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 resource contrasts Jev with LLMs, highlighting how Jev evaluates predefined decisions directly. It demonstrates how this architectural difference allows for the parallel processing of independent questions, distinguishing its operational approach from standard generative language models.
jev-shield is a semantic MCP firewall utilizing Jev for verification. The creator claims it screens tool interactions with high recall and low latency, aiming to provide a secure layer for monitoring tool calls, results, and descriptions within agentic workflows.
This experiment explores a framework for converting raw trading signals into actionable automated decisions. It demonstrates a methodology for improving system responsiveness when interpreting market data inputs.
JEVLAB ARTThis React-based developer tool attempts to predict age ranges associated with specific Korean names. It functions as an experimental utility for analyzing naming trends and demographic patterns within the Korean language context.
This project integrates Jev into Noul and Score frameworks to support MCP applications. It demonstrates how developers can implement type-safe structures within agentic workflows to improve reliability when building modular AI-driven systems.

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Explore supporterThis project demonstrates an integration between Home Assistant and Jev. It aims to process user input using structured logic rather than relying on large language models for command interpretation.
This resource provides an independent walkthrough that distinguishes TypeSafe's official project claims from available public evidence. It focuses on the practical implementation of typed decision-making frameworks rather than relying on conversational AI interfaces.
This experiment demonstrates using Jev as a plugin within Claude to optimize tool usage. The creator claims this approach significantly reduced token consumption from 1 million to 86,000 during their testing process.
jevmeter provides a tool to overlay live TypeSafe Jev scoring onto video content. The project demonstrates how to analyze and render sentence-level metrics directly into a 16:9 video format for visual assessment of Jev compliance.
This documentation defines the NoulQuestion interface for the TypeSafe AI SDK. It outlines the structure for yes/no queries, including optional fields for outcome descriptions and instructions, enabling developers to integrate structured binary decision-making into their AI workflows.

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Explore supporterThis demonstration showcases Jev performing real-time decision-making within the game Doom. The creator claims the system executes moves at a frequency of up to ten times per second during gameplay.
This project provides a secondary MCP server implementation for Jev. It serves as a technical resource for developers looking to integrate PyModel-based components into their Jev-compatible agent workflows.
This extension provides five pi tools that expose TypeSafe Jev judgments. It allows AI models to perform narrow semantic evaluations while ensuring developers retain full control over operational thresholds, weighting, and final system actions.
This resource demonstrates the use of Jev for real-time coaching support. It highlights how the tool assists users in identifying and correcting errors or inconsistencies during writing sessions to improve overall quality.
This research project explores the integration of Jev with Qwen3 models within an NVIDIA DGX Spark environment. It demonstrates a specific implementation approach for running these models on high-performance hardware configurations.

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Explore supporterThis tool functions as a logic interpreter that utilizes plain English to assist Jev in executing complex reasoning tasks. It demonstrates a method for extending model capabilities through structured natural language processing for logical operations.
Lurk is a monitoring tool designed to track Reddit threads for AI-cited data. It provides automated analysis of threads, delivering insights directly to users through email, Discord, or Slack integrations to help monitor information usage.
This documentation details the RetryPolicy class for the TypeSafe AI Python SDK. It explains how to configure retry attempts, backoff strategies, jitter, and specific HTTP status codes to handle transient network errors and API connection issues effectively.
This repository demonstrates how to implement agent routing using TypeSafe Jev integrated with the Cloudflare AI Gateway. It serves as a practical example for developers looking to manage agent workflows through a managed infrastructure layer.
This SQLite extension enables direct integration with TypeSafe Jev, allowing users to execute decision-based AI tasks like classification, scoring, and yes/no probability checks directly within SQL queries using standard libcurl-based function calls.
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Explore supporterThis project demonstrates the integration of Jev with an Astra planner to control gameplay within Minecraft. It serves as a technical experiment for autonomous agent navigation and task execution in a sandbox environment.
This tool provides a local browser automation interface. It utilizes TypeSafe Jev to determine bounded page actions while restricting text models to handling field values only.
SmartMoney-Cub is a read-only trading journal and review harness that uses Jev to analyze financial evidence. It helps human traders evaluate their decisions and evolve strategies through structured, offline feedback loops without ever executing trades or accessing brokerage accounts.
JEVLAB ARTjevwire provides a decision layer for AI agents, featuring an MCP server, an embeddable DecisionModel library, and a Claude Code plugin. It demonstrates how to integrate TypeSafe AI's Jev framework to manage agentic decision-making processes.
This project provides an open-source router that integrates Jev with LiteLLM to dynamically select appropriate language models. It demonstrates a method for implementing type-safe model routing within AI agent workflows.

