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.
Leaked JEV Signal Landscape Sparks Discussion
The post says the JEV signal landscape shows how six input sources can be handled in one agent, rather than requiring separate parsers, prompts, and code paths per input.
WHOEVER LEAKED THIS JEV LANDSCAPE OF SIGNALS DESERVES THE CTO SEAT I thought handling six input feeds in one agent meant six parsers, six prompts, and six code paths that disagree in production. Apparently not. This landscape's setup is much more interesting: → SOURCES: 6
· 28 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.
