Stanford professor uses Jev to triage 8 billion experiments
A data science system replaced per-experiment LLM analysis with Jev deciding which experiments are worth re-examining, processing massive result sets in under 10 minutes and spotting conflicting conclusions.
a Stanford professor gave JEV access to 8.000.000.000 experiments then he built a data science system powered now by fresh released Opus 5.5 that can make sense of them in under 10 minutes. the system can spot conflicting results and automatically select which experiments deserve another round of analysis. the first version used LLMs to analyze each experiment individually. it worked, but processing billions of results this way made the pipeline painfully slow and expensive. then they moved the decision-making layer to Jev. instead of sending every result to an LLM, Jev first determines which experiments are actually worth investigating. I found a breakdown of the pipeline they used, and one part of the decision loop is much simpler than I expected. I’ll show you how it works next, so stay tuned if you want to see that. would you trust an AI to decide which experiments are worth running again?
This post quotes another post: x.com/me_barnyx/status/2101976764779999381