Find Your Next AI Superpower

What your last session knew, scored against what this one is doing. MCP server: a per-project ledger written as things happen, recalled per task with TypeSafe's Jev evaluation model via Vercel AI Gate

MCP server for Jev (TypeSafe System One): the three official question types — choice, score, noul — plus batch classify. Calibrated probabilities, ~0.5s, <$0.001/call.

lint text in code by jev scorerer

Ten runnable Jev examples for agent decisions: memory conflicts, tool-result checks, recovery, context selection, and handoffs. JavaScript, zero dependencies.

Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.

Evidence-first long-term experiment memory skill for AI coding agents, with optional Jev decision-model layers

Jev-powered: Probability-scored guardrails for Claude Code: deny rule-breaking edits and unasked-for deploys, route your docs into each prompt, and check the final answer against the turn's own eviden

Researching how Jev can accelerate tasks that rely on LLM decision-making.

A Clash Royale bot with no trained policy: Jev (TypeSafe System One) makes every decision from the live game state

This project integrates Jev to provide structured decisions for its workflow. See the repository for implementation details.

Unofficial Go client for the TypeSafe AI System One API (Jev) — typed questions in, calibrated answers out.

Claude Code plugin: Jev for rule checks, verbatim compaction, and prompt routing

Jev? Déjà vu. Browser agents that run on instinct, no Jev needed. One look at the page, one call to any open model, one action. Faster than the Jev demo on Google Flights.

This project integrates Jev to provide structured decisions for its workflow. See the repository for implementation details.

Coding-agent memory where every fact is a verbatim quote graded by TypeSafe Jev's calibrated confidence. Local-first, SQLite receipts, zero dependencies.

Model performance first. Token savings second. A Pi extension with freshness-aware read dedupe, Jev log filtering, and searchable verbatim recall. Keeps existing message history intact.

Jev (TypeSafe System One) inner loop for ego-browser — one ~0.4s typed decision per DOM step instead of an LLM turn. Agent skill for ego lite.

Jev-powered: Score a candidate pair after ordinary SQL has generated it:

This project integrates Jev to provide structured decisions for its workflow. See the repository for implementation details.

Most AI demos generate text. Jev does not. It reads a sentence and returns typed answers with probabilities: a choice, a yes or no, a score. That makes it usable as a primitive inside ordinary code ra

Jev-powered: Extra state goes in `context`, such as a policy or the documents a RAG app retrieved. A claim can name it in backticks:

Jev-powered: (`agent_framework.foundry.FoundryChatClient` is a different client this repo doesn't use — its `credential` parameter only accepts Azure AD token credentials, not an API key. If you only

TypeSafe Jev semantic guardrails for Claude Code