kraayenjon/awesome-jev

Awesome Jev

A curated list of Jev use cases, projects, SDKs, tools, and learning resources.
Jev is the first System One model from TypeSafe AI — an AI model that returns typed decisions (Choice, Score, Noul) with calibrated probabilities instead of generated text.

Looking for real-world Jev use cases with numbers? 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 →

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?
• Jev vs LLM
• Pricing, limits, and access
• Quick start
• Official resources
• Community
• Featured builds with real numbers
• SDKs and clients
• Applications
• Browser and computer-use agents
• Search, retrieval, and data
• Developer tools and code review
• Model routing
• Business and vertical apps
• Robotics and hardware
• Demos and games
• Agent tools and MCP servers
• Use cases by industry
• Patterns
• Cookbooks
• Benchmarks and evaluations
• Research and open models
• Articles and coverage
• Discussions
• FAQ
• Related lists
• 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 | Pick one option from a list | choice, probabilities, confidence | | Score | Rate the state on a rubric | score, probabilities, confidence | | 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:

| | 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 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 | | No-waitlist access | Vercel AI Gateway (typesafe-ai/jev) and Cloudflare Workers AI (typesafe/jev) |

Quick start

Get an API key from the TypeSafe console, then:

Python — pip install typesafe-sdk

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

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 for details.

Official resources

• TypeSafe AI - Company homepage, waitlist, and product overview.
• Introducing System One Models and Jev - Launch post by founder Diogo Almeida: architecture, RLCD, pricing, Doom and Wikiracing demos, FAQ.
• Documentation - Introduction, primitives, patterns, API, and SDKs. Start with the quick start.
• Playground - Paste a state, add questions, see typed answers in the browser.
• API keys - Dashboard for TypeSafe API keys (TYPESAFE_API_KEY).
• HTTP API reference - POST https://api.typesafe.ai/v1/systemone.
• Models, prices, and limits - Aliases, versions, and rate limits.
• Workflow evals - Published eval methodology and per-model results on automation workflows.
• GitHub org - Official open-source repositories.
• Agent skill - Drop-in skill for Claude Code, Codex, and other coding agents (typesafe-ai/skills).
• Jev 1.13 jaggedness - Known failure modes of the current public model.
• Manifesto - The case for machine-native intelligence built for software, not conversation.
• The Bitterest Lesson - Why optimizing the wrong task can dominate gains from scale.
• AI: too good to be true, too bad to be useful - Against preference-optimized chat models for automation.
• Jev on Vercel AI Gateway - Hosted typesafe-ai/jev for the AI SDK's experimental_evaluate, no TypeSafe waitlist required.
• Jev on Cloudflare Workers AI - typesafe/jev via env.AI.run, with worked support-routing and risk-escalation examples.

Community

• Discord - Official TypeSafe server; builder demos live in Show and Tell.
• X @typesafeai - Product and research updates.
• X @CompleteSkeptic - Founder Diogo Almeida.
• LinkedIn - 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, the Jev use-case directory that maintains this list.

| Build | What it does | Reported numbers | Source | |---|---|---|---| | Jev plays Doom | Game loop asking Jev what to do ~10 times a second | ~10 queries/s, ~$7/hour | X | | jev-ultrafast | Browser Use's agent with the next-action decision moved to Jev | ~2.9k stars | GitHub | | Flight search with Browser Use | Booking flow driven end to end | ~7 s, ~$0.004 | X | | Stagehand on a remote browser | Browser tasks at a tenth of a cent each | ~$0.001/task | X | | jev-trader | Buy/sell decided inside a 300 ms Monad block, on Kuru's order book | 300 ms decision window | GitHub | | Triage across 1,500 emails | A full inbox sorted in one pass | ~1,500 emails | X | | Every's editorial vibe check | 37 documents, 21 questions each; 6 of 7 planted defects caught | 1,709 judgments, <$0.01, 0.35 s median | Every | | 1kpapers | A corpus classified by topic and published as a site | 1,018 papers | Site | | Jev plays chess | Legal moves as a Choice, compared with reasoning models | illegal moves impossible by construction | dev.to | | 3,282 posts, eight questions each | Ian Nuttall's X back catalogue scored for what travels | 4.25M tokens, $0.1282, 8 m 34 s | X | | Post scoring with SuperX | 61 questions about a draft before it ships | ~1 s, $0.0004/draft | X | | 724 competitor ads, broken down | Hook, format, offer, CTA per ad across 37 brands | ~40 s, ~$0.09 | X | | typesafe-computer-use | macOS computer use, one typed decision per step | ~$0.0002/step | GitHub | | jev-drone | Tactical judgment loop flying on hardware | control at 2.5 Hz | GitHub | | Wikiracing | Pick one link out of thousands until you arrive | 255-option Choice ceiling | TypeSafe |

All figures are as reported by each author, not measured by this list. → Browse the full directory at madewithjev.com

SDKs and clients

Official first, then community clients. Community packages are not affiliated with TypeSafe.

