User shares a cost-saving model calling strategy: first ask Jev to answer yes/no and score, if confidence is above 0.9 just stop, otherwise Jev routes the call to the right model (Haiku, Sonnet, etc.), reducing reliance on expensive models.
the cheapest setup upgrade i made this month: ask the smallest model first. jev → haiku 5.5 → sonnet 5.5 → opus 5.5 > jev answers yes/no, picks and scores. above 0.9 confidence it stops there > otherwise jev routes the call to the right model > haiku does bulk, sonnet
LLM holds memory/context, uses agent-desktop to take snapshots and sends them to Jev, which selects the right element to act on for faster Computer Use, with Cursor enabled and headless support.
Riley Brown shares that TypeSafe AI's Jev classified 500 emails in seconds for just 3.5 cents, highlighting its fast and low-cost email classification.
A comprehensive explainer video introducing Jev, an AI that doesn't write text but determines which department to route inquiries to and how urgent they are.
