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.
Understanding Jev: Calibration Is the Product
The author points out that many haven't truly understood Jev, emphasizing that calibration is the core value, and illustrates its importance with a simple coin toss probability example where wrong decisions lack cost.
People really haven't understood Jev. The calibration is the product. Most jev demos you are seeing don't have a "cost" associated to a wrong decision. At worst the dino runs into the cactus. Simple example of calibration of the probabilities: "I tossed a coin. The top face
· 0 likesOpen on X
More like this
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.
