The Jev launch post
Diogo Almeida introduces Jev, a structured decision model from TypeSafe. This resource provides an overview of the model's core approach, practical examples, and performance metrics as reported by the author.
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What’s newDiogo Almeida introduces Jev, a structured decision model from TypeSafe. This resource provides an overview of the model's core approach, practical examples, and performance metrics as reported by the author.
TypeSafe officially launches its research lab and introduces Jev to the developer community. This announcement features a launch film by Diogo Almeida and provides foundational context for the project's mission and future direction.
TypeSafe has removed the waitlist for Jev, allowing public access to the platform. Developers can use the official console to integrate and test typed decision-making capabilities within their own software applications.
TypeSafe highlights Jev’s typed-output approach, where applications provide state and questions to receive structured probabilities, choices, or scores. The announcement also details the integration of the Venice API for developers.
AI-assisted summaries and translations. Check original sources for context and performance claims.
This tool utilizes Jev to evaluate startup concepts. Users submit their business ideas, and the system provides feedback by suggesting whether to kill, fix, or ship the project based on its analysis.

Wallpets brings cinematic animal companions to your desktop with lightweight mouse-following behavior, idle play, and a growing pet catalog.
Explore supporterThis project explores how Jev can be integrated into UX/UI design workflows. It demonstrates the model's potential for interpreting color-related tasks and suggests new ways to interact with creative interfaces through language-based inputs.
This repository provides a reproducible evaluation framework for Jev, focusing on Korean language comprehension and medical text analysis. It includes documented runtime and cost metrics to support performance assessment in specialized domains.
Nitro optimizes Grok Build by using Jev to filter tool lists once per turn. This reduces token usage and costs by narrowing the available tools before model execution, while maintaining prompt cache efficiency.
This project demonstrates the integration of Jev on Cloudflare Workers to enhance search functionality and content tagging for keep.md. It highlights a practical implementation of serverless architecture to improve data organization and retrieval efficiency within a markdown-based environment.
This work-in-progress demonstration showcases the Jev model navigating the Minecraft environment. The creator illustrates the agent performing basic survival tasks, such as actively avoiding hostile entities like zombies during nighttime gameplay.
This tool provides a Jev-augmented proxy for Playwright MCP, designed to enhance coding agents with page-state triage, prompt-injection shielding, and goal-based snapshot pruning. It functions as a drop-in wrapper to implement risky-action gating during automated browser interactions.
This repository provides Python implementations for TypeSafe AI's Jev model, demonstrating how to use calibrated probabilities for automated, typed decision-making in software. It includes various use cases like security triage, invoice processing, and guardrails for generative language models.
Jev Arcade features a 1v1 first-person shooter experience where Jev autonomously manages movement, aiming, and combat actions. This web-based demonstration showcases real-time decision-making capabilities within a competitive gaming environment at a frequency of approximately 9 Hz.
JEVLAB ARTThis resource provides a collection of official agent skills for Claude Code, Codex, and compatible systems. It demonstrates fundamental primitives, design patterns, and methodologies for structuring effective evaluations within agentic workflows.
This project demonstrates the integration of Jev into the board game Codenames. The creator claims this implementation achieves fast and cost-effective performance during gameplay sessions.
