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All Jev resources

This is the full index of everything Jev: 2441 entries for TypeSafe AI's flagship System One model, from official documentation, SDKs, and cookbooks to community-built clients, MCP servers, agent tools, and write-ups. Every entry is tagged by category, format, and topic, and sorted newest first.

Filter by category or tag, or search titles, descriptions, authors, and repos. Open any card for its detail page — most entries carry a verified deep dive with highlights, caveats, and a quickstart snippet.

Entries
2441
Categories
11
Official
99
Community
2342
Source sites
148
Deep dives
2441

Last updated . Entries are added as they appear and re-checked when they are touched.

Browse Jev by category

11 ways into the ecosystem. Each category page leads with its own shortlist of starting points.

All 2441 Jev resources

Search the full directory, or filter by category and tag.

Showing 5 of 2441 resources

A benchmark providing exact instance-level class probabilities across three simulated text domains: peer review, covert information transfer, and credit risk. Because the data-generating process is defined, exact probabilities and Bayes error are computable, enabling KL-divergence scoring of reported probabilities.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
An audit of whether Jev's confidence reflects missing knowledge rather than chance, using over 15 public datasets and 6 generated task families with paired interventions. Jev's confidence is calibrated on familiar closed-choice tasks but fails as an indicator of missing knowledge.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Introduces Sys1Cal-v1, a dataset of True/False questions about propositions whose exact probability is known by construction, queried through Jev's three primitives and scored by total variation from ground truth. Recovering a suppressed third value raises median soft accuracy from 0.771 to 0.978.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
KITE queries a typed behavioral kernel once per unique state, then runs populations of any size from the table, reserving an expensive flagship model for sparse paired anchors. On Epstein experiments with 9,070 participants, anchors covering 1.7% of states cut effect error by 41%.
Jev Tools & CLIsArticle#community#research#arxiv
An exploratory study of JevForest, a budgeted feature-acquisition policy that aggregates path-dependent proposals from bootstrapped trees and queries Jev for semantic answers. On small held-out samples it beats static and random orderings on AG News but not on TREC.
Jev Practices & PatternsArticle#community#research#arxiv
Community

Start here with Jev

Hand-picked entries the maintainers recommend first, from official primitives to community tools.

Published evaluations of four automation workflows, security incidents, agent trace observability, invoice processing, and customer service, comparing Jev and frontier LLMs as structured workflows versus single prompts.
Jev Practices & PatternsDocs#official#benchmarks#evaluation
Official
6GitHub stars
.NET SDK for the System One API with typed question sets, HttpClientFactory and dependency-injection wiring, plus Microsoft.Extensions.AI guardrail, routing, tool, and evaluator adapters.
Jev Repos & SDKs#community#sdk#csharp
A NativeAOT-ready .NET SDK generated from TypeSafe's OpenAPI definition, with typed question sets, batching, dependency injection, and Microsoft.Extensions.AI integrations.
Jev Repos & SDKs#community#sdk#csharp

Watch: Jev in action

Walkthroughs, tests, and demos from the community.

YouTube: KodeKloud's three-minute primer: what a System One model decides, and why it answers in milliseconds.

YouTube: Caleb Writes Code explains what Jev is and how to call it, in seven minutes.

YouTube: Greg Isenberg and Ryan Vogel on the businesses Jev unlocks, and a path in through the Vercel AI Gateway.

YouTube: CJ from Syntax demos browser use, classification, code review, a model router, and a chat bot with no LLM.

YouTube: Ryan Vogel runs Jev on 100 and then 1,000 emails: category, priority, spam, and reply predictions.

YouTube: AICodeKing tests support routing, refund detection, prompt-injection resistance, and browser automation.

Discussions about Jev

Threads from Reddit and Hacker News about building with Jev.

Hacker News: Show HN: Ad Spend Guard gates budget before the auction, not the creative after.

Show HN: Gating ad budget with a decision modelRead the thread on Hacker News

Reddit: Reranking rebooking options with a decision model, ranked with reasons.

Hacker News: Pick Verify: a single decision at the warehouse pick station catches confusable picks.

Show HN: Verifying robot picks with a decision modelRead the thread on Hacker News

Hacker News: Software Bombe and live Enigma demo where Jev ranks cribs and judges plaintext.

ag_212 pointsShow HN: Jev as the Bletchley Park Analyst: A Software Bombe on EnigmaRead the thread on Hacker News

Hacker News: Show HN for the decide CLI: brew install, file context, and multi-question shell scripts.

vsekhar2 pointsShow HN: Decide – Jev decisions in the shell, scripts, and agent skillsRead the thread on Hacker News

How entries are chosen

Every entry is hand-picked against the same bar, whether it comes from TypeSafe or the community.

  • Public and working — no paywalls, sign-up walls, or dead links.
  • Concrete — it has to be about Jev and the System One API, not generic LLM content.
  • Useful today — something a developer can learn from or use right now.
  • Distinct — if a similar entry exists, the description has to say what makes this one different: a different runtime, a cheaper path, a measured result, or a narrower focus.
  • Documented — repos state their license, and docs are in English or carry an English summary.

Spam, affiliate links, and thin AI-generated content are rejected. New entries are expected to ship with a deep dive: two to five highlights, caveats, a quickstart snippet where one helps, and a verification date. Star counts are refreshed when an entry is touched.

Common questions

Short answers about Jev and how this directory works.

What is Jev?

Jev is TypeSafe AI's first System One model. It takes a state plus typed questions and returns one structured answer per question — a choice, an ordered score, or the probability of yes — with calibrated confidence on Choice and Score answers, so code can branch on the result without parsing generated text.

What is the difference between Choice, Score, and Noul?

They are the three question primitives. Choice picks one of up to 255 options, Score rates on 2 to 10 ordered levels, and Noul answers yes or no. Choice and Score answers carry a confidence between 0 and 1; a Noul answer is the probability of yes.

Which Jev SDKs and client libraries are available?

TypeSafe maintains official JavaScript/TypeScript and Python SDKs plus a Python adapter. The community has built clients for Go, .NET, PHP, Laravel, Rust, Elixir, Ruby, and Scala, along with framework integrations such as DSPy, LlamaIndex, and Home Assistant. All of them are listed in the Repos & SDKs category.

Is the directory free, and is it affiliated with TypeSafe AI?

The directory is free to browse: every entry is hand-picked, and spam, affiliate links, and sign-up walls are rejected. JevDirectory.org is community-run and not affiliated with TypeSafe AI — Jev and System One are TypeSafe AI trademarks.

How often is the directory updated?

Entries are added as new resources appear and re-checked when they are touched. The directory currently holds 2441 entries from 148 source sites, last updated Oct 14, 2026.

What is a verified deep dive?

Most entries include a deep dive: two to five highlight bullets someone would only know from using the resource, a caveats note about limits and requirements, an optional quickstart snippet, and the date the link was last checked. 2441 of the 2441 entries carry one today.

For the canonical details, read the official TypeSafe docs and the API reference.

From the Jev community

Posts from builders shipping with Jev right now.

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