Mark

Jev is "Internet" moment for AI: up to 193x faster and 444x cheaper in tests with Claude Fable 5.1 and GPT-6 Astra Whar is Jev, how to use and unlock its real 100х advantage in my 10-page research: step 1 → meet Jev: LLM writes, agents act, Jev chooses the next move - split intelligence from execution step 2 → turn every agent fork into three primitives: Choice selects one route, Score measures a defined scale, Noul returns the probability of yes step 3 → build before getting access: use TypeSafe’s official adapter with OpenAI, Anthropic or xAI, then swap in Jev without rebuilding the graph step 4 → setup first Jev: one state, three parallel decisions, risk-based thresholds and a real queue your agents can execute step 5 → batch decisions instead of serializing them: 13 questions in one call ran 10x faster and 12.2x cheaper than 13 sequential calls step 6 → place Jev at every bounded fork: choose the agent, model, tool, browser action or human escalation, then read fresh state step 7 → benchmark the entire loop: Browser Use hit Google Flights in 7.1s, Every ran 777 checks in under 0.7s, Mobile Jev completed 9 actions in 21s step 8 → rank wide, read narrow: Jev cut wrong Hermes skill loads from 16.8% to 7.3% and pushed legal Top-10 retrieval from 38% to 62% step 9 → steal a system, not a prompt: Chief of Staff, model router, inbox firewall, research feed, browser controller and safety gate all use State → Questions → Action → Verify step 10 → keep Jev out of math, writing and irreversible execution: code computes, LLMs create, Jev decides, fresh state proves the result the result: one slow, expensive agent becomes an always-on decision machine that routes, scores and escalates in milliseconds Copy the complete 10-page Jev blueprint - then read full 10-step roadmap below ↓