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<b>A curated list of Jev use cases, projects, SDKs, tools, and learning resources.</b><br>
<a href="https://typesafe.ai/blog/introducing-system-one-models-and-jev">Jev</a> is the first System One model from <a href="https://typesafe.ai">TypeSafe AI</a> — an AI model that returns typed decisions (Choice, Score, Noul) with calibrated probabilities instead of generated text.
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> **Looking for real-world Jev use cases with numbers?** [madewithjev.com](https://madewithjev.com) is a directory of what people are building with Jev — every build with the cost, latency, and source the author reported. [Submit yours →](https://madewithjev.com/submit)
Jev launched in early access on **September 15, 2026**. This list is unofficial and not affiliated with TypeSafe AI. Pull requests are welcome — the ecosystem is days old and growing fast.
## Contents
- [What is Jev?](#what-is-jev)
- [Jev vs LLM](#jev-vs-llm)
- [Pricing, limits, and access](#pricing-limits-and-access)
- [Quick start](#quick-start)
- [Official resources](#official-resources)
- [Community](#community)
- [Featured builds with real numbers](#featured-builds-with-real-numbers)
- [Jev guides by madewithjev.com](#jev-guides-by-madewithjevcom)
- [What people use Jev for, by job](#what-people-use-jev-for-by-job)
- [SDKs and clients](#sdks-and-clients)
- [Applications](#applications)
- [Browser and computer-use agents](#browser-and-computer-use-agents)
- [Search, retrieval, and data](#search-retrieval-and-data)
- [Developer tools and code review](#developer-tools-and-code-review)
- [Model routing](#model-routing)
- [Business and vertical apps](#business-and-vertical-apps)
- [Robotics and hardware](#robotics-and-hardware)
- [Demos and games](#demos-and-games)
- [Agent tools and MCP servers](#agent-tools-and-mcp-servers)
- [Use cases by industry](#use-cases-by-industry)
- [Patterns](#patterns)
- [Cookbooks](#cookbooks)
- [Benchmarks and evaluations](#benchmarks-and-evaluations)
- [Research and open models](#research-and-open-models)
- [Articles and coverage](#articles-and-coverage)
- [Discussions](#discussions)
- [FAQ](#faq)
- [More guides, integrations and lists](#more-guides-integrations-and-lists)
- [Related lists](#related-lists)
- [Contribute](#contribute)
## What is Jev?
Large language models generate text. Jev does not. It evaluates typed *questions* against a *state* and returns values your code can branch on, sort by, and route with — plus calibrated probabilities and confidence. TypeSafe AI calls this model class a **System One model**: fast, structured decisions that software can use directly, trained with **RLCD (Reinforcement Learning for Calibrated Decisions)**.
```
text or JSON state + typed questions → constrained answers + probabilities → your code
```
Jev exposes three question types. Questions in one request run in parallel against the same state.
| Question | Goal | Returns |
|---|---|---|
| [Choice](https://docs.typesafe.ai/primitives/choice) | Pick one option from a list | `choice`, `probabilities`, `confidence` |
| [Score](https://docs.typesafe.ai/primitives/score) | Rate the state on a rubric | `score`, `probabilities`, `confidence` |
| [Noul](https://docs.typesafe.ai/primitives/noul) | Is this statement true? | `noul` (0–1) |
Use it to classify, route, score, detect, rank, extract, verify, and gate automation — anywhere you would otherwise write a brittle regex or pay an LLM to return JSON you then have to parse. Questions describe judgments; your code owns composition, thresholds, and side effects.
## Jev vs LLM
From TypeSafe's [launch post](https://typesafe.ai/blog/introducing-system-one-models-and-jev):
| | Existing LLMs | System One + Jev |
|---|---|---|
| Optimized with | RLHF / RLVR | RLCD (Reinforcement Learning for Calibrated Decisions) |
| Optimizes for | Human preference; verifiable rewards | Calibrated decisions with honest probabilities |
| Output | Strings that need parsing and validation | Type-safe structured values, defined in advance |
| Sampling | Sequential, token by token | Parallel, all outputs in a single query |
| Cost | $0.20–$10 / MTok input, output ~5x more | $0.042 / MTok input, output free |
| Speed (vendor-reported) | 3–329 s end-to-end for frontier models | 70–500 ms end-to-end |
| Confidence | Tends to be overconfident if asked | Calibrated confidence on every answer |
| Best at | Chat, writing, code, open-ended reasoning | Decisions inside software: classify, route, score, verify |
Jev is not a replacement for an LLM. When you need free-form text, pair them: let Jev route, retrieve, verify, or guard the call, then let the LLM write inside the boundaries your code enforces.
## Pricing, limits, and access
Snapshot reviewed September 18, 2026. Check [Models](https://docs.typesafe.ai/models) for current values — limits can change dynamically.
| Item | Current detail |
|---|---|
| Model alias | `jev-latest` (current version: `jev-1.13.0`) |
| Endpoint | `POST https://api.typesafe.ai/v1/systemone` |
| Price | $0.042 / 1M input tokens; output tokens free |
| Listed limits | 250,000 tokens/second, 1,200 requests/minute |
| Choice cardinality | Up to 255 options per Choice question |
| Modalities | Text only — no images, audio, or video |
| Direct access | Early access via waitlist at [typesafe.ai](https://typesafe.ai) |
| No-waitlist access | [Vercel AI Gateway](https://vercel.com/ai-gateway/models/jev) (`typesafe-ai/jev`) and [Cloudflare Workers AI](https://developers.cloudflare.com/ai/models/typesafe/jev/) (`typesafe/jev`) |
## Quick start
Get an API key from the [TypeSafe console](https://console.typesafe.ai/settings/keys), then:
**Python** — `pip install typesafe-sdk`
```python
from typesafe_sdk import Choice, Noul, Score, TypeSafeClient
state = {"ticket": "I was charged twice and need the duplicate refunded today."}
with TypeSafeClient() as client: # reads TYPESAFE_API_KEY from the environment
response = client.system_one(
state=state,
questions={
"intent": Choice(
instructions="What is the customer's main request?",
criteria={
"refund": "The customer wants money returned.",
"technical_help": "The customer needs a bug or integration fixed.",
"information": "The customer is asking for information only.",
"other": "None of the other options clearly fits.",
},
),
"is_urgent": Noul(instructions="Does the ticket explicitly communicate time pressure?"),
"frustration": Score(
instructions="How frustrated does the customer appear?",
criteria=["Calm and neutral", "Concerned but civil", "Very angry or using strong language"],
),
},
)
print(response.answers["intent"].choice) # "refund"
print(response.answers["is_urgent"].noul) # 0.0–1.0
print(response.answers["frustration"].score) # probability-weighted rubric position
```
**JavaScript / TypeScript** — `npm install @typesafe-ai/sdk`
```ts
import { choice, noul, score, TypeSafeClient } from "@typesafe-ai/sdk";
const client = new TypeSafeClient();
const result = await client.systemOne({
state: { ticket: "I was charged twice and need the duplicate refunded today." },
questions: {
intent: choice("What is the customer's main request?", {
refund: "The customer wants money returned.",
technical_help: "The customer needs a bug or integration fixed.",
information: "The customer is asking for information only.",
other: "None of the other options clearly fits.",
}),
isUrgent: noul("Does the ticket explicitly communicate time pressure?"),
},
});
```
On Vercel AI Gateway, use `experimental_evaluate` from the AI SDK with the model id `typesafe-ai/jev`. See the [official quick start](https://docs.typesafe.ai/introduction/quickstart) for details.
## Official resources
- [TypeSafe AI](https://typesafe.ai) - Company homepage, waitlist, and product overview.
- [Introducing System One Models and Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) - Launch post by founder Diogo Almeida: architecture, RLCD, pricing, Doom and Wikiracing demos, FAQ.
- [Documentation](https://docs.typesafe.ai/introduction) - Introduction, primitives, patterns, API, and SDKs. Start with the [quick start](https://docs.typesafe.ai/introduction/quickstart).
- [Playground](https://console.typesafe.ai/playground) - Paste a state, add questions, see typed answers in the browser.
- [API keys](https://console.typesafe.ai/settings/keys) - Dashboard for TypeSafe API keys (`TYPESAFE_API_KEY`).
- [HTTP API reference](https://docs.typesafe.ai/api) - `POST https://api.typesafe.ai/v1/systemone`.
- [Models, prices, and limits](https://docs.typesafe.ai/models) - Aliases, versions, and rate limits.
- [Workflow evals](https://evals.typesafe.ai) - Published eval methodology and per-model results on automation workflows.
- [GitHub org](https://github.com/typesafe-ai) - Official open-source repositories.
- [Agent skill](https://docs.typesafe.ai/agent-skill) - Drop-in skill for Claude Code, Codex, and other coding agents ([typesafe-ai/skills](https://github.com/typesafe-ai/skills)).
- [Jev 1.13 jaggedness](https://docs.typesafe.ai/model-jaggedness/jev-1.13) - Known failure modes of the current public model.
- [Manifesto](https://typesafe.ai/manifesto) - The case for machine-native intelligence built for software, not conversation.
- [The Bitterest Lesson](https://typesafe.ai/blog/bitterest-lesson) - Why optimizing the wrong task can dominate gains from scale.
- [AI: too good to be true, too bad to be useful](https://typesafe.ai/blog/ai-too-good-to-be-true-too-bad-to-be-useful-typesafe-ai) - Against preference-optimized chat models for automation.
- [Jev on Vercel AI Gateway](https://vercel.com/ai-gateway/models/jev) - Hosted `typesafe-ai/jev` for the AI SDK's `experimental_evaluate`, no TypeSafe waitlist required.
- [Jev on Cloudflare Workers AI](https://developers.cloudflare.com/ai/models/typesafe/jev/) - `typesafe/jev` via `env.AI.run`, with worked support-routing and risk-escalation examples.
## Community
- [Discord](https://discord.gg/typesafe) - Official TypeSafe server; builder demos live in Show and Tell.
