TypeSafe JEV: System One Model Tutorial & Plugin Directory

What makes JEV different

• No text generation: Returns typed decisions, scores, and probabilities that code can act on directly
• No hallucinations: Calibrated confidence scores mean you know when the model is uncertain
• ~150ms decision time: 40-200x faster than frontier LLMs
• Built for machines: Designed for automated workflows and code integration, not human conversation
• Use cases: Classification, routing, scoring, A/B testing, workflow automation

JEV ecosystem (top plugins by GitHub stars)

Quick start: using JEV in your code

import { TypeSafe } from '@typesafe/client';

const client = new TypeSafe({ apiKey: process.env.TYPESAFE_API_KEY });

// Classification with calibrated confidence
const result = await client.classify({
  input: "Is this customer support request urgent?",
  labels: ["urgent", "normal", "low"],
});

console.log(result.decision); // "urgent"
console.log(result.confidence); // 0.94
console.log(result.probabilities); // { urgent: 0.94, normal: 0.05, low: 0.01 }

When to use JEV vs traditional LLMs