gogoout

You don’t need classifier to filter noise. The noise is the same patterns repeated many times. I’ve got a tool does that to break down big diff and then feed to llm for grouping. https://github.com/thepartly/differential

Yep, this is the architecture design from day 1. The differential-engine crate is what you looking for, the packed TUI is just a frontend render. Check here: https://github.com/thepartly/differential/tree/main/crates/e...

Is this the reverse of LLM? Ie, "what's the capital of France?" LLM picks from "Paris" 99.9% / "London" 0.001%, LLM then with some randomness output you "Paris". For Jev, you ask it to give probability of a set of answers "what's the capital of France?" choose from answers (Paris/London), it then gives you (99.9%/0.001%)