- [X @typesafeai](https://x.com/typesafeai) - Product and research updates.
- [X @CompleteSkeptic](https://x.com/CompleteSkeptic) - Founder Diogo Almeida.
- [LinkedIn](https://www.linkedin.com/company/typesafe-ai/) - Company announcements and hiring.
## Featured builds with real numbers
Production-shaped uses with the cost and latency their authors reported. Each links to a full breakdown on [madewithjev.com](https://madewithjev.com), the Jev use-case directory that maintains this list.
| Build | What it does | Reported numbers | Source |
|---|---|---|---|
| [Jev plays Doom](https://madewithjev.com/builds/jev-plays-doom) | Game loop asking Jev what to do ~10 times a second | ~10 queries/s, ~$7/hour | [X](https://x.com/CompleteSkeptic/status/2099925687465570372) |
| [jev-ultrafast](https://madewithjev.com/builds/jev-ultrafast) | Browser Use's agent with the next-action decision moved to Jev | ~2.9k stars | [GitHub](https://github.com/browser-use/jev-ultrafast) |
| [Flight search with Browser Use](https://madewithjev.com/builds/browser-use-flights) | Booking flow driven end to end | ~7 s, ~$0.004 | [X](https://x.com/gregpr07/status/2100411066966749359) |
| [Stagehand on a remote browser](https://madewithjev.com/builds/stagehand-remote-browser) | Browser tasks at a tenth of a cent each | ~$0.001/task | [X](https://x.com/kylejeong/status/2100622054945095934) |
| [jev-trader](https://madewithjev.com/builds/jev-trader) | Buy/sell decided inside a 300 ms Monad block, on Kuru's order book | 300 ms decision window | [GitHub](https://github.com/jarrodwatts/jev-trader) |
| [Triage across 1,500 emails](https://madewithjev.com/builds/inbox-triage-1500-emails) | A full inbox sorted in one pass | ~1,500 emails | [X](https://x.com/ryanvogel/status/2100042788851101842) |
| [Every's editorial vibe check](https://madewithjev.com/builds/every-editorial-judgments) | 37 documents, 21 questions each; 6 of 7 planted defects caught | 1,709 judgments, <$0.01, 0.35 s median | [Every](https://every.to/also-true-for-humans/mini-vibe-check-typesafe-s-jev-judged-everything-i-ve-written-in-0-7-seconds) |
| [1kpapers](https://madewithjev.com/builds/1kpapers) | A corpus classified by topic and published as a site | 1,018 papers | [Site](https://www.1kpapers.com/) |
| [Jev plays chess](https://madewithjev.com/builds/jev-plays-chess) | Legal moves as a Choice, compared with reasoning models | illegal moves impossible by construction | [dev.to](https://dev.to/maximsaplin/typesafe-jev-played-chess-and-landed-next-to-reasoning-models-28ga) |
| [3,282 posts, eight questions each](https://madewithjev.com/builds/x-post-analysis) | Ian Nuttall's X back catalogue scored for what travels | 4.25M tokens, $0.1282, 8 m 34 s | [X](https://x.com/iannuttall/status/2100668908227162567) |
| [Post scoring with SuperX](https://madewithjev.com/builds/superx-post-scoring) | 61 questions about a draft before it ships | ~1 s, $0.0004/draft | [X](https://x.com/robj3d3/status/2100722975645598191) |
| [724 competitor ads, broken down](https://madewithjev.com/builds/competitor-ad-teardown) | Hook, format, offer, CTA per ad across 37 brands | ~40 s, ~$0.09 | [X](https://x.com/TheMattBerman/status/2100654891756589230) |
| [typesafe-computer-use](https://madewithjev.com/builds/typesafe-computer-use) | macOS computer use, one typed decision per step | ~$0.0002/step | [GitHub](https://github.com/awlevin/typesafe-computer-use) |
| [jev-drone](https://madewithjev.com/builds/jev-drone) | Tactical judgment loop flying on hardware | control at 2.5 Hz | [GitHub](https://github.com/RomanSlack/jev-drone) |
| [Wikiracing](https://madewithjev.com/builds/wikiracing) | Pick one link out of thousands until you arrive | 255-option Choice ceiling | [TypeSafe](https://typesafe.ai/blog/introducing-system-one-models-and-jev) |
| [100,000 viral posts in 20.4 seconds](https://madewithjev.com/builds/viral-post-analyser) | Fourteen yes-or-no questions per post, over a whole archive | 100,000 posts, 20.4 s, $0.67 | [X](https://x.com/0xMovez/status/2101325703635435523) |
| [Tocsin](https://madewithjev.com/builds/tocsin) | 22.8M log lines grouped into patterns, then one question each | 22.8M lines, ~6 min, $0.64 | [GitHub](https://github.com/TPAteeq/tocsin) |
| [723 ads, 30 personas, 22 cents](https://madewithjev.com/builds/ad-focus-group) | One synthetic focus group over a whole ad library | 21,690 decisions, $0.22 | [X](https://x.com/TheMattBerman/status/2101439340588974096) |
| [1,891 ads in 19 seconds](https://madewithjev.com/builds/maxfusion-ad-library) | Every live ad in a competitor's library tagged by hook, offer and format | 1,891 ads, 19 s, $0.12 | [X](https://x.com/aresotik/status/2100949805573030378) |
| [2,300 AI papers in about 83 seconds](https://madewithjev.com/builds/2300-papers-in-83-seconds) | A paper corpus screened and labelled in one pass | 2,300 papers, ~83 s, ~$0.14 | [X](https://x.com/KennyChinaTech/status/2102201611502448865) |
| [700 leads scored in 40 seconds](https://madewithjev.com/builds/lead-outreach-scoring) | ICP fit and outreach quality before a human reads the list | 700 leads, 40 s, $0.09 | [X](https://x.com/romanbuildsaas/status/2100891604735099103) |
| [24,000 Hacker News posts in two minutes](https://madewithjev.com/builds/venice-hacker-news-classification) | A whole forum archive clustered into twelve categories | 24,000 posts, ~2 min | [X](https://x.com/sabrinaesaquino/status/2101102660997017747) |
| [Jev blocks a $50,000 transfer](https://madewithjev.com/builds/jev-blocks-transfer) | One Noul in front of an irreversible action | 95% irreversible risk, 94% sensitivity — it refused | [X](https://x.com/fluixoo/status/2102680895425503354) |
| [A second-hand shopping agent](https://madewithjev.com/builds/second-hand-shopping-agent) | Listing matched to a shopper, per item, in a loop | ~26 listings/min, 406 ms, $0.00085 | [X](https://x.com/AlanDaitch/status/2100757989212754085) |
| [900 images in 40 seconds](https://madewithjev.com/builds/ocr-image-classifier) | OCR output classified one image at a time | ~900 images, 40 s | [X](https://x.com/fayazara/status/2100953838891192789) |
| [40,000 wallets screened in four seconds](https://madewithjev.com/builds/wallet-filter-40k) | Addresses judged against a rule set at scale | 40,000 wallets, 4 s | [X](https://x.com/burstingbagel/status/2102592197476626486) |
| [Beating the Ender Dragon in Minecraft](https://madewithjev.com/builds/minecraft-ender-dragon) | Jev and Astra play a full Minecraft run to the end | 8m 43s, $0.97 | [X](https://x.com/rronak_/status/2101544156757950697) |
| [Laya, 421M parameters, local](https://madewithjev.com/builds/laya-421m-local-decisions) | An open-weight decision model on a laptop | 86.5 decisions/s, P50 ~9 ms | [X](https://x.com/the_mdfazal/status/2102012970373111933) |
| [von](https://madewithjev.com/builds/von-sub-15ms) | A local decision runtime built for latency | under 15 ms per decision | [GitHub](https://github.com/wfzyx/von) |
| [Tev1 0.8B](https://madewithjev.com/builds/tev1-local-classifier) | Together AI's open Jev-style classifier | 0.8B params, ~50 ms | [X](https://x.com/nutlope/status/2103183092428984413) |
All figures are as reported by each author, not measured by this list. → [Browse the full directory at madewithjev.com](https://madewithjev.com)
## Jev guides by madewithjev.com
The directory that maintains this list also writes the explainers. Every figure in them is read from the same builds — 729 of them as of this writing — so they can be cited, and the counts are recomputed as the directory grows. Start at the [guides index](https://madewithjev.com/guides).
**The basics**
- [Jev explained in plain English](https://madewithjev.com/jev-explained) - No jargon: Jev answers multiple-choice questions about a piece of text, in about the time it takes to blink, for about a hundredth of a cent. What that changes, and what it cannot do.
- [What is Jev?](https://madewithjev.com/what-is-jev) - The full explainer: what a System One model is, the three question types, what a call returns, what it costs and where it fails.
- [What is Jev Engineering?](https://madewithjev.com/what-is-jev-engineering) - The name for the split — an LLM writes, Jev decides, code acts — and the rules the builds on this list have in common.
- [Jev vs an LLM](https://madewithjev.com/jev-vs-llm) - The same job done both ways, with the figures each builder published, including the cases where the larger model still won.
- [Jev pricing](https://madewithjev.com/jev-pricing) - $0.042 per million input tokens, output free, and the median published cost per decision across the directory.
- [The Jev Build Report](https://madewithjev.com/jev-statistics) - The directory's own count: every public build, the median cost per decision, the median decision time, and the stars and languages of every repository created since launch. Free to cite, with the rows as JSON.
**Building with Jev**
- [How to use Jev](https://madewithjev.com/how-to-use-jev) - Your first call line by line, then the SDKs, the gateways and every video walkthrough.
- [Jev with Claude Code](https://madewithjev.com/jev-with/claude-code) - Routing and compaction inside Claude Code, the builds behind the 200× headline, and where that ceiling stops.
- [Jev and MCP](https://madewithjev.com/jev-mcp) - The MCP servers that let Claude Code, Cursor and Codex ask for a typed judgment while they work.
