Stanford team sorts 75 billion data points every 11 minutes with Jev and Claude Code
Jev makes the cheap relevance calls first and filters out noise, sending only ambiguous edge cases to Claude for reasoning, which cuts context and call costs.
this is the kind of architecture that makes current AI agents look primitive a Stanford team is sorting 75 billion data points every 11 minutes by pairing JEV with Claude Code they don't dump the entire firehose into Claude and ask a frontier model to think about every single item JEV reads everything first and handles the cheap decisions: relevant or noise, normal or suspicious, resolved or worth escalating the obvious cases are handled immediately. only the ambiguous edge cases reach Claude, where the expensive reasoning is actually useful JEV reacts. Claude reasons. that simple split changes the economics of the whole system less context sent to Claude, fewer expensive calls, faster decisions, and a pipeline that can operate at a scale where using one giant model for everything would be absurd most people are still trying to build agents with one brain doing every job this team gave the agent a fast brain for instinct and a powerful brain for judgment 75 billion data points every 11 minutes and the smartest model only wakes up when something is genuinely difficult
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