Jev 分层路由把 token 开销砍掉 98%
开发者用 Jev 做反射层，把简单判断和路由从重型 LLM 剥离，声称速度提升 15000%、API 延迟约 40ms。
Most developers treat LLMs like a cure-all, running every tiny agent decision through a $15/M token model. Jev Engineering fixes this structural flaw by separating rapid routing from heavy reasoning. The operational leap: Speed: +15,000% Token Overhead: -98.4% API Latency: ~40ms The execution pipeline: System 2 (Heavy LLM) -> Deep Analysis & Code Jev Layer -> Filter, Score, & Gate Worker Agent -> Execute Action State -> Update Loop By offloading simple yes/no checks, state updates, and routing away from the core model, you stop paying for unnecessary generation tokens. Heavy models should only be triggered when true reasoning is required. Everything else belongs to the reflex layer.
这条是引用着另一条写的：x.com/bober_smart/status/2103581364134256863