- [Jev agentic harness](https://madewithjev.com/jev-agentic-harness) - The decision layer: the model proposes, Jev answers, code acts, with a receipt per step.
- [Jev agent orchestration](https://madewithjev.com/jev-multi-agent) - Who goes next, is this good enough, should we retry, and when a person has to step in.
- [Open-source Jev](https://madewithjev.com/open-source-jev) - There are no open weights. These are the models that answer typed questions on your own machine, by family.
- [Can you fine-tune Jev?](https://madewithjev.com/jev-fine-tune) - What people train instead: a $17 run on Qwen3.5 4B, a $95 open reproduction, and every replica in the directory.
**By job**
- [Jev use cases](https://madewithjev.com/jev-use-cases) - Every catalogued build, grouped by the job Jev does inside it rather than by category.
- [Jev as a judge](https://madewithjev.com/jev-as-a-judge) - Jev in place of an LLM judge: evals, rubrics, and what the confidence score is and is not.
- [Jev for SEO](https://madewithjev.com/jev-for-seo) / [Jev for ads](https://madewithjev.com/jev-for-ads) / [Jev for marketing](https://madewithjev.com/jev-for-marketing) - The three verticals with the most published numbers.
**Elsewhere**
- [Qué es Jev: el modelo System One de TypeSafe AI](https://www.linkedin.com/pulse/qu%C3%A9-es-jev-el-modelo-system-one-de-typesafe-ai-que-en-kraayenbrink-g65ff/) - (Spanish) Our explainer on LinkedIn: what a System One model is, what Jev decides, and where it fits.
- [Sift](https://madewithjev.com/sift) - Our own Mac app: sorts, labels and marks your Gmail with a System One model running on your machine.
## What people use Jev for, by job
The same builds as the table above, grouped by the job Jev does inside them rather than by industry. [Every catalogued build, grouped this way →](https://madewithjev.com/jev-use-cases)
| Job | What Jev decides | One published result |
|---|---|---|
| Sort a pile too big to read | One question, or twenty, asked of every item in a corpus | 100,000 posts, 20.4 s, $0.67 |
| Triage a queue into buckets | Email, tickets, PRs and leads sorted by what each needs next | 500 emails for 3.5 cents |
| Choose an agent's next action | Given this page, which element to act on | a flight search in ~7 s, ~$0.004 |
| Decide inside a frame | Games, control loops and price feeds on a deadline | Tetris at ~0.3 s a move |
| Score something before it ships | A draft, ad, lead or page graded against a rubric | 61 questions in ~1 s, $0.0004 |
| Block the risky step | One Noul in front of an action that cannot be undone | a $50,000 transfer refused at 95% risk |
| Shrink what the LLM reads | Score each piece of context and drop the stale ones | a Claude session from ~1M to 86K tokens |
| Judge every row where the data is | A Jev call written as a SQL function | 129 rows in ~1 s, in SQL |
| Pick what the screen shows | The next component, look or result from a catalogue | 6,241 companies ranked in ~1 s |
## SDKs and clients
Official first, then community clients. Community packages are not affiliated with TypeSafe.
**Official**
- [Python SDK](https://github.com/typesafe-ai/typesafe-sdk-python) - `pip install typesafe-sdk`. [Docs](https://docs.typesafe.ai/sdk/python).
- [JavaScript / TypeScript SDK](https://github.com/typesafe-ai/typesafe-sdk-js) - `npm install @typesafe-ai/sdk`. [Docs](https://docs.typesafe.ai/sdk/javascript).
- [System One adapter (Python)](https://github.com/typesafe-ai/system-one-adapter-python) - Drop-in `TypeSafeClient` replacement backed by LLM APIs, to compare Jev against chat models on the same questions. `pip install system-one-adapter`.
- [Vercel AI SDK provider](https://ai-sdk.dev/providers/ai-sdk-providers/typesafe-ai) - `@ai-sdk/typesafe-ai` with `experimental_evaluate`; use `typeSafeAi.evaluationModel('jev-latest')` or the Gateway id `typesafe-ai/jev`.
**Community, by language**
- Go: [jev-go](https://github.com/Gaurav-Gosain/jev-go) - `go get github.com/Gaurav-Gosain/jev-go`. Also [Stumble/jev-go](https://github.com/Stumble/jev-go) - dependency-free, works against TypeSafe direct and Vercel AI Gateway, with an interactive CLI and an installable agent skill.
- Elixir: [typesafe_sdk](https://github.com/nshkrdotcom/typesafe_sdk) - Hex package for `system_one` and model listing. Also [Jev (OTP)](https://github.com/dannote/jev) - Jev as a peer GenServer; answers arrive as messages you pattern-match, with network-free tests.
- Ruby: [typesafe-sdk](https://github.com/joshmn/typesafe-sdk) - Ruby 3.1+, retries, thread-safe pooled HTTP. Also [RubyLLM TypeSafe](https://github.com/kieranklaassen/ruby_llm-typesafe) - TypeSafe provider for RubyLLM 2. And [typesafe-ai-rails](https://github.com/GenieRobot/typesafe-ai-rails) - Rails integration with usage telemetry and opt-in confidence policies.
- Rust: [typesafe-ai-rs](https://github.com/gilljon/typesafe-ai-rs) - async and blocking client. Also [Twister915/typesafe-ai](https://github.com/Twister915/typesafe-ai) - observable retries; [typesafe-rs](https://github.com/AbdelStark/typesafe-rs) - latency-focused transport; [s1-rs](https://github.com/AbdelStark/s1-rs) - derive layer for Choice / Score / Noul with confidence gates and network-free tests.
- PHP / Laravel: [typesafe-sdk-php](https://github.com/Butochnikov/typesafe-sdk-php) - typed DTOs and promises. Plus [laravel-typesafe-jev](https://github.com/Butochnikov/laravel-typesafe-jev) - Laravel 12/13 config, facade, scoped DI, and a recording fake.
- Python: [jevclient](https://github.com/AboveColin/jevclient) - async client (`pip install jevclient`), separate from the official SDK.
- Swift: [swift-typesafe](https://github.com/ainame/swift-typesafe) - Swift 6.4 client aligned with the Python SDK 0.6.0 API, including Linux.
- Scala / ZIO: [zio-typesafe-ai](https://github.com/jamesward/zio-typesafe-ai) - ZIO client with a small DSL for noul / choice / score.
- .NET: [typesafe-dotnet-sdk](https://github.com/saibimajdi/typesafe-dotnet-sdk) - typed questions and confidence-scored answers.
- TypeScript: [Advocaat](https://github.com/pithings/advocaat) - small client with tagged helpers for chances, choices, and scores.
- Cloud: [typesafe-on-neon](https://github.com/andrelandgraf/typesafe-on-neon) - Neon Function proxy for the Neon AI Gateway.
## Applications
Open-source projects that put Jev in a real loop. Grouped by what Jev decides.
### Browser and computer-use agents
- [Jev Ultrafast](https://github.com/browser-use/jev-ultrafast) - Browser agent from [Browser Use](https://github.com/browser-use). Jev picks an operation and a DOM element in one request; a small LLM writes text only for `TYPE_TEXT`. Zürich → London on Google Flights in ~7 s. Library, local inspector, and measurements included.
- [Jev for Chrome](https://github.com/chy4pro/jev-for-chrome) - Unofficial Chrome extension (Manifest V3) port of Jev Ultrafast: Jev picks the operation and DOM element in one request, a small text model writes typed values, and it runs in the user's own tabs through OpenRouter, TypeSafe or Cloudflare; includes a 17-task headless-Chromium suite with recorded traces.
- [jev-ego](https://github.com/romaluev/jev-ego) - Browser agent on ego lite: one TypeSafe request picks operation + indexed element; agent-facing observe/act/suggest/step CLI.
- [jev-browser](https://github.com/Ying-Kai-Liao/jev-browser) - An LLM plans the outcome, Jev decides each click/type on a Playwright snapshot (~300 ms/call). Ships as a library, CLI, and MCP server.
- [Jev Browser (Vlad Terin)](https://github.com/vlad-terin/jev-browser) - Agent skill + runtime: Codex plans, Jev selects elements, a runner acts and verifies each step.
- [typesafe-computer-use](https://github.com/awlevin/typesafe-computer-use) - macOS computer-use loop: OCR the screen, Jev classifies the next action, then click. About $0.0002/step.
- [Mobile Jev](https://github.com/droidrun/mobile-jev) - Android agent on Mobilerun: Jev decides each tap. Opens Uber, SFO → Golden Gate, payment screen in ~21 s / 9 actions. No ADB.
- [Unclutter](https://github.com/kitze/unclutter) - Chrome / Firefox extension: Jev classifies nonessential page elements; local template rules hide them on later visits.
- [TypeSafe AdBlock](https://github.com/realZachi/typesafe-adblock) - Chrome extension: Jev judges whether a DOM element is an ad and removes it. BYOK, no backend; a demo, not a real ad blocker.
- [jev-skip](https://github.com/valentynkit/jev-skip) - Browser extension that reads a YouTube video's caption track and paints a per-segment sponsor probability on the seek bar before the intro ends, with no crowdsourced database.
- [Jev Social](https://github.com/socai-io/jev-social) - Local Instagram, TikTok, and LinkedIn research loop: Jev uses Choice to select a platform and a concrete read-only operation from captured evidence, then the socai CLI executes that browser step and returns new evidence.
→ [More agents and browsers on madewithjev.com](https://madewithjev.com/categories/agents-and-browsers)
### Search, retrieval, and data
- [Every](https://github.com/sufianetaouil/every) - Semantic code-search CLI: a yes/no question against every function, ranked by Noul probability.
- [blink](https://github.com/ellipsis-dev/blink) - Codebase search: an ensemble of walkers asks Jev which file answers a natural-language query.
