The author shares a workflow for building a fraud investigation agent with Jev and TigerGraph: after an alert, graph evidence, Bayesian updates, and pattern recognition are used, Jev performs an extra lookup, and a policy-based verdict is made before human approval. Jev acts as the control layer, orchestrating the decision process.
Been building a fraud investigation agent using Jev + TigerGraph. The flow is basically: Alert → graph evidence → Bayesian update → pattern → Jev's one extra lookup → policy → verdict → human approval. Jev isn't the decision-maker. It's the control layer that decides
James Long uses Jev to tackle the most annoying problem in personal finance tools: generating good payee descriptions from raw bank data. It got about 95% of the way there on the first try, with many ways to improve the prompting.
A developer uses Jev to decide buy or sell and executes real trades on Kuru's on-chain order book via Monad every 300ms block. Link
Erik Spock Gafni suggests that those who make a lot of profit from Jev trading donate some of their profits to charity.