Editor's Synopsis
Jev is a new AI classifier developed by Diogo Almeida, whose research contributed to building ChatGPT. Unlike conversational LLMs, Jev takes an input and an output schema, then returns probability scores for each possible answer in roughly 200 milliseconds. Ryan Vogel of the OpenCode founding team demonstrated its capabilities by running 1,700 real emails through it — getting category, priority, spam score, and reply score for each — at a total cost of just 18 cents. The output is type-safe numerical data, making it immediately usable in code without parsing natural language responses.
The core use cases center on decision-making at scale: lead scoring (rating contact form inquiries from 0 to 1), email triage, support ticket routing, and even video clip discovery where Ryan built a tool in 10 minutes that identified 17 strong moments in about 3 seconds. Greg from The Startup Ideas Podcast frames Jev as an "AI traffic cop" — high-confidence, high-value inputs get routed to humans immediately, lower-confidence ones go to automation or LLM drafts, and very low-confidence inputs get ignored.
The article also covers where Jev breaks down: Ryan tested it on Bitcoin buy/hold/sell signals and it performed poorly, suggesting it's not suited for complex predictive tasks requiring cross-referencing external data. He recommends keeping Jev in a "heavy advisory role" rather than giving it full autonomous control. The practical takeaway is to identify any expensive queue of incoming information in a business — inbox, contact form, support tickets — and place Jev at the front of it for near-instant, extremely cheap classification.