- [Jev Search](https://github.com/superagents-lab/jev-search) - Web search app using Choice and Noul judgments to select sources, time ranges, and query candidates, then rank results retrieved through Search1API. Live demo: [jev.s1.dev](https://jev.s1.dev).
- [neo4jev](https://github.com/jexp/neo4jev) - Neo4j graph navigation: at each node Jev chooses which relationship to follow, with beam search over log-probabilities.
- [jev-bfs](https://github.com/komikat/jev-bfs) - Finds link paths between Wikipedia articles; Jev ranks each page's outgoing links while Python controls the search.
- [hono-jev-router](https://github.com/yusukebe/hono-jev-router) - Experimental Hono router: Jev matches an incoming request to a plain-language route description.
- [sqlite3-jev](https://github.com/mattn/sqlite3-jev) - SQLite C extension: `jev_noul` / `jev_choice` / `jev_score` as SQL functions via libcurl.
- [jev-curate](https://github.com/AkashPriyadarshii/jev-curate) - High-throughput synthetic dataset sifter in Rust: Noul checks on JSONL and Parquet rows, streaming clean and rejected rows to disk.
- [1kpapers](https://www.1kpapers.com/) - 1,018 papers classified by topic and published as a browsable site.
- [jev.nvim](https://github.com/valentynkit/jev.nvim) - Neovim plugin that splits the current buffer into functions with Treesitter, asks Jev a plain-language question against each one, and lists the answers in the quickfix window ranked by probability.
- [JevPDF](https://github.com/kylemclaren/jevpdf) - Ctrl+F by meaning for PDFs: pdf.js extracts each line in the browser, Jev answers a Noul per line ("does this line answer the query?") in batches of 16, and matching lines highlight page by page ranked by probability. Live demo: [jevpdf.fly.dev](https://jevpdf.fly.dev).
- [jevsearch](https://github.com/kylemclaren/jevsearch) - Site search as a shadcn/ui registry block: keyword hits show on the first keystroke, then one request asks Jev a Noul per candidate page plus a Choice over all of them, re-orders the top 20, and keeps keyword order if the call fails; distinct from superagents-lab/jev-search.
- [jevql](https://github.com/kylemclaren/jevql) - psql-style client for vanilla PostgreSQL: the plain SQL runs on the server, Jev answers the `jev()` / `jev_prob` / `jev_choice` / `jev_score` calls on the rows it returns, and the client applies the filter, sort, or group. Playground: [jevql.fly.dev/playground](https://jevql.fly.dev/playground).
- [Milvus Model](https://github.com/milvus-io/milvus-model) - Python reranker adapter that sends candidate documents as Jev Noul questions in one request, then sorts the returned scores and preserves original document indices.
- [Vector Graph RAG](https://github.com/zilliztech/vector-graph-rag) - Given a query and candidate graph relations, an optional Jev Noul reranker scores their relevance while application code filters and orders relations before retrieving supporting documents.
- [Milvus Search with Jev](https://github.com/milvus-io/bootcamp/tree/master/bootcamp/RAG/search_with_jev) - Community cookbook with nine runnable Python notebooks combining Gemini embeddings, Milvus retrieval, and Jev decisions for reranking, filtering, search stopping, routing, cache reuse, curation, guardrails, and evaluation.
→ [More search and reranking builds on madewithjev.com](https://madewithjev.com/categories/search)
### Developer tools and code review
- [Jev Review](https://github.com/devagrawal09/jev-review) - Staged code-review workflow and local dashboard driven by focused Jev calls.
- [Foreman](https://github.com/thruwire/foreman) - Software-factory loop: Codex implements; Jev independently judges completeness, tests, and whether a human is needed.
- [Clean Code Judge](https://github.com/frostney/clean-code-review) - Scores every PR file on 31 boolean Clean Code smells plus function size and nesting, then hands verdicts to a writing model for prose.
- [OpenWork](https://github.com/different-ai/openwork) - Wires Jev into its eval testkit as a verification judge so agent-produced work is gated by typed verdicts.
- [jev-shell-history](https://github.com/mrnugget/jev-shell-history) - Fish-style zsh autosuggestions: Jev ranks recent history as you type.
- [jev-secret-detection](https://github.com/teyhouse/jev-secret-detection) - Secret-in-diff detector with repeatable Jev verdicts.
- [commit-miner](https://github.com/devanshbatham/commit-miner) - Rust CLI that classifies commit diffs: bug fixes, security/CWEs, and change types. HTML/CSV reports.
- [Jev Logs](https://github.com/reachjalil/jevlogs) - OpenTelemetry log triage: Jev scores diagnostic value and priority before an expensive LLM looks at the archive.
- [typeful-triage](https://github.com/cephalization/jev-triage) - Multiplayer issue-triage dashboard: fixed typed questions per issue (kind, severity, urgency, duplicate, next step), with human corrections shown back to the model on later runs.
- [jev-resilience](https://github.com/Vicente-MD/jev-resilience) - Spring WebFlux starter: a semantic circuit breaker that uses Jev to catch silent HTTP 200 failures.
- [tripwire](https://github.com/noelzappy/tripwire) - AI SDK middleware and OpenAI-compatible proxy: seven Jev checks on every LLM response in ~100 ms, confidence-gated.
- [ProgressGate](https://github.com/AshutoshVJTI/progressgate) - Detects semantic stagnation in agent loops: Jev judges the trajectory; code returns CONTINUE / WARN / REPLAN / HALT.
- [jev-harness](https://github.com/AntonioCoppe/jev-harness) - Production layer around Jev: policy, confidence gate, shadow mode, recipes, and an eval CLI.
- [jev-tree](https://github.com/reachjalil/jev-tree) - Recursive Choice over a taxonomy so catalogs larger than Jev's 255-option cap still fit.
- [Notra](https://github.com/usenotra/notra) - Marketing analytics: its `NOTRA_JEV_CLASSIFIERS` flag routes brand-visibility classifiers off an LLM and onto Jev boolean decisions at a 0.5 threshold, targeting 300 ms p50.
- [jev-eval-agent](https://github.com/vinilana/jev-eval-agent) - Public eval harness for early Jev tests.
- [Supercov](https://github.com/supercorp-ai/supercov) - Code quality and test coverage for coding agents: Jev scores each source file so the agent knows what to fix first.
- [jev-commit](https://github.com/valentynkit/jev-commit) - Pre-commit hook where one Jev call judges whether the commit message matches the staged diff, checks for debug leftovers and unmentioned work, and blocks the commit only when it detects a credential.
- [jgrep](https://github.com/kyu1204/jgrep) - Evaluates code chunks, git diff hunks, or CSV rows against a description with one Noul each, 16 per request; `--diff` turns the answer into grep exit codes for a CI gate and `--tests` returns the test files a diff plausibly affects.
### Model routing
- [jev-router](https://github.com/gargpratyush/jev-router) - Per-turn routing for Claude Code and Codex: simple work to the fast tier, hard work to the strong tier. `npm i -g jev-router`.
- [jev-codex-router](https://github.com/0xNatoshi/jev-codex-router) - Per-turn Codex routing: Jev picks model, thinking depth, and speed mode.
- [Codex Jev Router](https://github.com/suenot/codex-jev-router) - Uses Jev Choice and Noul decisions to choose Codex subagent model and reasoning effort, falling back to Sol when uncertain.
- [jev-router (prismhq)](https://github.com/prismhq/jev-router) - Open-source LiteLLM-based router where a Jev decision picks which model serves each request.
- [pi-jev-router](https://github.com/mejiasd3v/pi-jev-router) - Automatic per-request model routing for the Pi coding agent through Jev decisions on Vercel AI Gateway.
- [jcm-router](https://github.com/adarshmishra07/jcm-router) - Local proxy that picks the Claude model and reasoning effort per message while leaving the cached main chat untouched.
- [jev-agent-skill-router](https://github.com/GodsBoy/jev-agent-skill-router) - Routes agent skill selection through typed, confidence-aware decisions so weak matches are declined instead of guessed.
- [langchain-skill-router](https://github.com/deyna256/langchain-skill-router) - Reads the user's request plus the recent conversation, asks Jev one Choice over the SKILL.md catalog together with a "does this need a skill at all" gate and a per-candidate fit question, and the middleware loads one skill's instructions, offers up to three candidates, or nothing.
- [Jevonian](https://github.com/xinyao27/jevonian) - Local OpenAI, Anthropic, and Responses-compatible proxy where one Jev call answers both the model route and the thinking level for the virtual model jevonian/auto, from session state, quota health, candidate capabilities, and cache-switch penalties; code filters candidates by wire protocol, context window, effort floor, and spent quota windows before Jev is asked, and a pinned model ID or explicit jevonian route skips Jev entirely.
### Business and vertical apps
- [typesafe-jev CV screener](https://github.com/gtaras7/typesafe-jev) - Screens a folder of CVs against an editable policy; re-scoring candidates is free when the policy changes.
- [Jev email intent workflow](https://github.com/GiesN/typesafe-jev-workflow) - Async LangGraph workflow: a typed Choice (`invoice` or `general`) routes each inbound email to the matching handler.
- [HA-Jev](https://github.com/AboveColin/HA-Jev) - Home Assistant integration: typed questions about entity state become sensors and automation actions, with usage, cost, and daily-budget entities.
- [Jev Trader](https://github.com/jarrodwatts/jev-trader) - One buy/sell decision per Monad block on Kuru's MON-USDC book. Live demo: [jev-trader.vercel.app](https://jev-trader.vercel.app/).
- [Human Compiler](https://github.com/asfarsadewa/human-compiler) - Paste corporate prose; Jev scores passive-aggression, urgency, and information density, then code emits rustc-style diagnostics. Live: [human-compiler.asfarlab.fun](https://human-compiler.asfarlab.fun).
- [JEVMETER](https://github.com/ChetasLua/jevmeter) - Live Jev meter on any video: every sentence scored, rendered as a 16:9 edit.
