A Jev-like vision wrapper turns image questions into typed choices
An independent Python example uses vision-model token probabilities to return typed decisions from images. Its webcam rates are author-reported, and accuracy remains untested.
The big change
What changed: A developer has applied the small-decision format associated with Jev to images using general vision models. The image is attached to each request, while a one-letter answer turns a visual question into a value software can handle.
Why it matters: Developers can change the visual criterion in text and receive a typed result without building a separate image classifier for each question. This example covers visible people, plants, scene setting and brightness; it does not establish how reliably those judgments transfer to other cameras or scenes.
What to watch: The practical decision is whether a chosen model and endpoint return the required alternative-token scores consistently enough for the task. Frame throughput and decision quality need to be measured together on representative images before a webcam result drives an action.
