A single function Jev-like wrapper for LLMs, including vision models
A developer blog shows a Jev-style logprob wrapper that scores webcam frames with text and vision LLMs.
A developer blog describes a small Python wrapper that turns LLM token log probabilities into fast structured answers, including for vision models. The prompt asks for a single option letter while requesting top logprobs, then maps those letters to choices, booleans, or scores. The author adds image attachments to a Jev-like JSON format, sends webcam JPEGs, and asks whether a person or plant is visible, whether the scene is indoors, and how bright it is. Locally, Gemma 4 12B on an RTX 3090 processed about one frame per second across three questions, while OpenAI gpt-6-luna reached about 0.2 FPS in the same setup.
Hacker News · AI22