All Jev resources

The canonical introduction: Jev is TypeSafe's flagship model and the first System One model. Send state and typed questions, get structured answers your code can use directly.

Everything you need to make your first request: install an SDK, get an API key, and send state plus typed questions.

The three TypeSafe question types, the typed answers they return, how to choose between them, and how to ask several in a single call.

How TypeSafe reports certainty, how it differs from probability, and how to use it architecturally to gate and route decisions.

Architectural patterns for System One systems: speculative fan-out, confidence-gated routing, composite scoring, and intent routing.

Design AI-powered software by keeping code in control and giving System One narrow, structured decisions to make.

Explore TypeSafe use cases by industry and turn promising ideas into concrete software workflows.

A 13-question regulatory briefing that shows batching every question into one call is 12.2x cheaper and 10x faster with no change in answers.

One question per query-candidate pair raises top-1 accuracy from 5% to 18% and top-10 accuracy from 38% to 62% on 40 CLERC legal queries.

Screen every message going into and out of an LLM app: a Noul describes possible hazards, a Score rates severity, and code thresholds the probabilities.

Classify documents through deep patent, retail product, biomedical, and source-code hierarchies using parallel beam search over Choice probabilities.

Classify SEC annual reports into 75 industry groups with one Choice each, then read confidence to decide when to fall back to a broader division.

A smart home assistant that uses TypeSafe to evaluate user requests, pick a tool, and fill its arguments from plain language.

Drop-in skill for Claude Code, Codex, and other agent environments that teaches the right way to structure states and questions.

Official TypeScript/JavaScript client with inferred answer types, retries, and a question DSL. `npm install @typesafe-ai/sdk`.

Official sync and async Python client for the TypeSafe API with typed questions, retry policies, and inferred answers. `pip install typesafe-sdk`.

Official drop-in TypeSafeClient replacement backed by OpenAI, Anthropic, and compatible LLM APIs, for comparing Jev against chat models on the same questions.

Get an API key, manage models, and try the playground: three walkthrough lessons plus realistic use cases like resume screening and ticket routing.

The launch story: why TypeSafe built decision models, the side-by-side demo, the Doom and Wikiracing bots, and the published workflow evals.

A tracked index of the whole ecosystem: hundreds of GitHub repos, live sites, and articles, with stars refreshed daily from GitHub.

A community hub for Jev extras: widgets, workflows, and playbooks you can try in the browser and share with others.

Use-case walkthroughs and server-side code samples for calling Jev from your own stack, including OpenRouter and TypeScript examples.

Browse real systems built on Jev and the primitives they send. Copy a state and questions into the playground, or sign in to share your own.

AI Gateway's evaluation modality: call Jev as `typesafe-ai/jev` through the AI SDK's experimental_evaluate API, with structured answers instead of free-form text.

End-to-end guide to a ticket-routing workflow with confidence thresholds, zero-data-retention options, and mock-based tests that need no network call.

Model card for `typesafe-ai/jev`: evaluation type, $0.042 per 1M input tokens, zero output token charge, and a hosted playground.

Access Jev through OpenRouter's decisions endpoint, an alternative route to early access that still uses TypeSafe's question and answer shapes.

A monorepo of roughly 20 latency-focused demos: moderation firehoses, rerankers, inbox triage, macOS launchers, and closed-loop control simulators, each with p50/p95 telemetry.

One AI trade decision every Monad block: Jev evaluates the market state and returns a typed decision the bot acts on, on Kuru's MON-USDC market.

Train a small model that chooses among a changing list of text options, one probability per option in a single pass. Includes Doom, chess, and Wikispeedia demos.

Run a Pydantic AI agent on Jev: each field of the output type becomes one question, the prompt is the state, and confidence comes back per field.

A practical tour with JavaScript examples: typed choices, scores, probabilities, real latencies and costs, the jagged edges, and how to split jobs correctly.

Give Jev a screen snapshot and a task, and it chooses the next action for agent-driven mobile QA. Ships as a CLI you can point at a real app.

Use Jev as a Braintrust judge scorer, then inspect selected answers, confidence, and probabilities alongside the scorer answer. Includes tracing for your own calls.

An agent-first CLI for System One: ask, rank, extract, verify, screen, find, and decide from the shell, with an installable agent skill. Published on PyPI.

An MCP server that puts Jev in the coding loop for Cursor, Codex, and other MCP clients: coding-loop checks, ranking, verification, and review tools.

An open Python toolkit with question builders, confidence gates, tiered routing, a concurrent corpus evaluator, calibration tooling, a CLI, and an MCP server, with published benchmarks.

@jev.fn compiles a Python function definition (parameters, docstring, return annotation) into a state plus typed questions, then validates the answers back into Pydantic models.

An unofficial Go client for the System One API: typed judgments and calibrated probabilities instead of generated text.

Community .NET SDK for the System One API: typed noul, choice, and score questions with structured, confidence-scored answers.

A community PHP and Laravel SDK for the Jev model series, wrapping typed questions and probability answers for the PHP ecosystem.

Eight minimal, zero-dependency examples applying Jev to mechanical and electrical engineering: CAD/CAE/CAM routing, FEM result triage, DFM screening, and BOM alignment.

A community playground with over a hundred use cases, games, and model challenges, with editable prompts and side-by-side comparisons.

Jev-backed auto mode for the Pi coding agent: semantically approves bash, write, and edit tool calls, and fails closed whenever a decision cannot be made.