awesome-jev: 805 verified examples of TypeSafe AI's Jev decision model indexed by decision, not blog

Social·Community

awesome-jev: curated list of projects built on TypeSafe AI's Jev typed-decision model

A curated GitHub repository, awesome-jev, aggregates public projects, integrations, and discussions built on Jev, TypeSafe AI's System One model for typed decisions. Jev is not a chat model: it takes unstructured state plus a typed question and returns a typed decision — a choice, score, or boolean with confidence — positioning it as a drop-in decision layer for classification, routing, scoring, verification, and agent guardrails. The list gives practitioners a fast-scanning field guide to which decision patterns transfer across industries.

A curated GitHub repository, awesome-jev, aggregates public projects, integrations, and discussions built on Jev, TypeSafe AI's System One model for typed decisions. Jev is not a chat model: it takes unstructured state plus a typed question and returns a typed decision — a choice, score, or boolean with confidence — positioning it as a drop-in decision layer for classification, routing, scoring, verification, and agent guardrails. The list gives practitioners a fast-scanning field guide to which decision patterns transfer across industries.

Social·Community

CLM: open Qwen3-8B projection head claims full API parity with TypeSafe AI's Jev at 4x-13x speed

A r/LocalLLaMA post argues that CLM, a new open-weights projection head built on Qwen3-8B, achieves functional parity with TypeSafe AI's proprietary Jev decision API while offering 4x–13x faster latency and free self-hosting. CLM implements Jev's full primitive set (Choice, Noul, Score) and reports state-of-the-art verifier scores when fine-tuned, though it trails Jev in zero-shot generalization and context length.

A r/LocalLLaMA post argues that CLM, a new open-weights projection head built on Qwen3-8B, achieves functional parity with TypeSafe AI's proprietary Jev decision API while offering 4x–13x faster latency and free self-hosting. CLM implements Jev's full primitive set (Choice, Noul, Score) and reports state-of-the-art verifier scores when fine-tuned, though it trails Jev in zero-shot generalization and context length.

During Jev launch week, Latent Space published an interview with Diogo Almeida, CEO of TypeSafe AI and InstructGPT co-author, on why he built a 'System One' model for production software rather than chat. Jev's launch video drew ~40M views, and its core training technique, Reinforcement Learning for Calibrated Decisions (RLCD), optimizes for calibrated probabilities instead of human preference feedback. Practitioners get official patterns, cookbooks, and a coding-agents guide, plus use cases spanning computer use, games, linting, and analytics.

AI Advances publishes guide to Jev, a typed probability-driven decision model

Simon Willison released llm-typesafe 0.1a0, a plugin for his LLM CLI tool that adds support for TypeSafe AI's new Jev model. The plugin supports three answer types — yes/no 'noul' questions, choice questions, and scoring questions — returning structured JSON output. It gives LLM users CLI access to Jev for classification-style tasks.