A single function Jev-like wrapper for LLMs, including vision models

The author describes creating a wrapper for large language models that uses token probability analysis to quickly classify information with minimal output. The technique works by prompting the model to generate only a single token response while capturing log probabilities, and the author demonstrates it successfully with both local and cloud-based vision models to analyze webcam frames in real-time, achieving approximately 1 frame per second locally.