Sound notifications for any AI agent — hooks for Claude Code, Cursor, Codex & more, plus an MCP server so the agent can choose its own sounds.
Explore supporterThis integration connects the typesafe-sdk with Ruby on Rails applications. It provides tools for managing configuration, tracking usage and cost telemetry, and implementing optional confidence policies for AI-driven workflows.
This repository provides an implementation of the Typesafe AI SDK for the Elixir programming language. It demonstrates how to integrate AI services using the Req HTTP client library for streamlined request handling.
refgarden is a spatial reference explorer designed for creators. It provides tools for local Jev query management, metadata highlighting, and the organization of source-linked collections to assist in research workflows.
Hollow Creek features village NPCs that evaluate the player every tick to determine their actions and emotional state. This experiment demonstrates a reactive, non-dialogue-based interaction system where characters continuously process environmental inputs to shape their behavior.
JEVLAB ARTJev Sift is a developer tool designed for agent-based classification and selective reading. It utilizes an agent plugin and MCP tool to help users filter and organize information streams efficiently.
This project demonstrates an implementation of web browser automation utilizing the Jev model. It provides a framework for agents to interact with web interfaces, showcasing how Jev can be applied to navigate and perform tasks within a browser environment.
This browser extension project demonstrates an approach to content filtering by using AI models to identify and remove advertisements in real time. The creator claims the system performs classification dynamically as users browse web pages.
JevBench provides a standardized framework for evaluating Jev-class typed decision models. This resource demonstrates benchmarking methodologies focused on assessing the efficiency, reliability, and openness of decision-making systems within the Jev ecosystem.
frost is a command-line interface model router built with TypeSafe Jev. It provides a configurable framework for managing model interactions, allowing users to route requests through flexible, defined pathways.
JEVLAB ARTThis project demonstrates the integration of TypeSafe Jev with Mobile MCP to facilitate structured control loops on Android devices. It provides a framework for developers to implement responsive agent-based interactions within mobile environments.

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Explore supporterThis community node for n8n integrates Jev by TypeSafe to automate text classification and routing. It allows users to define custom questions and receive probability scores for each answer to facilitate manual review of uncertain items.
This resource demonstrates a method for high-speed model routing within agent-based systems using Jev. It highlights how criteria-based classification can be applied to optimize task distribution across different AI models.
JEVLAB ART
JEVLAB ARTThis repository provides runnable examples for implementing Jev using OpenRouter. It serves as a practical resource for developers looking to integrate type-safe decision-making workflows into their applications through direct, hands-on code demonstrations.
This Nushell module provides an interface for the TypeSafe System One API. It enables users to execute typed decisions supported by calibrated probability outputs within the shell environment.
JEVLAB ARTThis proof of concept demonstrates an implementation of the Model Context Protocol for the Jev AI model. It serves as an experimental integration tool for developers exploring Jev connectivity within agentic workflows.

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Explore supporterThis Chrome extension demonstrates an agentic approach to web navigation. It utilizes Jev to facilitate automated decision-making, allowing the agent to interact with website elements and perform tasks directly within the browser environment.
This project implements a Pareto-optimal router for OpenRouter designed for Raspberry Pi hardware. It demonstrates how Jev-based logic can be utilized to automate decision-making processes for selecting optimal routing paths within a resource-constrained environment.
spendbrake provides a mechanism for managing AI agent expenditures. It demonstrates how to implement budget controls by enabling users to continue, downgrade, or stop agent operations using TypeSafe Jev.
This tool provides a recursive tournament engine for evaluating AI models. It utilizes Swiss matchmaking logic to organize competitive matchups and calculate Elo ratings for performance analysis within the Jev ecosystem.
This experimental protocol explores evidence-aware handoffs between AI agents. It demonstrates a Jev-assisted review mechanism designed to validate outputs before they are passed to a lead agent for final processing.
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Explore supporterJev automates the conversion of websites into native mobile applications. The platform determines the most effective build process and assists users with the technical requirements for app store submissions.
This package provides an unofficial Laravel integration for TypeSafe Jev AI. It demonstrates how to implement typed responses, asynchronous request handling, and scoped dependency injection within the Laravel framework, while including testing fakes for development workflows.
JEVLAB ARTThis project demonstrates a Jev-powered agent designed to automate LinkedIn recruitment tasks. It showcases the agent browsing professional profiles, saving relevant links, and evaluating candidates based on specific hiring criteria provided by the user.
This official documentation provides the reference materials for the TypeSafe Python SDK. It outlines the necessary methods and configurations for developers to integrate TypeSafe AI capabilities into their Python-based projects.
This community discussion explores the potential of Jev to function as a high-speed, cost-effective decision-making layer for AI agents. It proposes that integrating Jev could streamline agentic workflows by providing a lightweight control mechanism for complex task execution.

