Jev 用类型化决策替代 LLM 分支调用
作者称 Jev 比前沿模型快 193 倍、便宜 444 倍，并给出 700 条线索 40 秒 $0.09 的分类示例与四套落地模式。
someone finally fixed the expensive part a new AI called Jev was released only a few days ago. it doesn’t generate paragraphs - it makes typed decisions that software can use directly TypeSafe says Jev is up to 193.6× faster and 444.6× cheaper than frontier models in selected workflow tests the listed price is $0.042 per million input tokens. output tokens are free that changes the economics of agents. instead of spending a full LLM call on every yes-or-no branch, Jev can classify, route or score the entire batch one author-reported example: 700 leads → match / no match → 40 seconds → $0.09 business lift is still unverified, but the decision itself is measurable and easy to audit the setup: > define allowed outputs > batch the inputs > set a confidence threshold > execute high-confidence decisions > send uncertain cases to a human four patterns already map the idea: slop detector → score or null UI selector → choose a component Mac intent → choose an action lead matcher → score 700 leads the 28-second video follows: FIND → TYPE → BUILD → SHIP LLMs write and explain. Jev decides. software executes the full 9-step blueprint and all four patterns are in the article below ↓
这条是引用着另一条写的：x.com/mika_systems/status/2101686148338798610