Every explains Jev for cheap text judgments and the Jevgram tool
Every describes Jev as a fast, cheap model that returns a probability for a question about text or structured data, and @hammer_mt used it to build Jevgram, which scores whether text sounds like AI.
Jev is a new kind of model: fast, cheap, and accurate enough to make judgment calls. You ask it a question about text or structured data and it returns a probability. Is this email urgent? Does this paragraph contain a particular writing tic? @hammer_mt used it to build Jevgram, a tool that scores text against questions such as “Does this sound like AI?” His suggestion: Give a coding agent more specific checks—like looking for “not X but Y” constructions—so it can revise flagged passages as it writes rather than after. What makes it useful is that the feedback is cheap enough to run constantly, not just at the end. Start with a judgment you could make in a few seconds, define what a useful answer looks like, and test whether Jev agrees with you on a handful of examples before running it across the whole collection. Read the full piece: https://every.to/context-window/how-to-get-the-most-out-of-jev?utm_source=x&utm_campaign=bau&utm_content=every-260926-jev-explainer