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Explore supporterThis official reference documentation details advanced structural primitives for TypeSafe AI. It provides technical guidance on organizing complex data architectures to ensure consistency and reliability within AI-driven development workflows.
This project showcases a dashboard that utilizes Jev to categorize social media content across eight distinct dimensions. It demonstrates a method for organizing large datasets to facilitate structured content research and analysis for community-driven insights.
This tool utilizes Jev to perform editorial content evaluation. It demonstrates a method for generating probability scores rather than text, aiming for increased efficiency and reduced operational costs compared to standard language models.
This tool utilizes Maxfusion and Jev to evaluate individual shots within video advertisements. The creator claims the system processes hundreds of ads in minutes, demonstrating a method for rapid, automated visual content analysis and performance scoring.
This integration enables Jev to function within the Netlify AI Gateway environment. It demonstrates a streamlined configuration process for deploying Jev-based workflows directly through Netlify infrastructure.

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JEVLAB ARTThis resource outlines the core architecture of Jev as a System One model. It explains the intended applications and operational boundaries of the technology for developers and users interested in its specific functional design.
SIEGE is a typed action gate system designed to defend against multiple agents. The project demonstrates a defender loop architecture that learns from breach attempts, with evaluation facilitated by W&B Weave.
This command-line tool utilizes TypeSafe Jev to identify personally identifiable information within text. It demonstrates how to detect data presence, assess sensitivity levels, and extract specific character spans for privacy auditing.
This project demonstrates a live Bluesky feed integrated with real-time emotion detection. It showcases an experimental approach to social media interaction by automatically matching user avatars to the detected sentiment of their posts.
This project demonstrates an agentic workflow where Jev is utilized to select and execute actions within the Codex Computer Use environment. It serves as a practical implementation for integrating automated decision-making into desktop browser tasks.
OpenAI reported that a new internal model has solved over 100 long-standing mathematical problems, including the Navier-Stokes millennium challenge. By utilizing approximately 10,000 AI agents working in parallel, the company achieved these results in just 24 days of training and development.
JEVLAB ARTThis repository provides a reproducible benchmark for TypeSafe AI's Jev, evaluating its performance on agent tool-call risk classification. It specifically tests accuracy, latency, and the reliability of confidence scores across clear, ambiguous, and adversarial task scenarios.
This project demonstrates an agentic browser runtime that utilizes TypeSafe Jev for decision-making processes. It showcases how the Jev model integrates with the Aside runtime to manage choice, scoring, and noul operations within a web environment.
JEVLAB ARTThis resource serves as a curated directory for Jev-based projects. It provides developers with organized access to various repositories, software development kits, and performance benchmarks relevant to the Jev ecosystem.
This project demonstrates an unconventional use of Jev to perform string left-padding. It uses a model call to select a specific number of spaces, serving as a humorous example of over-engineering a task that standard library functions handle natively.

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Explore supporterThis demonstration compares Jev and LLM outputs side-by-side using affectus and Vercel AI Gateway. It allows users to observe differences in emotional fluctuation metrics and response latency for identical input statements within a single interface.
This tool integrates Jev to manage Ableton Live through concise command inputs. It demonstrates a workflow for automating digital audio workstation tasks by utilizing Jev's decision-making logic to execute user-defined musical control sequences.
This project provides an open-source decision server compatible with Jev architecture. It utilizes DiffusionGemma to demonstrate a System One processing approach for automated decision-making tasks within a modular framework.
This project demonstrates a Snake game implementation where movement is controlled by parallel Jev assessments. It showcases a specific architectural pattern using one API call per game tick to manage the snake's directional logic.
This command-line interface and agent skill for the TypeSafe System One framework enables structured interaction with Jev. It demonstrates how to implement typed Choice, Score, and Noul judgments within an agentic workflow.
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Explore supporterThis repository serves as a curated directory of software projects utilizing TypeSafe's jev framework. It provides a centralized list to help developers discover real-world implementations and community-driven applications built with this specific technology.
This project implements a web search tool that utilizes Jev to select relevant sources for user queries. It demonstrates how to integrate Jev into an MCP-compliant search workflow for retrieving information in plain language.
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