JEV vai melhorar seu Claude Code em 10x (Veja como)
Rafael Voss explains TypeSafe AI's Jev model as a fast, low-cost System 1 decision engine. He contrasts it with generative System 2 LLMs and outlines three tiers of practical usage: LLM/skill routing, discrete data and email classification, and agentic computer use.
Original by Rafa Voss | IA na PráticaGetting startedBeginner14 min 47 secPublished
Jev functions as a System 1 decision model returning binary choices, discrete category selections, or numeric scores without generating freeform text.
Using Jev as an intermediary router can dynamically select LLM models or skills before calling generative models.
Jev can execute fast classification and agentic tasks like email triage or computer-use action routing where outputs are restricted to defined decision options.
Worth knowing
Presented latency numbers and cost savings (e.g., 10x to 20x cheaper) reflect informal illustrative demonstrations rather than rigorous independent benchmark evaluations, and output schema constraints do not guarantee factual decision accuracy.