Evidence-backed use cases, patterns, prompts, and starter code for TypeSafe Jev — a System One model for fast, typed, confidence-aware decisions in software.
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Sep 23, 2026 - Python
Evidence-backed use cases, patterns, prompts, and starter code for TypeSafe Jev — a System One model for fast, typed, confidence-aware decisions in software.
Awesome Jev: a source-backed field guide to TypeSafe's System One model, with SDKs, live demos, agent tools, and independent evaluations.
A curated, source-backed list of projects built with Jev, TypeSafe AI's System One model for typed decisions.
🔥🔥 Papers, open reproductions and independent evaluations behind System One models and Jev.
Evidence-backed use cases, patterns, and guidance for building with Jev, TypeSafe AI's System One model. Every claim is labeled and sourced.
200+ plug-and-play Jev recipes: small, calibrated AI decisions that route, grade, gate, compare, and label text for agents, RAG, support, code review, and music. Import from JavaScript or TypeScript, or call the CLI with JSON from any language.
Observable browser stealth game: Jev makes typed guard judgments while deterministic code owns the world.
Fast, typed, calibrated evaluations for LLM and agent outputs, powered by Jev — with simple, framework-agnostic Python APIs
Community Java SDK for Jev, TypeSafe's System One model: typed questions in, typed answers with calibrated probabilities out. Java 17+, Spring Boot starter (unofficial)
An idiomatic, type-safe Elixir port of the official TypeScript AI SDK (ai / ai-sdk) providing unified LLM integrations, streaming text and structured outputs, tool calling, and agentic workflows. Jev is their current flagship model and is the first System One model.
ReflexBench — open benchmark and evaluation harness for System One models and typed decision engines
Community-maintained Go SDK for the TypeSafe AI System One evaluation API, with typed questions, fluent builders, retries, and examples.
Self-hosted prompt monitoring with System One Models
AX-first computer use for AI agents on macOS with optional Jev (TypeSafe System One) semantic guards: calibrated target/input judgments before an irreversible action, decisions kept in code. Accessibility-tree targeting, window-scoped input, clipboard-safe paste, read-back verification. Ships a pip CLI, a pi package and a DeepSeek Harness plugin.
Open schemas, recipes, templates, examples and agent tooling for Brida Reflex.
Code and data for evaluating Jev, a System One model, on scientific decisions and how its choices affect downstream results.
Independent benchmark data for TypeSafe's Jev (System One model) vs LLMs: accuracy, calibration, cost. Boards + per-decision logs, CC-BY-4.0
Find files by describing them in plain English: find(1) with a semantic --like predicate, answered by TypeSafe.ai's jev model
decision-first data cleaning system powered by Jev
Minesweeper where deterministic logic does the provable work and TypeSafe Jev is consulted only when the board forces a guess.
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