斯坦福团队把 400 亿数据点决策层换成 Jev，快 193 倍省 444 倍
原用 LLM 解读每条结果导致延迟和成本爆炸，改用 Jev 做结构化打分与路由后，生产环境提速 193 倍、降本 444 倍。
a Stanford team built a system that analyzes 40,000,000,000 data points every 15 minutes v1 used LLMs to interpret every result. it worked, but analyzing millions of records created crippling latency and massive inference costs. then they redesigned the decision layer around Jev. up to 193x faster and 444x cheaper in production. the model shouldn’t decide everything. in this setup: 40B data points → structured buffer → Jev router → typed score & filter (sub-15ms) → anomaly gate → cheapest capable model → frontier reasoner (top 0.001% only) → action the important part is that the decision layer doesn’t generate prose. it routes → scores → blocks → approves. so expensive models only get called when the data actually demands them. that’s the point of Jev Engineering: separate reasoning from decision-making, then make the decision layer measurable, cheap and fast. full breakdown in the article below ↓
这条是引用着另一条写的：「长文：如何搭好 Jev 驱动的 agent 技术栈」