Find Your Next AI Superpower

Jev decision layer for Muse: a fast, cheap TypeSafe AI gate before expensive agent work — confidence policy, recipes, reference router, honest measurement.

The Windows app reads visible WeChat chat text via UI Automation or local OCR, uses Jev for structured intent judgment, and optionally uses DeepSeek to generate three copyable reply suggestions.

Jev is the typed-decision model from TypeSafe: it takes a `state` and a set of questions (`noul`, `choice`, `score`) and returns probabilities instead of text. How well a question works depends on its

Jev-powered: A context compactor that can only score, never write — so an agent's memory can't hold a fact the transcript never contained. Working demo, runs offline.

Jev-powered: High-speed, cross-agent safety gate plugin for **Claude Code**, **Codex CLI**, and **Antigravity**.

Keep Browser Use's execution engine. Move bounded decisions to Jev.

1,000 arXiv AI papers classified with one Jev decision each, checked against an LLM judge. Open rebuild, MIT.

Jev Engineering: Typed Decision Systems for Reliable Agent Workflows. Paper, diagrams, and companion examples by Av1dlive.

One TypeSafe Jev decision per step over the A11Y tree, executed via ADB. No screenshots and ultra fast!

Pre-message hook: use TypeSafe Jev to keep/drop skills and shrink agent context

A silenced message never reaches the language model, so it costs one Jev call and no model tokens. Direct messages always get an answer unless you choose to gate them too.

Classify support tickets in BigQuery with Jev and Cloud Run

The app sends typed text to Jev to get parallel emoji Choice, emotion Choice, Score, and Boolean results displayed as an emoji keyboard.

JEV Smart Router — a Databricks App that uses TypeSafe JEV to pick which model answers each message, then runs inference on the chosen Databricks Foundation Model API endpoint.

This Codex Skill lets Codex drive Chrome through Jev to complete multi-step browser tasks from a goal description with preset inputs.

Dependency-free Go client for TypeSafe AI's System One API and the Jev model

300 public pages audited for AI citability with Jev decisions, checked against an LLM judge: agreement, cost and latency, measured

JEV Reinforcement Learning: four classic games trained with JEV-powered rewards, reproducible experiments and checkpoint replays.

Fail-open Codex UserPromptSubmit hook that injects TypeSafe Jev pre-task routing metadata.

A Go client for the TypeSafe AI's System One API and its model, Jev.

Single-purpose MCP server (one tool, one job): a code-review gate powered by TypeSafe Jev (System One decision model).

A Claude Code hook that asks whether the decision you are writing needs a model at all. Includes a measured 149-row comparison of TypeSafe Jev against Claude Haiku 4.5.

Mock Jev API decisions with MSW for deterministic tests without real API calls or credits.