- [jev-audio-beeper](https://github.com/santos-sanz/jev-audio-beeper) - Low-latency audio insult detector: Jev decides, ffmpeg beeps in ~466 ms without rewriting the rest of the track.
- [Jev Moderation Bot](https://github.com/brainstormity/Jev-Moderation-Bot) - Discord bot scoring incoming messages for phishing, spam, and social engineering, with a four-stage escalation ladder.
- [citation-verifier](https://github.com/MarissaFamularo/citation-verifier) - Checks whether each cited paper actually supports the sentence citing it: Claude locates the quote, Jev scores the support, a human makes the final call.
- [LegalForecast-MTD](https://github.com/johnhughes3/LegalForecastBench) - Benchmark that asks Jev to predict federal motion-to-dismiss rulings, scored with claim-defendant micro-Brier metrics.
- [Smart home assistant demo](https://docs.typesafe.ai/demos/smart-home) - Official interactive demo of speculative fan-out: many questions in one call, code keeps the relevant answers, LLM only for splits and chit-chat.
- [SmartMoney-Cub](https://github.com/myc0576/Smartmoney-Cub) - Experimental, read-only trading journal that passes filings, event wires and central-bank statements to Jev for typed Choice, Noul and Score answers on evidence and policy stance, keeping a human in the promote/reject loop and never placing an order.
- [Jev Web Analyzer](https://github.com/replynodes/jev-web-analyzer) - Product evaluation: evaluates a public SaaS landing page as clean Markdown with ten bounded Jev `Choice` questions about first-visit understanding, leaving validation, policy, and presentation in application code.
- [profanity-checker](https://github.com/4rays/profanity-checker) - Cloudflare Workers profanity filter: state is the text or username, Jev answers a `Noul` for literal profanity and a `Noul` for phonetic/look-alike disguise (one parallel inference call), and the Worker applies a 0.5 threshold and returns a typed `is_profane` verdict.
### Robotics and hardware
- [Jev Drone](https://github.com/RomanSlack/jev-drone) - MuJoCo quadrotor: control and safety stay in code; Jev handles slower tactical judgments at 2.5 Hz.
- [jev-askable-arm](https://github.com/TarunTomar122/jev-askable-arm) - Zero-shot English goals on a simulated Franka arm; Jev chains hardcoded primitives.
- [robo-harness](https://github.com/grmkris/robo-harness) - SO-101 arm workbench: a Jev decision runner picks bounded joint steps from typed candidate actions under a spend budget.
→ [More robotics and devices on madewithjev.com](https://madewithjev.com/categories/robotics-and-devices)
## Demos and games
Toys, live sites, and realtime agents. Most shipped in the first days after launch.
- [Yes / No](https://yesno.coderai.dev) - Free no-signup Noul demo. Ask a question, get yes / no / maybe, with web search when needed.
- [Jev Tetris](https://jev-omega.vercel.app) - Jev picks rotation and column from holes, stack height, and bumpiness.
- [Jev Pac-Man](https://jev-pacman.ephraimduncan.com) - Maze as JSON; Jev picks the turn at each junction in realtime.
- [typesafe-mario](https://github.com/fhshaik/typesafe-mario) - Super Mario Bros. from structured emulator state.
- [jev-doom-agent](https://github.com/lukaske/jev-doom-agent) - Browser-native Doom with Chocolate Doom WASM, spatial state, and live decision telemetry.
- [jev-gomoku](https://github.com/mizchi/jev-gomoku) - MoonBit client plus Jev-vs-Jev gomoku.
- [jev-t-rex-runner](https://github.com/joshlarsen/jev-t-rex-runner) - The Chrome dinosaur game, played by Jev.
- [snake-jev](https://github.com/siroccomask/snake-jev) - Snake: hundreds of typed direction decisions per run. Also [typesafe-snake](https://github.com/sorrycc/typesafe-snake).
- [Jev Plays StarCraft](https://github.com/phyous/tsai-sc) - Structured-state harness for the original StarCraft shareware campaign, with verified run and probability traces.
- [Jev × Civilization II](https://github.com/phyous/tsai-civ2) - Original Civ II in a browser; Jev chooses empire, city, research, and unit actions. Experimental; no verified win yet.
- [Jev Guard](https://guard-jev.vercel.app) - Comment-moderation playground.
- [Hollow Creek](https://hollow-creek-sigma.vercel.app) - Village NPCs that *judge* you each tick instead of chatting.
- [Jev mood demo](https://jev-demo.vercel.app) - Talk nicely or nastily over time; structured state tracks mood.
- [Jev Room](https://jev-room.moe136231.chatgpt.site) - One sentence → six room settings. Jev chooses, the app renders.
- [TypeSafe Typewriter](https://typesafe-demo.val.run/) - Live Val Town demo: 16 typed judgments update as you type.
- [got-jev](https://github.com/phureewat29/got-jev) - Game of Thrones roleplay: a story model writes the scene; Jev answers where Jon Snow is, how much danger, and what should play under it.
- [Little Airways](https://github.com/lbotinelly/jev-little-airways) - Toy archipelago air-traffic control: divert / emergency / who lands first, ~150 ms.
- [jev-plays-pokemon-red](https://github.com/valentynkit/jev-plays-pokemon-red) - Pokemon Red on PyBoy where code owns the route and the arithmetic, Jev picks only at branches, and every battle turn logs a faint prediction scored by Brier against what the RAM says.
- [HR tool pile](https://dormytech.com/jev) - Describe an HR problem in plain words; one Noul per tool asks whether it is one of the best answers, a separate Choice reads company size, and code applies both before lifting the matching tools out of a pile of 50 HR logos.
→ [More games and real-time builds on madewithjev.com](https://madewithjev.com/categories/games-and-real-time)
## Agent tools and MCP servers
Tools that expose Jev to coding agents and MCP clients.
- [TypeSafe agent skill](https://github.com/typesafe-ai/skills) - Official skill: primitives, patterns, and how to structure evaluations. Claude Code: `claude plugin marketplace add typesafe-ai/skills` then `claude plugin install typesafe@typesafe-ai`. Other agents: `npx skills add typesafe-ai/skills --skill typesafe-ai`.
- [eve](https://github.com/vercel/eve) - Vercel's agent framework. Experimental `autoModel` defaults to Gateway `typesafe-ai/jev` to pick a language model from an allowlist.
- [AI CLI](https://github.com/vercel-labs/ai-cli) - Vercel Labs CLI that can run Jev as the evaluation model for its `evaluate` command.
- [jev-mcp (jkudish)](https://github.com/jkudish/jev-mcp) - Node MCP wrapping three cookbook patterns: `jev_verify` (citation check), `jev_screen` (prompt-injection guardrails), `jev_find` (semantic ranking without embeddings). `npx -y github:jkudish/jev-mcp`.
- [jev-mcp (blakestone-x)](https://github.com/blakestone-x/jev-mcp) - Python MCP server: classify, score, check, match, and screen tools.
- [Jev Review MCP](https://github.com/NiazMorshed2007/jev-review) - Local-first MCP: Claude Code, Codex, Cursor, and OpenCode get structured quality review from Jev while they write.
- [typesafe-mcp](https://github.com/itsmostafa/typesafe-mcp) - Go CLI and single-binary MCP for Claude Desktop, Claude Code, and Codex.
- [Jevbridge](https://github.com/gamesonrblx/Jevbridge) - ACP/MCP adapter: typed Jev decisions and computer use beside Codex, Claude, Grok, and OpenCode.
- [fast-jev-compaction](https://github.com/tamaratran/fast-jev-compaction) - Claude Code plugin and npm library: Jev scores tool calls and drops stale ones instead of summarizing context.
- [SkillRanker](https://github.com/Dicklesworthstone/skillranker) - Rust CLI: Jev ranks which agent skill fits the next step from live session context, with Claude Code hooks.
- [pi-typesafe](https://github.com/DevMortimer/pi-typesafe) - Pi extension: one consented, key-managed TypeSafe client, batched `typesafe_evaluate`, offline-testable transport.
- [pi-jev](https://github.com/y0usaf/pi-jev) - Pi extension with a shadow-mode tool-call gate, output judge, and typed `jev_ask`.
- [pi-warden](https://github.com/DevMortimer/pi-warden) - Pi guardrails on pi-typesafe: held tool results instead of a dialog; write checks against a project rules file.
- [pi-jev-auto-mode](https://github.com/jomatsu/pi-jev-auto-mode) - Pi auto mode: Jev semantically approves `bash` / `write` / `edit`, and fails closed when it cannot decide.
- [Bicameral](https://github.com/AbdelStark/bicameral) - Pi coding harness: LLM writes, Jev supplies typed reflexes for policy, loop detection, and review. Explicitly not a sandbox.
- [ask-jev-skill](https://github.com/shantanugoel/ask-jev-skill) - Hermes skill: ask Jev whenever the agent needs a bounded decision.
- [jev-system-architect](https://github.com/samtay32/jev-system-architect) - Skill that hunts for brittle semantic logic and turns it into Choice / Score / Noul boundaries.
- [augustus](https://github.com/24601/Augustus) - Design-judgment skill: maps Choice/Score/Noul onto classical methods with a composition algebra, question-design diagnosis, and falsifying validation gates.
- [jev-judgment](https://github.com/HyunjunJeon/jev-judgment) - Agent skill that sends closed coding-agent judgments to Jev so verdicts stay typed, cheap, and comparable across runs.
- [pi-typesafe-jev](https://github.com/legacybridge-tech/pi-typesafe-jev) - Exposes System One judgments as five Pi tools; code and users keep control of thresholds, weights, and actions.
- [limpet](https://github.com/noplan-inc/limpet) - Stop hook that keeps an agent from finishing too early by judging plain-language completion rules with Jev.
- [jev-guard](https://github.com/leepokai/jev-guard) - Prompt-injection and dangerous-action guard for Claude Code, Codex, Pi, and ACP agents.
