Jevry

jev-voice-browser is a Node application that controls a headed Chromium browser by voice or typed commands. It streams partial speech transcripts from the browser's Web Speech API to a Node server, which uses TypeSafe's Jev model to classify the intent, select a page element or site, extract candidate text and URL spans, determine whether the command is complete or addressed to the browser, and assess whether an action is destructive. Playwright then executes navigation, searches, typing, clicking, scrolling, history, and tab actions. The application sends the page URL, title, detected site, a compact snapshot of visible elements, and recent actions to Jev for each transcript update. Code—not the model—constructs URLs, copies text verbatim, and applies policy thresholds to decide whether to act, wait, ignore, request confirmation, or show numbered alternatives. Recent action context supports corrections such as reversing an action, rejecting a selected result, or choosing another candidate. It requires Node.js, Chrome or Edge for microphone input, Playwright's Chromium installation, and a TypeSafe API key. The server can launch a persistent-profile Chromium window or attach to an existing browser through CDP; it also supports headless operation and typed commands when no microphone is available.

jev-use is a macOS voice and text computer-use harness built on Jev. It reads the frontmost app's Accessibility tree rather than taking screenshots, sends the command, app and window names, visible targets, and recent actions to Jev, then selects and performs operations such as clicking, typing, pressing keys, scrolling, opening items, and arranging windows. After each operation it reads the Accessibility tree again and continues until the task is complete, blocked, or waiting; low-confidence or destructive actions stop for confirmation. The app supports hold-to-talk, typed commands, hands-free listening, and an optional “Hey Jev” wake phrase, with speech handled by Apple Speech. It requires macOS 14.2 or later and Xcode, has no external dependencies, and stores the TypeSafe API key in the macOS Keychain.

Jev Search is a web search application built by Search1API that uses TypeSafe's Jev to interpret plain-language requests, select search terms, sources, and time ranges, and rank results. It queries general and vertical search engines concurrently, merges results by URL, engine agreement, original rank, and Jev relevance scores, and returns links and snippets with editable filters rather than generated answers. The application streams results as search lanes finish, reports failed sources separately, and can be installed as a browser app; it requires a network connection for searches. The open-source application is MIT licensed and can be self-hosted on Cloudflare Workers with Search1API and a Jev provider such as TypeSafe, Vercel AI Gateway, or Cloudflare Workers AI.

Jev is TypeSafe AI's System One model for making fast, structured decisions within software. It accepts unstructured data or program state and returns predefined, type-safe structured values with calibrated probabilities, confidence, and uncertainty rather than generated text. Jev uses a model architecture and parallel sampler that produces outputs in a single query, together with TypeSafe's Reinforcement Learning for Calibrated Decisions (RLCD) training method. It is intended for classification, routing, scoring, extraction, moderation, verification, guardrails, and other AI-powered workflow decisions. TypeSafe describes it as an early-access service for integrating probabilistic decision functions into software, with reported end-to-end response times in the tens to hundreds of milliseconds.

Jevmind is a Python decision layer for software agents. It turns agent decisions into typed answers such as yes/no results, choices, and scores with confidence values, then applies a code-defined gate: uncertain answers escalate, trusted positive answers may act, and trusted negative answers are held. Its decisions are recorded in an append-only, hash-chained ledger, graded against later outcomes, and optionally recalibrated with Platt scaling. It includes nine skills for compacting tool output, navigating repositories, reviewing diffs, routing tasks to model tiers, checking completion claims, curating training data, walking linked markdown vaults, guarding commands, and running a structured-state control-loop demo. The default local brain uses readable rules, reflexes, and BM25 retrieval without a language model; an optional Jev HTTP brain can answer the same typed questions, while replay mode uses recorded answers. Jevmind runs as a command-line tool and MCP server, includes a Claude Code hook for shell-command screening, supports Python 3.10+, has no runtime dependencies, and is distributed under the MIT license.