Is this list an official TypeSafe resource I can build against?

No. The digest states it is a community resource not affiliated with TypeSafe AI, so confirm behavior against official docs before relying on it for integration details.

Are the benchmark numbers real independent results?

No. The list keeps a separate benchmarks section and notes company figures are TypeSafe claims unless marked independent, so read vendor and independent results separately.

Do I need a TypeSafe signup if I use a gateway path?

The digest lists gateway pass-throughs including Vercel AI Gateway, OpenRouter, Cloudflare Workers AI, Netlify AI Gateway, and LiteLLM that need no TypeSafe signup.

Can I try the examples before getting an API key?

Yes. The digest notes runnable examples ship with an offline --demo mock mode needing no key, while real calls need a key and follow the documented request shape.

What is this list and its license?

A community-curated resource for Jev, MIT licensed and not affiliated with TypeSafe AI. Company figures are TypeSafe's own claims unless marked independent.

What does the spec sheet say?

Model alias jev-latest to jev-1.13.0, endpoint POST https://api.typesafe.ai/v1/systemone, 64k tokens per request (32k for state plus the longest question), up to 255 Choice options, 70-500 ms latency, and $0.042 per million input tokens with output free.

What does it say about access and training?

Jev is not trained on customer requests and has no per-account fine-tuning. Early access opened to all on Sept 20, 2026 with $5 free credit, then paused on Sept 22 under demand, and gateway pass-throughs need no TypeSafe signup.

CommunityJev Guides & Articles

Awesome Jev — Awesome-llms-labs

Community-curated Awesome list for Jev covering primitives, quickstart, specs, SDKs, gateway paths, benchmarks, limits, and use cases.

See highlights

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Stars: 0
License: MIT
Published by: Community
Author: Awesome-llms-labs
Last verified: Oct 2026

Awesome-llms-labs/awesome-jev

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Awesome list for Jev — TypeSafe AI's decision-only System One model: typed decisions (Choice, Score, Noul) with calibrated probabilities. Guides, recipes, runnable examples, honest benchmarks, community projects.

Forks: 1
Last pushed: Updated Sep 2026

Repo stats from the GitHub API, cached Oct 2026.

This is one of several Jev listings for the same project. The canonical entry is Awesome Jev — yibie.

About this project

Jev doesn't write. You send it a state (text or JSON) plus a map of typed questions — Choice, Score, or Noul — and it evaluates every question in one parallel pass, returning typed values with probabilities and confidence your code can branch on directly. Launched in early access on September 15, 2026, it's the first model in a new category TypeSafe calls System One models (a nod to Kahneman's fast, intuitive System 1 thinking).

Quoted from the project's README. Read the full README.

Tagscommunity github

Works forJev for Scoring Jev for Evaluation

Used inJev for Software & DevTools

Highlights

Explains Choice, Score, and Noul primitives evaluated in parallel in one forward pass.
Shows curl plus official Python and TypeScript SDK calls against POST /v1/systemone.
Lists gateway access via Vercel, OpenRouter, Cloudflare Workers AI, Netlify, and LiteLLM.
Ships runnable examples with offline --demo mock mode plus getting-started and recipe guides.

Watch out

Link collection, not runnable code; vendor figures are TypeSafe claims unless marked independent in the list. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

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Guide plus runnable cookbooks for deciding when Jev fits, favoring decomposed atomic questions with confidence gating over single vague prompts.

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Jev and System One Models: Calibration Beats Accuracy

Thread on Kartik Pansuriya's essay arguing calibrated probabilities matter more than headline accuracy for production classifiers.

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Using Jev for evals in Datadog

Datadog's walkthrough of one Jev rubric that scores every criterion in a single request, then runs as online evals on live spans and as offline experiments.

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Jev can't be calibrated

Alex Molas argues that a probability fit on TypeSafe's data is not calibrated on yours, so Jev's numbers should be treated as scores.

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Jev can't be calibrated

The Hacker News thread on Alex Molas's calibration argument, including a commenter who reran the fair-coin and fair-die prompts.

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From the Jev guides

Original write-ups on what this entry is for.

Data Retention, Residency, and the Decision-Only Advantage

Oct 12, 2026

The typed-answer shape removes a category of data risk, not all of it. Zero data retention per surface, the legal set to read, and the controls that remain yours to run.

Jev Guides & Articles#privacy#compliance#legal

Read article

The Unofficial Jev Hosts and Playgrounds

Oct 11, 2026

TypeSafe serves Jev from one API. A second tier of independent sites wraps it with their own keys, credits, and playgrounds. How to read the disclaimers before you paste a key.

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Read article

How Jev's Launch Landed

Oct 11, 2026

The first ten days produced three receptions that rarely meet: a Product Hunt launch, a 620-point Hacker News thread, and a r/LocalLLaMA that doubted the framing. Reading them together.

Jev Guides & Articles#launch#community#reception

Read article

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From the Jev community

Posts from builders shipping with Jev right now.

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