- [dsh-auto-mode](https://git.allen-software.com/allenh1/dsh-auto-mode) - DeepSeek Harness permission preset whose end-prompt step has Jev answer the open questions an agent leaves in its final message.
- [jev-belay](https://github.com/valentynkit/jev-belay) - Claude Code Stop hook that reads the transcript for evidence of a completed task and, only when files changed with no passing check since, spends one four-question Jev call before allowing the agent to stop, failing open on every error path.
- [TruthGate](https://github.com/satangel2222/truthgate) - Stop hook and deterministic gatekeeper for Claude Code, Cursor, and Antigravity: uses parallel Jev Nouls to block fake completions and passive nagging before physical exit code and screenshot verification.
- [DGP (Decision Graph Protocol)](https://github.com/numerous-com/dgp) - Experimental protocol for agents that decide before they act: Jev assesses framed evidence with typed choices (proceed, escalate, abstain); deterministic code checks permissions, freshness and commit guards before any action. Python/SQLite demo with a Jev resolver; actions simulated.
## Use cases by industry
Decision shapes that recur across domains. Each is a small decision system: a state object, atomic questions, and a code-owned review branch. Expanded from TypeSafe's [use-case map](https://docs.typesafe.ai/concepts/use-case-map) and [workflow evals](https://evals.typesafe.ai/).
| Domain | Example Jev workflow |
|---|---|
| Customer support | Classify intent, detect urgency and refund intent, score frustration; route with ordinary code and escalate low-confidence tickets. |
| Security operations | Join an alert with asset context and authorizations; ask whether the activity is unauthorized, then let a deterministic playbook close, queue, notify, or contain. |
| Finance and payments | Match invoices against POs and contracts; Jev flags duplicate/fraud/wrong-vendor signals while code owns totals, dates, and execution. |
| Insurance | Run a claims rubric as independent Nouls — coverage, exclusions, fraud indicators — and map the middle band to human review. |
| Legal and compliance | Find missing clauses, prohibited claims, and policy violations in contracts, filings, and marketing material. |
| Recruiting | Evaluate job-related evidence, match candidates to roles, route applications, escalate uncertain cases. |
| Sales and lead gen | Score ICP fit, buyer relevance, pain points, and purchase intent before routing leads. |
| E-commerce | Normalize listings, extract product attributes, detect counterfeit or prohibited-listing signals, route exceptions. |
| Moderation and trust & safety | Apply org-specific criteria to toxicity, spam, fraud, and personal-data exposure, with an explicit uncertain outcome. |
| Advertising | Check brand safety, audience suitability, regulatory claims, and ad-to-landing-page alignment. |
| Gaming | Moderate chat, score engagement or frustration, detect abuse and churn signals, route player support. |
| Financial crime | Evaluate transaction narratives and KYC material; match entities and prioritize investigator queues. |
| Scientific discovery | Screen papers, label themes in qualitative research, check manuscript citations, link entities to evidence. |
| Risk and forecasting | Turn incident reports and transaction descriptions into probabilistic features for a supervised model. |
| Knowledge graphs | Classify entity types and relationships, detect contradictions, support probabilistic traversal. |
Recurring architectures worth stealing:
- **Support inbox triage** - Fan out intent, urgency, severity, and frustration questions in one call; act on the confident answers, route the rest.
- **RAG passage filtering** - Score relevance, contradiction, and injection risk per passage before the answering model sees it.
- **LLM guardrails** - Screen prompts, replies, and tool calls with hazard Nouls and a harm Score; policy passes, reviews, or blocks.
- **Confidence-gated actions** - Lower thresholds for reversible read-only actions, higher ones for risky operations, humans for the rest.
- **Model routing** - Let a fast typed decision choose between deterministic code, a cheap LLM, a frontier LLM, or a person.
- **Structured extraction cascades** - A small model extracts candidate fields; Jev verifies each value; only failures escalate to a reasoning model.
- **Composite scoring** - Score independent dimensions, then combine with weights you own in code — leads, candidates, vendors, risk.
- **Corpus map-reduce** - Ask the same questions of every document: 1,018 papers, 3,282 posts, 724 ads, 1,500 emails. Read the aggregate, not the documents.
- **Real-time control** - When the deadline is a frame, a block, or a tick, code generates legal actions and Jev picks one.
## Patterns
Architectural recipes from the official docs.
- [Speculative fan-out](https://docs.typesafe.ai/patterns/fan-out) - Ask many questions, including ones that may not apply; filter in code.
- [Confidence-gated routing](https://docs.typesafe.ai/patterns/confidence-routing) - The answer is *what*; confidence is *whether to act*.
- [Composite scoring](https://docs.typesafe.ai/patterns/composite-scoring) - Atomic scores, weights you own in code.
- [Intent routing](https://docs.typesafe.ai/patterns/intent-routing) - Classify, then hand off to logic, a specialist LLM, or a human.
See also: [How to build with System One](https://docs.typesafe.ai/concepts/how-to-build-with-system-one), the [use-case map](https://docs.typesafe.ai/concepts/use-case-map), and [confidence](https://docs.typesafe.ai/confidence).
## Cookbooks
Official, copy-pasteable workflows. Full index: [console cookbooks](https://console.typesafe.ai/docs/cookbooks) and the [docs index](https://docs.typesafe.ai/llms.txt).
- [Parallel questions](https://docs.typesafe.ai/cookbooks/parallel_questions) - Batch many questions over one state; one call instead of N.
- [Line-by-line search](https://docs.typesafe.ai/cookbooks/semantic_find) - Score hundreds of line ids against a query with Choice + a Noul "does an answer exist?" check.
- [Re-ranking](https://docs.typesafe.ai/cookbooks/rerank_typesafe) - BM25 shortlist, then one TypeSafe question per query–candidate pair.
- [Guardrails for LLMs](https://docs.typesafe.ai/cookbooks/llm_guardrails) - Screen messages in and out of an LLM; threshold probabilities in code.
- [Double-checking citations](https://docs.typesafe.ai/cookbooks/citation_check) - Whether a quote's context supports the claim; confidence gates human review.
- [Classifying RAG passages](https://docs.typesafe.ai/cookbooks/classifying_rag_passages) - Keep, flag, or drop retrieved passages before the answering model.
- [Function calling](https://docs.typesafe.ai/cookbooks/function_calling) - Map natural-language requests onto ordinary typed functions with closed-set arguments.
- [Skill suggestion](https://docs.typesafe.ai/cookbooks/skill_suggestion) - Rank an agent skill catalog, then read only the top few.
- [Hierarchical classification](https://docs.typesafe.ai/cookbooks/hierarchical_classification) - Beam search over deep taxonomies with Choice probabilities.
- [SDE cascade](https://docs.typesafe.ai/cookbooks/sde_cascade) - Two-stage structured-data-extraction cascade (mini → verify → reasoning).
- [Date extraction](https://docs.typesafe.ai/cookbooks/date_extraction_cookbook) - Ask for named date parts, resolve and validate in code.
- [Pre-parsed value extraction](https://docs.typesafe.ai/cookbooks/pre_parsed_value_extraction_cookbook) - Regex candidates, then Jev selects the requested span.
- [Knowledge graph entity alignment](https://docs.typesafe.ai/cookbooks/entity_alignment) - Score merge / leave unlinked / send to a curator.
- [Autoresearch feature discovery](https://docs.typesafe.ai/cookbooks/autoresearch_feature_discovery) - Propose TypeSafe questions as numeric features for a supervised model.
- [Classification using confidence](https://docs.typesafe.ai/cookbooks/classification_using_confidence) - Report a fine label only when confidence is high; otherwise climb the hierarchy.
- [Structure recovery](https://docs.typesafe.ai/cookbooks/autoformat) - Reconstruct Markdown from de-formatted plain text.
- [Self-consistency: nouls](https://docs.typesafe.ai/cookbooks/consistency_noul_cookbook) / [choices](https://docs.typesafe.ai/cookbooks/consistency_choice_cookbook) - Route uncertain probabilities to review without hiding the raw values.
- [Jev Cookbook](https://github.com/nexibeo/jev-cookbook) - Community cookbook: 15 runnable Node recipes that pass tickets, table rows, documents, invoices and emails as state, ask Choice, Noul and Score questions in one call, and keep thresholds, review bands and actions in code.
## Benchmarks and evaluations
Official numbers are vendor-reported; these community efforts measure for themselves.
- [Workflow evals](https://evals.typesafe.ai) - Official: four automation workflows, accuracy/cost/time per case, Jev vs frontier models.
- [typesafe-ai-benchmark](https://github.com/iammrduncan/typesafe-ai-benchmark) - Jev vs Qwen 3.8 27B on Cerebras for the same System One questions.
- [Jev Rerank Bench](https://github.com/anessbelbati/jev-rerank-bench) - Reranking comparison with raw provider responses, scoring code, and uncertainty intervals.
- [Jev Spam Eval](https://github.com/bitnovus/jev-spam-eval) - Zero-shot spam study vs trained TF-IDF baselines, with post-hoc-tuning caveats.
- [Jev Phishing Bench](https://github.com/anisselbd/jev-phishing-bench) - 2,000 emails: Jev vs Claude Haiku 4.5 on click-or-not, with calibration, latency, and cost. Haiku wins accuracy here.
- [jev-agent-failure-benchmark](https://github.com/TokenTrim/jev-agent-failure-benchmark) - Who&When Pro (injected agent failures): Jev vs a strong LLM on who / which step / error category.
- [jev-sec-bench](https://github.com/Gaurav-Gosain/jev-sec-bench) - Blind prompt-injection and vulnerable-code detection benches on public corpora.
- [Jev DSPy Lab](https://github.com/jmanhype/jev-dspy-lab) - DSPy companion that records and replays TypeSafe calls while measuring calibration, selective risk, abstention, latency, and cost.
- [jevcal](https://github.com/abhixhek/jevcal) - CLI that fits a per-question confidence threshold to a target accuracy on your own labeled data, and fails CI when a Jev update breaks locked thresholds.
