8 posts on this topic
We build, step by step, a type-safe AI Router architecture that classifies incoming user requests with the Jev decision model before sending them to large language models in modern Next.js and TypeScript web applications.
We compare TypeSafe AI Jev and AWS Strands Decider 2B, two key representatives of the decision models becoming the new standard in AI agents, on intelligence score, local execution, calibration and cost.
We take a detailed look at the millisecond latency advantages and zero output token cost of using TypeSafe AI's Jev decision model instead of traditional large language models in production AI workflows.
We look at how you can solve wrong function calls and broken schemas in autonomous AI agents running in production with the TypeSafe AI Jev model's zero-type-error, calibrated confidence score architecture.
We examine the technical details of the TypeSafe AI System One architecture inspired by Daniel Kahneman's cognitive theory, the RLCD training methodology and the Jev model's record 1.6% calibration accuracy.
Jev does not generate chat text. It returns a defined decision and a confidence score. In the first example it picks a route, and if the score is low the request goes to a larger model or a human.
TypeSafe AI has left behind the slow, hallucination-prone design of classic LLMs and announced Jev, a System One decision model that is 200 times faster with zero type errors.
Developed by TypeSafe AI, Jev offers a new AI architecture that makes micro-decisions instead of generating text. We examine how Jev works and its impact on the industry.
8 articles about Jev, often together with AI, LLM, AI Agents. The newest one is "Jev Integration in Next.js and TypeScript Projects: Step-by-Step AI Router Setup".