斯坦福团队用 Jev + Claude Code 做百亿级数据过滤
Jev 先做低成本初筛，只有边缘案例才交给 Claude，20 分钟处理超 1000 亿数据点。
HOLY SH*T, IT'S HARD TO BELIEVE This is completely mind-blowing A Stanford AI research team combined JEV with Claude Code to process more than 100 billion data points every 20 minutes The strategy behind it is ridiculously simple: JEV handles a fast, low-cost initial filtering pass. Claude is only brought in when non-trivial edge cases appear Instead of routing everything through a costly model: Raw data -> Claude -> astronomical costs $$$ The workflow transforms into: Raw data -> JEV filter -> edge cases -> Claude analysis Standard noise never touches the expensive model Claude only receives a minimal curated pile that genuinely warrants deeper reasoning The outcome: faster execution, lower costs, and drastically reduced compute burn Essentially, JEV pre-sorts the incoming mail so the expert only reviews critical letters Bookmark this. More details in the article below
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