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Explore supporterThis tool integrates WordPress with the Jev System One model. It enables the platform to generate structured decision outputs, specifically choices, scores, and null values, facilitating automated content logic within a WordPress environment.
JEVLAB ARTThis guide demonstrates how to integrate Jev with Pydantic AI agents to facilitate type-safe operations. It provides developers with a structured approach to ensuring data consistency and reliability when building AI-driven workflows within the Pydantic ecosystem.
This project demonstrates an autonomous web browsing agent that integrates Jev for decision-making with Vercel's agent-browser for execution. It includes a benchmark to evaluate the system's performance in navigating and interacting with live websites.
This tool demonstrates a Jev-powered workflow that enhances standard copy-paste interactions. It introduces a decision-making layer that automatically adjusts content formatting or selection based on the specific context of the user's current task.
Visual-Jev is a research project that explores decision-making processes based on direct image analysis. The creator demonstrates a system designed to interpret visual content without relying on text-based descriptions, focusing on automated visual reasoning capabilities.
Superlog is an AI SRE agent that investigates Sentry and Datadog alerts, filters noise, and opens pull requests from Slack.
Explore supporterThis documentation outlines the Python SDK for TypeSafe AI, focusing on defining yes/no, choice, and score-based questions. It demonstrates how to structure state and criteria using objects or dictionaries to facilitate structured interaction with AI models.
This TypeSafe reference guide explores the self-consistency of nouls within AI models. It provides technical documentation on maintaining logical coherence and structural integrity when processing these specific data types in automated workflows.
JEVLAB ARTThis tool improves zsh autosuggestions by replacing standard prefix matching with Jev-based ranking. It demonstrates a method for integrating intelligent suggestion logic directly into command-line interface workflows for more relevant terminal completions.
JEVLAB ARTThis repository serves as a curated directory of Jev-based projects. It provides a standardized GitHub workflow designed to facilitate the review and integration of Jev-only development tools within the ecosystem.
This video provides a concise four-minute overview of Jev. It outlines the core concepts of the framework while also addressing its current functional limitations for those looking to understand its practical scope.
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Explore supporterThis project demonstrates using the Jev AI model to solve Minesweeper. It employs speculative fan-out and confidence-gated routing patterns to query the model for mine probabilities, comparing its performance against a deterministic solver.
This community resource introduces a plugin designed to optimize agent context management. It demonstrates how to analyze and prune redundant data to maintain efficiency, providing a practical approach for developers working with agent-based systems.
This project provides a browser-based computer-use rig integrating Chrome DevTools with TypeSafe Jev. It serves as a testing environment for the Browser Use Olympics benchmark to evaluate agent performance in automated web navigation tasks.
This project introduces a 151M non-autoregressive decision engine designed for Jev environments. The creator claims the model achieves specific accuracy and calibration metrics on the LocalLLaMA typed-decisions benchmark compared to existing TypeSafe Jev and Laya implementations.
jev-align provides a framework for verifying the alignment of LLM responses and agent plans. This resource demonstrates how to use Jev to calibrate and validate model outputs against defined safety or operational constraints.