- [ASSAY-001](https://github.com/jourdanlabs/assay-001) - Independent pre-registered check of Jev calibration and type safety on Banking77 / CLINC150. Split verdict, full logs. [Write-up](https://donttrustme.ai/assay-001.html).
- [Jev search rerank eval](https://github.com/zhuyansen/jev-search-rerank-eval) - 9,831 labelled pairs: Jev rerank vs BM25 / bge-m3. Fusion wins; Jev alone does not beat embeddings.
- [Smoking-history extraction benchmark](https://github.com/vclic/smoking-extraction-benchmark) - 1,000 synthetic notes: Jev vs OpenAI structured outputs on accuracy, cost, and latency.
- [Jev Playground](https://github.com/hegargarcia/jev-playground) - Benchmarks Jev against Luna, Haiku, and Gemini at choosing validated legal moves in explicit-state games.
- [jev-research-eval](https://github.com/jgridifier/jev-research-eval) - Reproducible eval harness plus field note for Jev Ultrafast research-browser tasks.
## Research and open models
Independent work inspired by Jev's interface. These are not TypeSafe models.
- [jevlike](https://github.com/vinnylarouge/jevlike) - Train a small one-pass scorer mapping context + N text options to a probability per option. Doom / chess vision demos and a Wikispeedia example. Explicitly not a reproduction of TypeSafe's architecture or RLCD.
- [openjev](https://github.com/TheoLeeCJ/openjev) - Can we run something Jev-like on a home RTX 3090? Reads option logits instead of generating text. Also [zhihz/openjev](https://github.com/zhihz/openjev) - an independent local preview answering bilingual probability questions.
- [PocketJev](https://github.com/NullPo-jp/PocketJev) - On-device iPhone visual decisions with MLX + Qwen3-VL option logits. Camera + 3-choice, no text generation, ~1 s, no photo saved.
- [jev-visual](https://github.com/hr98w/jev-visual) - Educational Jev-like visual inference on Apple Silicon: shared multimodal context, candidate scoring, sorting-factory / Breakout / gesture demos.
- [jevmlx](https://github.com/bnsd55/jevmlx) - Jev-style parallel constrained decisions for any MLX model on Apple Silicon: schema-valid JSON in one forward pass.
- [JEVfire](https://github.com/kikoncuo/jevfire) - Jev-inspired parallel decisions for CUDA LLMs via vLLM, with a browser Mario demo (~71 ms/action locally).
- [decider](https://github.com/Mapika/decider) - Qwen3.5-2B fine-tune that emits typed decisions with calibrated probabilities in one pass.
- [PlayJev](https://github.com/OmniJev/PlayJev) - Plays ten browser games from the frame alone: a fine-tuned Qwen3.5-0.8B reads the option letters and returns one move per forward pass, with open weights and a browser demo.
- [Parallel Constrained Decoding (Qwen2.5-1B-RLCD)](https://huggingface.co/spaces/drinkmoonshine/parallel-constrained-decoding) - Hugging Face space exploring open-source RLCD-style parallel constrained decoding.
- [eve-rlcd](https://github.com/anthony-maio/eve-rlcd) - Jev-inspired 0.6B decision model trained with reinforcement learning from right/wrong feedback only (reward: outcome minus stated probability); answers parallel Choice, Score and Noul questions over one state without generating text, with an RLVR ablation and [open weights](https://huggingface.co/anthonym21/qwen3-0.6b-rlcd-decision). Explicitly not a reproduction of TypeSafe's method.
- [RSI-Jev](https://github.com/Shanghua-Gao/RSI-Jev) - Jev-inspired open 0.8B and 2B models that read a document, answer `Choice`, `Score` and `Noul` questions in one forward pass over the same `POST /v1/systemone` schema, and hand back a probability per option that the caller thresholds to a result the repository measures against its own confidence bins.
- [Verdict](https://github.com/Manavarya09/verdict) - Jev-inspired open (Apache-2.0) decision model: 118M multilingual bi-encoder answering Choice / Score / Noul on the same `POST /v1/systemone` wire format, with temperature scaling plus a conformal abstain set, runs on CPU or in the browser via ONNX, fits on your own labels in seconds; the README says it loses to Laya on typed decisions.
- [jevos](https://github.com/feder-cr/jev) - A 1B model (MiniCPM) cut to 17 layers with a one-logit output head, serving Noul-only decisions from open GGUF weights (619 MB) over llama.cpp on CPU, no GPU. Explicitly not a reproduction of TypeSafe's architecture.
## Articles and coverage
- [TypeSafe AI debuts model for machines that plays Doom](https://www.theregister.com/ai-and-ml/2026/09/16/typesafe-ai-debuts-model-for-machines-that-plays-doom/5296711) - The Register's launch coverage.
- [Mini-Vibe Check: TypeSafe's Jev Judged Everything I've Written in 0.7 Seconds](https://every.to/also-true-for-humans/mini-vibe-check-typesafe-s-jev-judged-everything-i-ve-written-in-0-7-seconds) - Every's Mike Taylor runs Jev over his writing corpus: 1,709 judgments for under a cent.
- [Building a harness with Jev](https://www.langchain.com/blog/building-a-harness-with-jev) - LangChain on model routing and gating dangerous tool calls behind a typed decision.
- [Jev, from a developer's angle](https://flaviocopes.com/jev/) - Flavio Copes on triage, RAG filtering, citation checks, and confidence gates.
- [Jev, Sorted](https://pearpages.com/blog/2026/09/16/jev-sorted-what-typesafes-system-one-model-actually-is-and-what-is-still-just-a-claim) - What the launch claims survive a reading of the primary sources, and what is still vendor-reported.
- [TypeSafe Jev played chess (and landed next to reasoning models)](https://dev.to/maximsaplin/typesafe-jev-played-chess-and-landed-next-to-reasoning-models-28ga) - Maxim Saplin constrains chess to legal-move Choices.
- [Jev: one judge call, or twelve dimension scores?](https://agentjournal.dev/blog/llm-judge-vs-feature-extraction/) - Independent measurement on three classification tasks, with token costs and false-positive rates.
- [Testing Jev on public and private data: classifier or filter?](https://amankumar.ai/blogs/jev-measured) - 16,000 calls vs gpt-5.4-mini and gpt-5.6-luna; where it wins, where it breaks, and a threshold procedure.
- [Jev vs Mistral and Gemini for event validation](https://nearhere.events/blog/typesafe-jev-mistral-gemini-event-validation) - Head-to-head at validating local event listings.
- [TypeSafeのJevを正しく驚く、それってLLMでできませんか？](https://zenn.dev/nwn/articles/824026c76116e0) - (Japanese) Reproduces the JSON-vs-logit shortcut on Gemma and compares Jev with LLMs on the public Mario harness.
- [jev 同士に五目並べで対戦させた](https://zenn.dev/mizchi/articles/jev-plays-gomoku) - (Japanese) Jev vs Jev gomoku with source and timing logs.
- [Jev on AI Wiki](https://aiwiki.ai/wiki/jev) - Community-maintained reference page.
- [A new kind of AI model is thrilling developers](https://techcrunch.com/2026/09/18/a-new-kind-of-ai-model-from-a-chatgpt-inventor-is-thrilling-developers/) - TechCrunch on why developers see a cheaper and faster path to software intelligence.
- [Jev, with Diogo Almeida](https://www.latent.space/p/jev) - Latent Space interviews TypeSafe's founder about System One models and what Jev is for.
- [Build an agentic harness using Jev](https://x.com/Av1dlive/status/2102802621664985241) - Avid's builder's guide, and the fullest public write-up of the pattern: where the decision layer sits in a coding harness, the five rules that keep it enforceable, and the Rust app it was built in.
- [The Jev + Claude Code Stack](https://x.com/cyrilXBT/status/2101515130798297448) - Where the 200× and 400× headline figures come from, why they are a ceiling rather than a typical result, and the decision-point audit that finds yours.
- [How to train your own Jev for $17](https://x.com/nutlope/status/2102881280115249597) - Together AI fine-tunes a Jev-like classifier on Qwen3.5 4B: ~38,000 sampled questions, ~25 minutes of training, and the tev1 repository that runs it.
- [What Jev is, and the businesses it unlocks](https://x.com/gregisenberg/status/2101018750916948237) - Greg Isenberg: find an expensive queue and put Jev at the front of it. Seven ideas, from instant quotes to lead scoring.
- [Introducing Jev-as-a-judge](https://deepeval.com/blog/introducing-jev-as-a-judge) - DeepEval on building evals with Jev as the judge instead of a generative model.
- [A hype-free explanation of Jev](https://x.com/NathanFlurry/status/2100036101809619314) - Nathan Flurry: Jev does not replace GPT or Claude. It works like a very smart switch statement.
- [Jev Explained for Normies](https://x.com/matthewcanham/status/2102077098756280413) - Matt Canham's X article, written for everyone who read the launch posts and still did not get it.
- [Jev, explained like you're five](https://x.com/mvanhorn/status/2100761338918363550) - Matt Van Horn: think AI multiple choice, not AI essay writing. Keep the big model for the hard thinking.
- [Jev in the Wild: A Data-Driven Analysis of the Jev Model's Functionality, Applications and Ecosystem](https://arxiv.org/abs/2609.30216) - The first data-driven survey of Jev's ecosystem: 2,170 public GitHub projects as of September 22, 2026, their early growth, the domains they cover, and how decision use splits across them.
## Discussions
- [Introducing System One Models and Jev](https://news.ycombinator.com/item?id=49717558) - The 1,800-point Hacker News launch thread; the sceptical reading of the benchmarks lives here.
- [Launch thread by Diogo Almeida](https://x.com/CompleteSkeptic/status/2099925682726002904) - TypeSafe's founder argues RLCD-trained decision models are a shorter path to economic value than chat models.
- [TypeSafe AI releases Jev (r/singularity)](https://reddit.com/r/singularity/comments/1whop6b/typesafe_ai_releases_ai_model_called_jev_rather/) - Reddit frames Jev as a low-hallucination, low-cost decision model for software rather than chat.
