Stanford pipeline uses Jev to pick models and Opus 5.5 to run experiments
A Stanford professor's team built a system where Jev decides which models and experiments to try next while Opus 5.5 executes them, testing 70,000 ML models in an hour and discarding bad results while giving more compute to promising ones.
JEV + OPUS 5.5 system built by a Stanford professor can test 70,000 machine learning models in 1 hour the system takes a dataset, while Jev decides which models and experiments are worth trying and Opus 5.5 handles the execution. the first version required researchers to manually choose which models and experiments to run. that worked for small projects, but became painfully inefficient as the number of possible combinations grew. Jev decides what the system should try next. Opus actually builds and runs the experiment. a bad result gets discarded. a promising result triggers another experiment. and the strongest approach gets more compute. the system essentially turns machine learning research into an automated feedback loop. found a breakdown of the pipeline they built, and the way Jev and Opus work together is surprisingly clever. going to post how it works next, so stay tuned and show it to your friends so they know. would you let an AI decide which model your research team should use?
This post quotes another post: x.com/me_barnyx/status/2101976764779999381