Jev in Practice: Browser Use and Agent Workflows

A step-by-step checklist for learning Jev, covering state as input, typed questions, the Choice, Score and Noul decision types, and confidence, to help developers turn application state into actionable decisions.

A curated GitHub list of public projects, integrations and discussions built on Jev, TypeSafe AI's System One model for typed decisions.

Trading agent project Hydra's weekend update integrates Jev to judge every lead in milliseconds, swaps in new data sources and runtime, and locks 18.6M HYDRA for a year.

Moved by an article, the author notes that Jev only outputs a 0-to-1 judgment usable as a fuzzy if and already plugged into RLM, and reflects on having seen the trees but not the forest.

A web app runs RDMs locally with in-browser models or via Jev router through OpenRouter, and also shows that dspy can run in the browser.

Each turn runs agents, scores output, keeps or rejects, updates state and checks the goal; after 5,400 agent runs Jev returned stop, with no questions sent to a human.

A trading bot chained nine Jev decision links, closing the session whenever any link's tension fell below 0.68; over 12 days it made 118 trades with a 61% win rate and a -13.1% max drawdown.

A Jev-driven trading agent moved only along 7 legal edges between 16 states, making 84 trades with a 58.3% win rate and growing the account from $1,000 to $3,486 with a -10.6% drawdown.

Routing 1M support tickets through Jev for classification and confidence checks, and sending only the hard 20% to a frontier model, cut the bill from $30,400 to $6,480.

The author sketches a chain where a crawler reads, Jev decides and bots execute, using a hypothetical CRAWL coin run from $52K to $3.3M to show how Jev would make fast buy and sell calls.

Muse proactively leaves messages and pushes information; for topic selection it first searches at least 20 candidates, uses Jev scoring to pick the top 10, then carries the work through to a spoken script, visuals and publishing on social platforms.

A developer uses Jev to tag subscribed content by user-defined prompts and build a personalized feed, then applies PCA clustering to produce visualizations.