Find Your Next AI Superpower

Jev-powered: The signal result above was challenged on three points: no non-AI baseline, selection and evaluation on the same emails, and no equivalent decomposition for the LLM. Three controls were a

Jev-powered: Chess moves, evaluations, persona opponents, and game classification using TypeSafe AI System One models. Resolves natural language move intents into legal moves, evaluates positional sha

Jev answers multiple-choice questions and does not generate text, so on its own it cannot return a name, an email address or a quote. jeveryword numbers the words of your text, offers those numbers as

Jev-powered: The live run excluded 205 reports with missing or invalid coordinates, completed **634 API calls without retries**, and reported **539,979 input tokens**. Repeated descriptions share one

Jev-powered: Every mode auto-curries when called with only the patterns:

Jev-powered: The agent loop where decisions don't cost a large language model call. Zero deps, runs offline, no API key needed.

Laya (open source, local) vs TypeSafe Jev (API): two AI models race in Snake and fight in a Mortal-Kombat-style arena. Every move is a real model decision.

Jev Auto Router (Jev Router): experimental per-call GPT model routing for Codex via TypeSafe Jev and a local Responses proxy, with independent task verification.

Jev-powered: Replaces hardcoded RAG thresholds with explicit calibrated decision points. Five primitives (retrieval stopping, chunk splitting, context selection, answer abstention, cache trust) behind

Classify Hugging Face datasets across typed semantic dimensions with TypeSafe Jev System One. Auto-adapts evaluation rubrics to dataset domains (reviews, news, LLM tuning, support) and classifies rows

Filter any live Twitch chat with Jev: a bring-your-own-key Chrome extension

Jev-powered: Requires Python 3.9+ and a modern browser with module workers and OffscreenCanvas (current Chrome, Edge, Firefox, or Safari). No packages, build step, or Node installation needed.

TypeSafe's Jev model plays Vampire Survivors on Steam: BepInEx plugin + Python brain + live decision dashboard. Native Linux only.

Jev-powered tool and skill selection, context search, and output triage for Codex via MCP

TypeSafe Jev (System One) decision tools for Hermes Agent: jev_check / jev_route / jev_score / jev_evaluate

Custom nodes for using Jev's text interpretation and judgments in ComfyUI. Use natural-language instructions to select candidates, evaluate conditions, score text, or extract numbers, then pass the re

Jev-powered: This project evaluates a fixed model. It records rewards and decisions without training or updating model weights. A seeded random policy provides a local baseline.

Jev-powered: Blender exposes ~2,500 operators; a TypeSafe `Choice` question holds at most 255 options, so the search is hierarchical (two requests per search):

A Codex overlay incorporating JEV to make the best decisions regarding model selection and depth of reasoning. All while automating the process via an automated Kanban system.

Browser use for coding agents, 3-5x faster than browser-use. MCP server + CLI; TypeSafe Jev decides every step in ~300 ms.

Jev Decisions Plugin for Hermes (and other AI Agents): tool risk reviews, human approval recommendations, evidence checks, and a local decision journal.

A browser form for building requests to TypeSafe's Jev: pick a template, fill in the blanks, copy the request. No JSON, no install, runs locally.

Sub-100ms security gate for AI agent tool calls, powered by TypeSafe's Jev (System-1) decision model. Single-request Choice/Noul/Score evaluation, dual-factor blocking matrix, calibrated-confidence ro