Guideless is an AI-powered platform that turns software workflows into video training guides.
Explore supporterThis resource outlines the core primitives of System One models. It provides a high-level overview of vendor workflow evaluations and pricing structures, serving as an introductory guide for those exploring these specific AI model architectures.
This resource documents a matched-precision comparison between a private fine-tuned classifier and standard models. It provides insights into decision-making accuracy when evaluating specific classification outputs against custom-trained benchmarks.
This project provides a benchmarking framework for market analysis using Jev. It demonstrates how developers can structure data-driven evaluations within the Jev ecosystem to assess specific financial modeling tasks.
JEVLAB ARTThis resource introduces Jev by demonstrating the practical application of Choice, Noul, and Score components within a Python project. It provides a foundational look at how these specific elements function to structure logic and data handling.
This experiment evaluates Jev as a search reranker, finding limited performance gains over standard vector retrieval. The creator reports mixed results that vary significantly based on the specific evaluation methodology applied during testing.

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Explore supporterThis demonstration shows Jev AI playing Tetris at high speed. The creator reports the system cleared 134 lines across 357 pieces in two minutes, illustrating the model's rapid decision-making capabilities in a real-time gaming environment.
This TypeSafe AI reference guide demonstrates standardized methods for extracting temporal data from unstructured text. It provides essential patterns for developers to implement reliable date parsing within their AI-driven applications.
This resource demonstrates how Box utilizes Jev to automate incident triage. It shows the system processing inputs to return boolean classification results alongside associated probability scores for improved operational decision-making.
The choice() function in the TypeSafe AI SDK allows developers to construct questions that require selecting from a set of named alternatives. It accepts specific instructions and criteria to define the available options for structured decision-making tasks.
This repository provides a curated instruction corpus for Jev and System One AI models. It features 119 rows of structured data including choice, noul, and score parameters to assist in training or evaluating TypeSafe AI agent behaviors.
This project demonstrates an offline decision system utilizing Jev to evaluate various research branches. It aims to streamline complex decision-making processes by providing a structured framework for selecting the most viable paths in research workflows.
This project demonstrates how Jev can be integrated into a Mac application to automatically retrieve and present relevant support articles from internal documentation, aiming to streamline the user assistance process.
JEVLAB ARTThis tool provides an agent skill designed to transmit completed coding-agent evaluations directly to the TypeSafe Jev platform. It serves as a utility for developers looking to integrate automated judgment workflows into their existing Jev-based development environments.
TrustGate provides an independent media trust and safety gateway utilizing TypeSafe Jev. This tool demonstrates a framework for managing content moderation and safety protocols within decentralized media environments through structured, type-safe validation processes.
PocketJev is an experimental tool designed for on-device visual decision-making on iPhones. It utilizes MLX and Qwen3-VL direct option logits to process visual inputs locally, demonstrating a potential approach for mobile-based Jev-integrated AI inference.

Falconer is an AI-powered company brain that keeps your engineering documentation accurate, searchable, and up to date by syncing with GitHub, Slack, Linear, and the rest of your stack.
Explore supporterThis tool demonstrates an approach to spreadsheet management that interprets user intent to provide contextual analysis. It moves beyond traditional static calculations by attempting to understand the underlying purpose of data entries.
This documentation details the SystemOneResponse structure within the TypeSafe Python SDK. It explains how the API returns structured answers, confidence scores, and token usage metadata for various question types.
This project provides an autonomous agent for reviewing pull requests. It utilizes typed decision-making, calibrated approval gates, and escalation protocols to trusted owners to streamline code review workflows within Jev-based development environments.
This project demonstrates a visual implementation of Tetris where Jev is utilized to determine piece placements. It serves as an experimental showcase for integrating TypeSafe logic into classic game mechanics.
This resource provides comprehensive documentation for Jev, a System One model designed for structured decision-making in software. It includes SDK guides, architectural patterns, and cookbooks for implementing typed questions like choices, scores, and yes/no evaluations within applications.

The AI-augmented control plane for cybersecurity — unify your security stack, quantify cyber risk in dollars, and run governed AI agents.
Explore supporterThis tool utilizes Jev to identify and flag low-quality text generated by Opus. It serves as an automated editorial assistant designed to help users filter out repetitive or unoriginal AI-generated content from their workflows.
This repository provides a curated collection of Jev use cases, workflows, and agent skills. It serves as a reference for developers looking to implement practical automation patterns and functional capabilities within the Jev ecosystem.
This repository provides a curated catalog of System One and decision-making models. It serves as a structured resource for contributors to the modelsystem.one project, focusing on organizing frameworks for analytical and intuitive cognitive processes.
This open-source Chrome extension utilizes Jev to filter out AI-generated prose and advertisements from web pages. It demonstrates a practical application for users seeking to manage content quality while browsing.
This official documentation provides a comprehensive technical overview of the TypeSafe AI API. It serves as a primary resource for developers to understand the available methods, parameters, and integration patterns required for building applications using the TypeSafe framework.

Menta es el software de gestión clínica impulsado por IA. Una plataforma todo en uno para la gestión administrativa y clínica de profesionales y clínicas
Explore supporterThis Rust library provides a lightweight integration for interacting with the Jev ecosystem by TypeSafe AI. It demonstrates how developers can implement client-side connectivity to Jev services within Rust-based applications.
jev-rs is a Rust-based engine that provides TypeSafe-compatible System One judgments from LLMs. It extracts log-probabilities for noul, choice, and score tasks without generating text, offering tools for local calibration and evaluation of model confidence and decision accuracy.
This official TypeScript and JavaScript client provides developers with inferred answer types for Jev integrations. It demonstrates how to implement type-safe communication patterns within web applications using a standardized SDK approach.
JEVLAB ARTcapture is a Mac voice diary application that utilizes Jev for automated content sorting. The tool integrates directly with Notion to organize recorded entries, providing a streamlined workflow for users to document their thoughts and daily activities.
JEVLAB ARTThis project provides a standalone Android agent framework where Jev handles decision-making processes. It includes a live React studio environment and demonstrates automated interaction capabilities through an Uber-themed application demo.

