Find Your Next AI Superpower

Jev-powered: A Pi extension exposing TypeSafe judgments as five narrow tools, keeping threshold and action control in host code.

Cost-aware LLM router that picks the cheapest model capable of handling a query, using TypeSafe's Jev for fast classification instead of an LLM call.

Jev Issue Radar is a read-only dashboard for GitHub duplicate-issue triage. It retrieves likely candidates, asks Jev whether each pair is duplicate, related, distinct, or insufficiently documented, an

Decisions, not tokens: minojev reads calibrated, typed probability distributions straight from hidden states in one forward pass — zero output tokens, fully reproducible on a laptop CPU.

A skill for Hermes and other agents to query TypeSafe Jev for bounded option judgments and confidence escalations.

Wikipedia link race pathfinder guided by Jev: assesses outbound links to navigate between two articles in real time.

Automatic model router for Pi coding assistant: integrates Jev via Vercel AI Gateway to dispatch tasks efficiently.

SQLite C extension enabling TypeSafe Jev judgments as native SQL functions for semantic scoring and choices.

Benchmark and tool evaluating how well TypeSafe Jev identifies real secret credentials in file snippets.

Real-time Snake game driven by parallel Jev assessments, deciding optimal turns in a single API call per tick.

Jev-powered: Ultra-fast, fail-open advisory decisions and verbatim extractive reading view for AI coding agents and CLI pipelines

A Python decision gateway and Model Context Protocol (MCP) server exposing TypeSafe's Jev model through OpenRouter's decisions endpoint to Claude Code, Codex, and Cursor agents.

Connect TypeSafe Jev to Grok Bot as a cheap decision layer - usage gates, skill template, examples

Jev-powered: An automated research paper radar that fetches the latest papers from arXiv, evaluates their relevance to a configurable research profile using TypeSafe AI, and ranks them by relevance sc

Jev-powered: 1. Evaluates the configured dataset and measures uncertainty. 2. Selects ambiguous rows plus a random audit sample for you to label. 3. Uses your accumulated labels and optional rationale

Provides Jev structured decision support for Agent Harness to recommend tools, Skills and Agents and return judgments with probabilities, with a native DeepSeek Harness plugin and an iPolloWork entry

Retrieve by relevance, not resemblance: filter an AI assistant's memories with TypeSafe's Jev

Voice and typed computer use for macOS. You say what you want. Jev picks the next on-screen action. macOS performs it. No screenshots: the app reads the screen through the Accessibility tree.

JevBench v1 - a benchmark for Jev-class typed decision models: smart, cheap, fast, reliable, open.

Multi-drone autonomy lab demonstrating TypeSafe Jev reflex decisions with optional System 2 strategy guidance.

The decision layer for your Rails app. A Rails-native wrapper around TypeSafe's Jev System One API: typed, calibrated decisions in your control flow.

Jev is a decision model: it answers a typed question with a calibrated probability rather than prose. This action asks it one yes/no question per thing worth being sure about — answered in parallel in

This repository collects real Jev API-call receipts, test suites, and bilingual guides to map which narrow-decision tasks suit Jev and how Agents should evaluate and report fit.