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Back to the labJEVLAB / FIELD NOTES
Fresh from the lab
A dated reading list of newly added resources and JEVLAB NEWS. Come back for what changed, or follow the RSS feed in your own reader.
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Dates show when an item became available in JEVLAB, in UTC, not when its original source was published. Translation-only edits do not count as new resources.
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TipTour
TipTour is an open-source AI-driven pointer assistant for macOS. It demonstrates how to integrate Jev or Gemini Live to enable cursor control through local processing, offering users a hands-free navigation experience on their desktop environment.
x.comDual-Arm Robot with Jev Integration
This project demonstrates a dual-arm robotic system utilizing Jev as a middle-layer decision-making engine. The creator reports that inverse kinematics and physical interactions are handled through dedicated code to ensure efficient and precise operational performance.
x.comjev-cli (shaharia-lab)
This command-line tool integrates with the TypeSafe AI Jev model to perform structured analysis on text. It supports yes/no, multiple-choice, and rubric-based queries, returning calibrated probabilities for use in shell scripts, CI pipelines, and AI agent workflows.
github.comIntent routing
This official TypeSafe guide explains the architectural pattern of intent routing. It demonstrates how to categorize user inputs to direct requests toward specific processing logic, ensuring more accurate and reliable handling of complex conversational tasks.
docs.typesafe.aiawesome-jev
This repository provides a curated, source-backed directory of projects developed using Jev. It serves as a reference for developers exploring TypeSafe AI's System One model, which is designed to facilitate typed decision-making processes in various software applications.
github.comJev Query Planner
This tool utilizes Jev to optimize Postgres query execution. The creator reports a 12% performance improvement in join order benchmarks achieved through specific tuning adjustments.
x.comtypesafe_sdk
This library provides an idiomatic, type-safe Elixir port of the TypeScript AI SDK. It enables unified LLM integrations, streaming text, structured outputs, and tool calling, while supporting agentic workflows using the Jev model.
github.compadflow-jev-evals
This resource provides a typed-decision benchmark derived from PadFlow land development data. It includes anonymized schemas and a runner designed to test the performance of confidence-calibrated models like TypeSafe Jev against structured decision-making tasks.
github.comPost Scoring with SuperX
SuperX integrates with Jev to automate content evaluation. The creator claims the tool analyzes 61 distinct questions per post in under one second to estimate viral potential.
x.comJev in Security Pipelines
This resource demonstrates the integration of Jev into security workflows. The creator claims that Jev improves pipeline efficiency, reporting a fivefold reduction in costs and faster processing speeds while maintaining higher accuracy compared to smaller models.
x.comAntigravity-mcp-semantic-search-with-TypeSafeAi
This project provides a semantic code search and diff auditing tool for AI coding assistants like Antigravity, Cursor, and Claude Code. It utilizes TypeSafe System One to help developers verify code changes and improve search efficiency within their development workflows.
github.comtypesafe-sdk-go (Tangerg)
This Go SDK provides an interface for the TypeSafe AI API. It enables developers to send typed queries to the service and receive structured probability distributions as output, facilitating integration within Go-based applications.
github.comtypesafe-computer-use
This project demonstrates an automated macOS agent that performs computer tasks by using OCR to read the screen and TypeSafe to classify actions. The creator claims a cost efficiency of approximately $0.0002 per step for these operations.
github.comManifesto
This resource outlines the TypeSafe AI vision for machine-native intelligence. It argues that AI development should prioritize software-integrated logic and functional reliability over conversational capabilities, aiming to build systems specifically designed for complex software environments rather than human-like chat interactions.
typesafe.aiswitchboard
This project provides a guardrail and model router for LLM gateways using TypeSafe's Jev System One model. It demonstrates how to implement independent evaluation metrics for accuracy, calibration, and latency using standard Python libraries.
github.comjev_projects
This 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.
github.comjev-playground
This repository provides a testing environment to benchmark Jev against alternative evaluation models. It focuses on game scenarios featuring explicit states, defined legal actions, and quantifiable outcomes to assess comparative performance.
github.comHunch
Hunch is a GitHub-integrated tool designed to assist with code reviews. It allows developers to define and apply custom quality standards using plain English instructions to analyze and refine their codebase.
github.comjev-git
This tool integrates TypeSafe AI Jev to provide automated semantic checks for Git workflows. It demonstrates a method for executing sub-second pre-commit and pre-push validation gates to maintain code quality.
github.comDasheng
Dasheng is a tool that integrates speech recognition with Jev to provide real-time, word-by-word transcription of spoken audio. It demonstrates a method for live content marking and automated text capture during speech.
github.comJev Pixel Canvas Experiment
This experiment tests Jev's ability to generate visual content on a pixel canvas. The demonstration shows that the model currently struggles to produce recognizable imagery, highlighting specific limitations in its spatial generative capabilities.
x.comjev-tool-runner
This project demonstrates an automated workflow where Jev selects specific developer tools, which then trigger Codex to generate corresponding code. It serves as an experimental integration for task-oriented software development automation.
github.comVisual-Jev
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.
github.comtypesafe-jev
This project demonstrates a decision model for screening CV folders using TypeSafe Jev. It provides a framework for applying typed judgments and editable policies, allowing users to perform free re-scoring of candidate documents based on defined criteria.
github.comjev-chess
This project demonstrates a chess engine integration using TypeSafe AI System One. It provides move evaluation, game classification, and simulated persona-based opponents for interactive play.
github.comjev-reach
This tool integrates Chrome DevTools with MCP to enable automated browser navigation. It demonstrates a workflow where an agent identifies a target location and executes a specific DevTools command to perform tasks.
github.comDrawing, one decision at a time
This experiment explores pixel-level image generation by utilizing parallel decision-making processes. It demonstrates a novel approach to constructing digital visuals through iterative, granular choices rather than traditional rendering methods.
x.comLaunching agents on a canvas
This project introduces a system for deploying agents directly onto digital canvases. The creator claims this approach bypasses traditional, slower LLM processing workflows to enable more rapid agent interaction and deployment within the interface.
x.comjev-browse
This project demonstrates browser automation using Jev as a decision-making model. It provides a framework for integrating TypeSafe AI logic into web navigation tasks, allowing for structured interaction with browser environments.
github.comTypeSafe AI API Reference
This official documentation provides a comprehensive technical reference for the TypeSafe AI Python SDK. It outlines available classes, methods, and parameters designed to assist developers in integrating TypeSafe functionality into their applications.
docs.typesafe.aiIntroducing System One Models and Jev
This launch post details the architecture and RLCD training methods behind System One models. It provides practical demonstrations using Doom and Wikiracing to showcase capabilities, alongside pricing information and a comprehensive FAQ for developers.
typesafe.aijev-review-action
This GitHub Action utilizes Jev to automate the triage and classification of pull requests. It demonstrates how developers can integrate automated decision-making workflows directly into their repository management processes to streamline code review tasks.
github.comjevgpt
This project demonstrates a chatbot implementation using Jev, a TypeSafe AI model architecture. It explores how autoregressive methods can be applied to a model that does not natively generate text, providing a unique approach to conversational interaction.
github.comguesswork
This 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.
github.comchess-jev
This web-based project features a 3D chess environment where Jev can play both sides of a match. Users have the option to observe the automated gameplay or intervene to take control of a side during the game.
chess-jev.loomens.comAI Hedge Fund with Jev
This project demonstrates an AI-driven hedge fund architecture using Jev. The creator claims the system facilitates rapid and cost-effective trading decision-making processes for automated financial markets.
x.com
