GitHub · by Sutro
Turns human labels into calibrated Jev classifiers with GEPA.
Sutro’s library builds calibrated classifiers from human feedback. You label examples, GEPA optimizes the question, and what comes out is a Jev function with a known error rate. It is on PyPI.
Open the source
Vincent Wang-Maścianica
@vinnylarouge
I reverse-engineered a jev-like architecture given its type. You can find the repo here to train your own jevlikes: github.com/vinnylarouge/j…
GitHubResearch and data
Eric Zhang
@ekzhang1
Inspired by @typesafeai , here is a Jev-compatible public API to play with It runs a comparable open model (Qwen3.6-35B-A3B), and just uses SGLang radix cache to preserve the prefill reuse / really fast parallel systemone generation - 64 tasks in <1s. github.com/ekzhang/openje…
GitHubResearch and data
A small open decision model: state and typed questions in, probabilities out.
Kshetrajna Raghavan
57