Wallpets brings cinematic animal companions to your desktop with lightweight mouse-following behavior, idle play, and a growing pet catalog.
Explore supporterThis open-source project introduces a routing mechanism for Claude Code. It demonstrates how to prioritize frequently used skills through Jev integration to improve task selection efficiency.
This tool utilizes Jev to identify AI-generated content on websites. It demonstrates a method for analyzing specific indicators to flag potential automated material, though the effectiveness of these detection patterns remains subject to ongoing evaluation.
JEVLAB ARTThis project provides a compact, extensible harness designed to facilitate decision-to-action workflows within TypeSafe Jev environments. It serves as a utility tool for developers looking to streamline automated logic execution.
This interactive game demonstrates Jev logic by navigating a maze represented as JSON. The system makes real-time movement decisions at each junction, showcasing how Jev processes structured data to drive gameplay mechanics in a classic arcade environment.
talkr is a speech analysis tool designed to evaluate 30-second audio clips. It identifies filler words, pauses, and confidence levels to provide users with actionable feedback for improving their public speaking and communication skills.
Lemonpod gives founders one morning brief that combines GitHub, calendar, tasks, inbox, and other work signals into a text summary and audio update. Start the day knowing what needs attention without checking multiple apps.
Explore supporterThis developer project demonstrates a voice control implementation for macOS. It utilizes Jev as a secondary fallback mechanism for processing commands when primary voice recognition inputs require additional support.
This demonstration shows Jev analyzing the narrative pacing of an essay authored by Dario Amodei. It illustrates how automated tools can be applied to evaluate the structural flow and composition of complex technical writing.
This TypeScript CLI tool enables users to search through code, documentation, and logs using natural language queries. It utilizes Jev Noul judgments to filter results based on probability thresholds and provides cached output for efficiency.
This guide demonstrates how to integrate Jev with LiteLLM to streamline model calls. It explains the configuration process for routing requests through an existing proxy to maintain consistent interaction patterns across different AI services.
jev4k provides a Kotlin-based domain-specific language and client interface for interacting with the Jev model. This tool simplifies integration by offering type-safe abstractions for developers working within the Kotlin ecosystem.
JEVLAB ARTJevinci is a developer tool that generates digital paintings by applying Jev pixel probability calculations. This project demonstrates how algorithmic probability models can be utilized to create visual art compositions within a programmatic framework.
This repository provides safety validation mechanisms for AI agents like Hermes. It demonstrates how to implement automated checks that require human approval or verification before an agent executes specific actions or modifies system states.
This repository serves as a sandbox for testing Jev, the System One model from TypeSafe. It provides a practical environment for developers to explore the model's capabilities through various experimental implementations and code samples.
jod provides semantic schema validation for Jev environments. This tool allows developers to validate local state and project typed answers, aiming to improve data consistency and type safety when working with Jev-based architectures.
This research deck explores the integration of Jev and System One models. It provides a structured overview of theoretical frameworks and conceptual applications, serving as an ongoing educational resource for those studying advanced AI architecture and decision-making processes.
chowder is a unified API layer that aggregates, normalizes, and serves your data from any source. One endpoint. Every ingredient.
Explore supporterThis demonstration shows a Jev-based pipeline classifying a 26-sheet construction plan set in under three seconds. The creator claims the system achieves performance parity with advanced LLM models while maintaining low operational costs for document processing tasks.
A little conversation in the lab.
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