typesafe

TypeSafe's Jev decision model now supports multimodal input with a new Box question type and assets field, returning structured, schema-safe answers (choice, noul, score, box) with probabilities directly usable in code instead of parsing free-form LLM JSON output.

Jev by TypeSafe is a decision component for enterprise AI that provides typed judgments (Choice, Score, Noul) to replace text generation in workflows, enabling better governance and validation.

This article provides a detailed guide on using the Jev AI model cost-effectively through batch queries and its stateful billing mechanism, with specific configurations and code examples.

The article analyzes the potential for OpenAI to replicate TypeSafe's Jev model, which uses LLMs as classifiers, and explores the competitive implications in the AI industry.

This article introduces TypeSafeClassifier, a LangChain Runnable that provides typed decisions with calibrated probabilities for model routing and tool-risk gating in agent workflows.

Jev is a new AI model from TypeSafe, focused on decision-making and analysis. It is now available to all users and can be integrated into coding agents like Claude Code and Codex via Skills, enhancing coding decision capabilities at a low cost.

This tweet promotes AskJev.net, a web service that allows users to ask questions and receive answers from an AI named Jev, integrated with TypeSafe's AI tools.

This GitHub project uses the Jev model to create a lousy chatbot with various sampling strategies, developed with Claude's help as a fun experiment.

The article discusses testing and evaluating TypeSafe's System One AI model in the context of the 2048 game.