Jev versus text-generating models
Jev does not generate free-form text. A side-by-side video compares parallel decision-making with token-by-token text generation.
JEV Engineering: Agent State to Feedback Decision Flow
Introduces the JEV Engineering agent flow: Agent State → Decision Engine → Typed Decision → Orchestrator → Action → Feedback, with the key being to separate probabilistic intelligence from deterministic execution.
JEV Engineering: Agent State → Decision Engine → Typed Decision → Orchestrator → Action → Feedback The key: separate probabilistic intelligence from deterministic execution. #JEV #AgenticAI #AIAgents #AIEngineering #GenAI
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Pricing, evaluations, and the name Jev
The launch thread explains code-and-AI workflow evaluations, the input price announced at launch, and free output tokens.
Jev Model: Decision-Making Probability Generation, 200× Faster and 400× Cheaper Than LLMs
This model is built for decision-making rather than text generation, outputting probabilities for each option in parallel instead of token-by-token responses. The author shares a simplified comparison of normal LLMs versus this Jev model.
