MIT quant professor uses Jev to filter 12 billion financial events
Jev screens events in real time and flags suspicious ones, passing only the cases that need deeper investigation to Opus 5.5, so most events never reach the expensive reasoning layer.
an MIT quant professor built a Jev + Opus 5.5 system that can monitor 12 billion financial events every 13 min the system watches incoming data in real time, while Jev decides which events are suspicious and Opus 5.5 investigates the cases that actually need deeper reasoning. Jev filters billions of events and decides which ones deserve attention. Opus then examines the selected cases, connects the relevant data, and explains what might be happening. most events never reach the expensive reasoning layer. I found a breakdown of the pipeline they used, and the way Jev reduces the workload for Opus is surprisingly clever. going to post how the system works next, so stay tuned and send it to someone building AI infrastructure. would you trust an AI to decide which transactions deserve a human investigation?
This post quotes another post: x.com/me_barnyx/status/2101976764779999381