to answer the question. I've implemented that in scripts with older computer vision tools (like tesseract) that needed a lot of hand holding; the fact that current models can just do it is sort of mind blowing to me...


Hmm. Maybe I'd make chilled barley tea with the toasted barley, and a simple chickpea salad with slices of bell pepper to accompany it -- or, alternatively, hummus with the bellpepper to dip in it. Would be good for hot weather.
If the weather's cold, maybe lentil and barley soup with the bellpepper added in as an extra ingredient along with any other veggies I have on hand that seem like they'd be good in a soup. (Edit: Maybe experiment with the Cajun "Holy Trinity" -- celery, bell pepper, onion -- as the base? I haven't tried that for lentil barley soup before, but might be interesting.)