Jev in Practice: Browser Use and Agent Workflows

The author calls Jev fun and shares a live demo on val.run that anyone can try directly.

The author demos testing an app with Typesafe's Jev and opencode, saying the testing will be very fast.

The author ran Jev against an existing classifier eval that previously used Gemini 2.5 Flash Lite, and it won on both quality (saturating the eval) and speed (6x).

The author built computer use with Typesafe's Jev, claiming it is 155x cheaper than Opus 5, about 20x faster, and generalizes across operating systems.

The author ran Jev with Cua Driver on 9 executable tasks and it passed 8, versus 9 of 9 for Astra/Codex, with median decisions 13.6x faster and median task time 27% lower.

The author demonstrates Jev generating images by predicting each pixel in parallel, showing how it can be used for image generation tasks.

A small crypto trading demo has Jev answer buy or wait on real BTC, ETH and XRP prices, returning inspectable JSON and executing no trades.

A diagram explains how Jev uses a single Transformer decoder pass with cached keys and values to pick each JSON field's token directly, replacing token-by-token generation.

A Jev beta tester uses it as a router that decides whether a chunk of text goes through deterministic rules or a more expensive model, splitting work by cost.

The author breaks character-by-character generation into 29 yes/no classification questions, using Jev as the classifier in an autoregressive loop that produces a text-continuation model.

The author demonstrates making the Jev decision model conversational, with a video showing the interaction.

A developer backtests Jev against a Gemini-2.5-flash-lite setup in their MCP-based multiuser spec governance system, finding it cheaper and more consistent but slower than expected.