- [Testing Jev for Pi extensions (r/PiCodingAgent)](https://reddit.com/r/PiCodingAgent/comments/1whsav6/anyone_else_testing_out_typesafe_ais_new_system/) - Builders using Jev as an agent tool-use safety layer.
- [Jev "playing" Minecraft (r/accelerate)](https://reddit.com/r/accelerate/comments/1whk9oy/new_typesafe_ai_jev_model_playing_minecraft_wip/) - Work-in-progress demo, including fleeing zombies at night.
- [Model router built with Jev](https://x.com/ephraimduncan/status/2100454070536351824) - Jev decides which model should serve a request before it is forwarded.
- [MLP on Qwen 4B mimicking Jev](https://x.com/justALEXWORTEGA/status/2100341039986798930) - A small MLP on top of Qwen 4B reproduces Jev-like decision behaviour.
- [Running a local TypeSafe Jev](https://x.com/wmoto_ai/status/2100454049359577516) - (Japanese) Local Jev-style decision model attempt.
- [Jev as an AI agent safety monitor](https://x.com/isNickMa/status/2100566407524344225) - Checking each agent action first reportedly catches most attacks with almost no false blocks.
- [Rethinking security engineering with Jev](https://x.com/Kostastsale/status/2100362415187833048) - Argues purely engineering decisions in security work belong to Jev rather than a chat model.
- [Ask Jev anything, it will judge](https://x.com/waynesutton/status/2100487878992388279) - Public Convex-backed demo inviting one million judged questions.
- [First Jev use case in a Mac app](https://x.com/malekoo/status/2100439840575684910) - A shipped Mac app routes setup and troubleshooting questions to Jev when no language model is loaded.
- [Jev 中文解读](https://x.com/dotey/status/2100109937237987823) - (Chinese) The System One category explained as a calibrated, typed decision layer for code.
- [Launch roundup](https://x.com/VaibhavSisinty/status/2100619641827836222) - Browser, papers, email, trading, and games in one thread.
## FAQ
### What is Jev?
Jev is an AI model from TypeSafe AI, launched in early access on September 15, 2026. It is the first "System One" model: instead of generating text, it evaluates typed questions (Choice, Score, Noul) against a state and returns structured answers with calibrated probabilities, in 70–500 ms (vendor-reported).
### What is TypeSafe AI?
TypeSafe AI is a San Francisco AI lab founded by Diogo Almeida, previously at OpenAI, where he worked on instruction-following methods. The company raised $40M and works on "machine-native" intelligence: models built for software to consume, not for people to chat with.
### Is Jev an LLM?
No. It reads natural language but never generates text. The answer space is defined in advance by your questions, so outputs are type-safe by construction and cannot hallucinate a value outside the space you gave it. See [Jev vs LLM](#jev-vs-llm).
### What is RLCD?
Reinforcement Learning for Calibrated Decisions — TypeSafe's training method for System One models. Where RLHF optimizes for responses humans prefer, RLCD optimizes for decisions with epistemically honest probabilities: higher confidence should mean higher accuracy.
### How much does Jev cost?
$0.042 per million input tokens; output tokens are free. A typical typed question costs a tiny fraction of a cent, which is why the featured builds above report numbers like 1,709 judgments for under a cent.
### How do I get access to the Jev API?
Three ways: join the early-access waitlist at [typesafe.ai](https://typesafe.ai), use [Vercel AI Gateway](https://vercel.com/ai-gateway/models/jev) (model id `typesafe-ai/jev`, no waitlist), or use [Cloudflare Workers AI](https://developers.cloudflare.com/ai/models/typesafe/jev/) (`typesafe/jev`).
### What are Jev's limits?
Choice questions cap at 255 options. Text only — no images, audio, or video. Listed rate limits are 250,000 tokens/second and 1,200 requests/minute, and TypeSafe says they can change dynamically. Known failure modes of the current model are documented in [Jev 1.13 jaggedness](https://docs.typesafe.ai/model-jaggedness/jev-1.13).
### What is a System One model?
TypeSafe's name for a model class built for fast, structured decisions inside software — as opposed to chat models that generate text for humans. Named after the fast, intuitive "System 1" mode of thinking. Jev is the first public one.
## More guides, integrations and lists
**Video explainers**
- [Jev Explained in 3 Mins](https://www.youtube.com/shorts/zkZQT5rbrf4) - KodeKloud's short: what a System One model decides, and why it answers in milliseconds.
- [Jev explained in 7 mins](https://www.youtube.com/watch?v=vj7hysh0mOI) - Caleb Writes Code: what Jev is and how to use it.
- [Jev explained with Python examples](https://www.youtube.com/watch?v=EAh1h1GYxDM) - ZazenCodes works through Jev hands on, from a first call to typed decisions.
- [Jev Explained for Python Developers](https://www.youtube.com/watch?v=JpfLID19QnQ) - Dave Ebbelaar: a support-ticket classification first, then the three primitives, with latency and price next to Claude Haiku and Opus.
- [Steerable Reranking: How JEV Solves RAG](https://www.youtube.com/watch?v=UhGH8cNG0qs) - Prompt Engineering puts Jev in the reranking step of a RAG pipeline, with a Colab notebook.
- [Jev AI Full COURSE 1 HOUR](https://www.youtube.com/watch?v=Hz8tobAFBVM) - Julian Goldie's hour-long course: the three question types and ten use cases.
- [Jev System One Model, clearly explained](https://www.youtube.com/watch?v=RMQI_y1AvvA) - Sean's AI Stories explains TypeSafe AI, fast judgment and evaluation end to end.
**Framework and platform integrations**
- [Opik](https://github.com/comet-ml/opik) - Comet's open-source evaluation and tracing platform, whose custom-metric interface takes several named scores from one Jev call.
- [Trace every judgment with Phoenix](https://arize.com/docs/phoenix/integrations/llm-providers/typesafe) - Arize's instrumentation: one line of code to trace each decision.
- [Jev in Pydantic AI](https://pydantic.dev/docs/ai/models/typesafe/) - The native TypeSafe model, to call Jev from a Pydantic AI agent.
- [Jev through LiteLLM](https://docs.litellm.ai/docs/pass_through/typesafe) - Pass-through docs for calling TypeSafe from an existing LiteLLM proxy.
- [langchain-typesafe](https://pypi.org/project/langchain-typesafe/) - The LangChain package with `TypeSafeClassifier`.
- [Jev on Netlify AI Gateway](https://www.netlify.com/changelog/typesafe-jev-ai-gateway/) - Jev through Netlify's AI Gateway with zero configuration.
**Directories and lists**
- [jev.directory](https://jev.directory/) - A community directory of systems built on Jev, listed by the Noul, Score and Choice questions they send.
- [awesomejev.com](https://awesomejev.com/) - A directory of projects built on Jev: repositories, SDKs, integrations, demos and benchmarks.
- [Jevable](https://jevable.com) - Nikunj Kothari's directory of the demos posted on X, filterable by category.
- [awesome-typesafe](https://github.com/AbdelStark/awesome-typesafe) - Official resources and community projects for TypeSafe, System One models and Jev.
- [jev-skill](https://github.com/wuyoscar/jev-skill) - A collection of Jev use cases, workflows and agent skills in one repository.
- [awesome-jev-projects](https://github.com/logicrw/awesome-jev-projects) - A source-backed radar of the open-source ecosystem, in English, Chinese, Japanese and Korean.
- [awesome-jev (verified catalog)](https://github.com/heyjunpenn/awesome-jev) - A community-maintained catalog that claims 433 verified open-source projects.
- [Papers and open reproductions](https://github.com/OmniJev/awesome-jev) - The papers, open reproductions and independent evaluations behind System One models.
- [fatwang2/awesome-jev](https://github.com/fatwang2/awesome-jev) - A source-backed project directory with a GitHub review workflow that uses only Jev.
## Related lists
- [awesome-jev (AnotiaWang)](https://github.com/AnotiaWang/awesome-jev) - Community list of Jev applications, libraries, and resources. English and 简体中文.
- [awesome-jev (yibie)](https://github.com/yibie/awesome-jev) - Jev projects and discussions organized by application domain.
- [awesome-jev (cobanov)](https://github.com/cobanov/awesome-jev) - Curated, source-backed list of Jev projects, sorted by decision domain.
- [awesome-jev-by-typesafe (Anil-matcha)](https://github.com/Anil-matcha/awesome-jev-by-typesafe) - Evidence-backed use cases, patterns, and starter code.
- [awesome-jev-typesafe (valentynkit)](https://github.com/valentynkit/awesome-jev-typesafe) - CC0, awesome-lint clean, sorted by what you would install, with a short know-before-you-build section on the limits.
- [laya.tools](https://laya.tools) - Directory of projects built on Laya, the open-source (Apache-2.0) System One typed-decision model from ConvAI Innovations (not a TypeSafe model), browsable by platform and use case, with a Laya vs Jev comparison.
- [typesafe-ai on PyPI](https://pypi.org/project/typesafe-ai/) - Community redirect shim; the real package is `typesafe-sdk`. Registered to block slopsquatting.
## Contribute
See [CONTRIBUTING.md](CONTRIBUTING.md). In short: open a pull request that adds a project with a link and a one-line description. It should be useful, interesting, and actually built on Jev (or clearly inspired by its interface). Mark experimental or dry-run-only paths (trading, home automation) explicitly.
Built something with Jev? Also submit it to [madewithjev.com/submit](https://madewithjev.com/submit) to get it in the directory with your reported numbers.
## License
[CC0 1.0](LICENSE) — this list is dedicated to the public domain.
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Maintained by <a href="https://x.com/kraayenjon">@kraayenjon</a> as part of <a href="https://madewithjev.com">madewithjev.com</a> — a directory of what people are building with Jev. Not affiliated with TypeSafe AI.
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