110 curated Jev entries. Official, runnable Jev recipes that show what decision models are good at, each with its own datasets, latencies, and numbers.
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All 110 Jev Cookbooks
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A text dataset of typed decision examples in parquet format, sized 1M to 10M rows. Rows carry state, question, target distribution, and provenance fields drawn from sources like MedQA and procedural rule-firing tasks.
Spanish-language course repository on Jev and System One, with five Google Colab labs written in pure Python stdlib: fast router, hybrid escalation, realtime guardrails, tool dispatch, and a 1,000-request benchmark, plus an English technical book PDF.
A hands-on lab that arranges thirty real uses of Jev into ten levels: levels 1–5 call Jev from plain code, and levels 6–10 run inside the pi coding agent, ending with an agent that writes its own Jev questions.
A single paste-in prompt for a coding agent that investigates what Jev, TypeSafe's structured decision model, could make possible in an existing project, from replacing parse-the-text calls to using semantic judgments as routine operations.
A minimal repository whose entire README is a two-step recipe for building your own Jev: download Qwen, then make Jev. It is a signpost for readers who want to assemble a System One-style decision model from an open base model.
Six small Python files that build one coding agent step by step on DSPy's RLM sandbox, with Jev answering closed questions — command safety, tool choice, history relevance, delegation — and plain Python owning the thresholds.
Korean-language Fastcampus seminar repo that teaches attaching Jev to a DeepAgents (LangGraph) shopping-mall support agent across 13 notebooks and a React sample app: tool selection, guardrails, risk gates and grounded RAG.
Fifteen open-source go-to-market recipes built on TypeSafe Jev by bcharleson: code does the work, Jev makes the judgment calls, and each recipe runs locally on data you already own. The README reports 16,711 connections scored for $0.73.
A community curriculum of Jupyter notebook recipes that teach typed decisions with Jev, TypeSafe's System One model. Each recipe takes one bounded Choice, Noul or Score decision over text or image state, spanning first classification through closed-loop control.
Runnable samples for Jev, TypeSafe AI's System One model: each sample sends state plus questions that carry their own answer options and then branches on the typed value returned, including a README checker and an AGENTS.md readiness scorer.
Runnable Python examples for Jev, TypeSafe's first System One model: the README explains the three typed question primitives — Choice, Score and Noul — gives a venv install for Python 3.10+ and is explicit that the model cannot chat or generate text.
A Chinese-language share pack on Jev and decision models: two long-form documents, eleven runnable Python examples, a live-demo notebook and a slide deck, covering the three primitives, batch calls, a confidence gate with LLM fallback and Chinese evaluation.
A small Python support-ticket triage pipeline: Jev judges every incoming ticket in one typed call, plain if statements decide whether it is worth a second pass, and an LLM writes a reply only when Jev's scores cross a threshold.
An OpenRouter cookbook recipe that replaces a blanket human-approval rule with a per-call Jev check: the proposed tool call, ticket, and policy go to Jev, whose probabilities route to approve, block, or review.
An OpenRouter cookbook recipe for a draft-verify-escalate cascade: a cheap model drafts an answer from retrieved context, Jev votes supported, unsupported, or declined, and a frontier model rewrites only when verification fails.
A walkthrough that pairs Unsiloed document extraction with Jev: extracted medication rows and their citations become Jev's state, one Noul question per row flags implausible or obscured doses, and the results decide which fields need review.
A support-ticket cookbook that lets Jev decide which requests are worth remembering, then wraps each call so saved tickets ride along in the state. Six later messages route correctly with memory against two without.
A live chat filter that asks three questions per message — category, toxicity, and spoilers — then turns the answers into show, highlight, dim, hide, or blur in plain code. The 29-message example hides seven, blurs two, and highlights six.
Korean lecture-lab code for a three-part Jev course plus a CCTV extra: first calls, agent-specific routers for Claude Code, Codex, and OpenClaw, keyless replays, Colab notebooks, and OneJev video judgment.
Reproducible recruiting cookbook that screens one resume against a job description in a single parallel Jev request of five Noul evidence gates, four Score dimensions and one background-routing Choice, with criteria iterated v1 to v3 against positive, negative and trap resumes.
Describe a startup idea and Jev returns KILL IT, FIX IT, or SHIP IT from one request of ten scored questions, with goal-weighted scoring and a SQLite archive.
An intraday trading bot for the Nifty 50: Jev ranks names every 15 seconds through the Vercel AI Gateway, and code sizes positions, stops, and flatten times.
Two browser games against a live Jev opponent: a first-person quick-scope arena and a neon survival challenge, where Jev picks tactics and authored code handles movement and physics.
An autonomous Pac-Man recreation steered by Jev: before each junction the game sends the maze, actors, and legal directions as structured data, and Jev returns one direction with a probability per option.
A 3D robot that understands without generating text: one TypeSafe call per turn with nine typed questions, then code decides whether to act, ask, or shrug.
A two-hour recorded run of 50 financial workflows with saved outputs, baselines, and failures, from ledger integrity to AML triage and sanctions matching.
Alan's team built an intern-review console on Jev after 4,000+ applications: it checked 3,518 applications in 4 minutes 54 seconds and surfaced 20 for humans.
Browser playground in the TypeSafe console for trying System One: paste state, define typed questions, and inspect answers with probabilities, confidence, latency, and usage.
A writing checker that runs seven named Jev questions, covering hedging, filler phrases, passive voice, tone, and actionability, and shows each verdict with its own confidence.
A System One harness that plays the original StarCraft shareware mission Strongarm by reading structured game state and issuing mouse and keyboard commands from Jev choices.
Pokémon Red under PyBoy where ordinary Python owns the route and arithmetic and Jev is asked only at branches, with each battle turn's faint prediction scored by Brier.
Live Hyperliquid desk across five isolated wallets where each tick packages the book, tape, and position as state and Jev answers Choice questions for long/short, open/close/hold, and leverage; hold sends no order. A missing private key means a dry run.
Local Instagram, TikTok, and LinkedIn research app where Jev makes confidence-gated typed choices over the platform and next socai operation, while deterministic Node code validates each decision and the socai CLI performs read-only browser capture.
A Manifest V3 Chrome extension that finds ad-shaped DOM elements and asks Jev, one noul question per candidate, whether each is a paid advertisement; hits above the threshold are removed.
A small demo app that fetches a URL, turns the page into clean Markdown, and asks Jev ten bounded Choice questions about what a first-time visitor is likely to understand.
A browser Snake game where code computes the legal moves, food distance, and reachable space and Jev picks one move per tick with a single Choice question; a late answer leaves the snake going straight.
Retrieval is a sequence of bounded judgments. How a decision model slots into passage filtering, semantic find, reranking, and citation checks, and what stays in deterministic code.
Jev bills input tokens and gives output away, and questions in one request run in parallel. The design consequence is simple: ask more per call, then decide in code.
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From the Jev community
Watch walkthroughs, read the threads, and see what builders are posting.
YouTube: Retriever AI tests Jev on real browser tasks: where it worked, where it struggled, and what comes next.
YouTube: One terminal, three subscriptions, and Jev picking the model for Claude Code, Codex, and Cursor.
YouTube: A podcast segment on TypeSafe AI, its $40M raise, and the Jevons paradox behind the name.
Hacker News: Software Bombe and live Enigma demo where Jev ranks cribs and judges plaintext.