Official

• Python SDK - pip install typesafe-sdk. Docs.
• JavaScript / TypeScript SDK - npm install @typesafe-ai/sdk. Docs.
• 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 - @ai-sdk/typesafe-ai with experimental_evaluate; use typeSafeAi.evaluationModel('jev-latest') or the Gateway id typesafe-ai/jev.

• Go: jev-go - go get github.com/Gaurav-Gosain/jev-go. Also 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 - Hex package for system_one and model listing. Also Jev (OTP) - Jev as a peer GenServer; answers arrive as messages you pattern-match, with network-free tests.
• Ruby: typesafe-sdk - Ruby 3.1+, retries, thread-safe pooled HTTP. Also RubyLLM TypeSafe - TypeSafe provider for RubyLLM 2. And typesafe-ai-rails - Rails integration with usage telemetry and opt-in confidence policies.
• Rust: typesafe-ai-rs - async and blocking client. Also Twister915/typesafe-ai - observable retries; typesafe-rs - latency-focused transport; s1-rs - derive layer for Choice / Score / Noul with confidence gates and network-free tests.
• PHP / Laravel: typesafe-sdk-php - typed DTOs and promises. Plus laravel-typesafe-jev - Laravel 12/13 config, facade, scoped DI, and a recording fake.
• Python: jevclient - async client (pip install jevclient), separate from the official SDK.
• Swift: swift-typesafe - Swift 6.4 client aligned with the Python SDK 0.6.0 API, including Linux.
• Scala / ZIO: zio-typesafe-ai - ZIO client with a small DSL for noul / choice / score.
• .NET: typesafe-dotnet-sdk - typed questions and confidence-scored answers.
• TypeScript: Advocaat - small client with tagged helpers for chances, choices, and scores.
• Cloud: 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 - Browser agent from 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 - 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 - Browser agent on ego lite: one TypeSafe request picks operation + indexed element; agent-facing observe/act/suggest/step CLI.
• 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) - Agent skill + runtime: Codex plans, Jev selects elements, a runner acts and verifies each step.
• typesafe-computer-use - macOS computer-use loop: OCR the screen, Jev classifies the next action, then click. About $0.0002/step.
• 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 - Chrome / Firefox extension: Jev classifies nonessential page elements; local template rules hide them on later visits.
• 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 - 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.

Search, retrieval, and data

• Every - Semantic code-search CLI: a yes/no question against every function, ranked by Noul probability.
• blink - Codebase search: an ensemble of walkers asks Jev which file answers a natural-language query.
• 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.
• neo4jev - Neo4j graph navigation: at each node Jev chooses which relationship to follow, with beam search over log-probabilities.
• jev-bfs - Finds link paths between Wikipedia articles; Jev ranks each page's outgoing links while Python controls the search.
• hono-jev-router - Experimental Hono router: Jev matches an incoming request to a plain-language route description.
• sqlite3-jev - SQLite C extension: jev_noul / jev_choice / jev_score as SQL functions via libcurl.
• jev-curate - High-throughput synthetic dataset sifter in Rust: Noul checks on JSONL and Parquet rows, streaming clean and rejected rows to disk.
• 1kpapers - 1,018 papers classified by topic and published as a browsable site.
• 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.

Developer tools and code review

• Jev Review - Staged code-review workflow and local dashboard driven by focused Jev calls.
• Foreman - Software-factory loop: Codex implements; Jev independently judges completeness, tests, and whether a human is needed.
• Clean Code Judge - 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 - Wires Jev into its eval testkit as a verification judge so agent-produced work is gated by typed verdicts.
• jev-shell-history - Fish-style zsh autosuggestions: Jev ranks recent history as you type.
• jev-secret-detection - Secret-in-diff detector with repeatable Jev verdicts.
• commit-miner - Rust CLI that classifies commit diffs: bug fixes, security/CWEs, and change types. HTML/CSV reports.
• Jev Logs - OpenTelemetry log triage: Jev scores diagnostic value and priority before an expensive LLM looks at the archive.
• typeful-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/Vicen