Cookbook: Agent skill suggestion
Picks which agent skill to load for a given task by scoring candidate skills against the current state.
Jev projects and examples: 638 builds on GitHub, X and YouTube (page 13 of 14)
Entries 577 to 624 of 638, in the hub's default order. The introduction is on page 1.
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Showing 577 to 624 of 638
Cookbook: Autoresearch feature discovery
Generates and tests candidate features over a dataset by scoring each one with Jev.
Cookbook: Batching parallel questions
Shows how to send many questions about the same state in a single request instead of issuing one call per question.
CookbookCookbook: Classification using confidence
Uses the confidence value returned with Choice and Score answers to decide when to act automatically and when to escalate.
Cookbook: Classifying RAG passages
Filters retrieved passages for relevance before they reach the generation step of a RAG pipeline.
CookbookRAG passage filteringCookbook: Date extraction
Extracts dates from text using pre-parsing plus Choice questions, working around the documented weakness of jev-1.13 at date arithmetic.
CookbookStructured extractionCookbook: Double-checking citations
Verifies that each citation in generated text is actually supported by the cited source.
Cookbook: Function calling from natural language
Maps a natural-language request onto a function and its arguments using Choice questions, as a cheaper substitute for LLM tool calling.
Cookbook: Guardrails for LLMs
Screens LLM inputs and outputs with typed checks so unsafe or off-policy content is caught before it is used.
Cookbook: Hierarchical classification
Classifies into a deep taxonomy by walking the tree one Choice question at a time instead of flattening every leaf into one list.
CookbookHierarchical classificationCookbook: Knowledge graph entity alignment
Decides whether two records refer to the same entity when merging knowledge graphs or databases.
CookbookEntity alignmentCookbook: Line-by-line semantic search (semantic find)
Runs a semantic grep over a file by asking a noul question per line, turning meaning-based lookup into a batch of typed decisions.
Cookbook: Pre-parsed value extraction
Uses code to enumerate candidate values first, then asks Jev to choose among them, avoiding the numeric weaknesses of the model.
Cookbook: Re-ranking with semantic search
Uses Jev scores to re-rank candidate results returned by a first-stage retriever.
Cookbook: SDE cascade
Chains cheap Jev decisions in front of expensive steps so only the uncertain cases escalate.
Cookbook: Self-consistency with Choice questions
Applies the same repeated-sampling self-consistency check to Choice questions so disagreement between samples becomes a signal to escalate.
Cookbook: Self-consistency with Noul questions
Samples a noul question repeatedly and routes uncertain probabilities to human review while keeping the underlying values visible. Worked example uses insurance claim evaluation.
Cookbook: Structure recovery from plain text (autoformat)
Recovers structured fields from unstructured plain text by asking typed questions rather than prompting an LLM for JSON.
CookbookStructured extractionCrowdcheck
Live demo that tests a post against a crowd of 10,000 AI readers with their own jobs, tastes and memories. Jev answers whether each one likes, dislikes, reposts, follows or blocks, and every reaction comes from an AI reader, not a real person.
crowdcheck-ai.vercel.appOpenCrowdcheck on crowdcheck-ai.vercel.appProjectDemo: Smart home assistant
Interactive demo that evaluates smart home requests with speculative questions and falls back to an LLM when Jev is uncertain.
dsh-auto-mode
Auto mode preset for the DeepSeek Harness coding agent, hosted on a self-run GitLab. At every end prompt, Jev decides whether the agent's open questions can be answered without the human, so full-access runs stay guided by a typed decision.
Hunter L. AllenOpendsh-auto-mode on git.allen-software.comHollow Creek
Browser village where nobody follows a script. Each villager watches what you do, remembers it and decides how to treat you, answering with expressions and actions rather than talk, and a side panel shows the state sent to Jev, the questions asked and the answers returned.
hollow-creek-sigma.vercel.appOpenHollow Creek on hollow-creek-sigma.vercel.appProjectReal-time controlJev 1.13 jaggedness (known limitations)
Official page listing what jev-1.13 does badly: literal interpretation, unreliable counting, weak numerics and date ordering, multi-hop indirection, distraction from irrelevant state, and no built-in prompt-injection defence.
Cookbook
Jev on Cloudflare Workers AI
Cloudflare Workers AI model catalog page for TypeSafe Jev, documenting the state plus questions request shape and the Noul, Choice and Score answer types. Examples cover ticket routing, refund review and risk assessment.
Jev on OpenRouter (TypeSafe provider page)
OpenRouter lists two TypeSafe models, typesafe/jev-latest and typesafe/jev-1.13, both with a 32K context window, at $0.042/M input and $0/M output.
Jev plays Pac-Man
Pac-Man recreation steered by Jev from structured game state. Before each junction the game sends the maze, the actors and the legal directions, and Jev answers with one direction and a probability for each option, with no screenshots, no scripted bot and no hidden pathfinding.
jev-pacman.ephraimduncan.comOpenJev plays Pac-Man on jev-pacman.ephraimduncan.comProjectReal-time control
Jev: TypeSafe's Decision Model, Speed and Cost Explained
Separates vendor-published numbers from independently observed ones, noting the architecture is unpublished, weights are not released, and the headline benchmark is internal workflow evals rather than a public leaderboard.
Articlejev.directory
Community directory site listing systems built on Jev and the primitives each one sends. Users sign in with GitHub to submit projects from a repo or an X post, and can copy examples into a playground.
communityOpenjev.directory on jev.directoryTool
Jevable: discover what people build with Jev
Showcase of 342 community projects built with Jev, each page linking to the maker's X post with the demo video. Grouped into eleven categories including Games, Developer tools, Agents, Browser extensions and Robotics.
List
JevBench by Benchmark Heaven: Jev-class decision model leaderboard
Benchmark Heaven's own leaderboard for Jev-class decision models, unrelated to the dhruvmehra/jevbench repo, ranking 106 of 112 systems on 1,624 choice, score and noul decisions each in release v1.5.4. Jev 1.13.0 leads on capability at 80.0, while on the four-axis composite that adds speed and cost, Cygnet and Winnow-12B Q8 tie first and Jev is third.
Florian Standhartinger (Benchmark Heaven)OpenJevBench by Benchmark Heaven: Jev-class decision model leaderboard on benchmarkheaven.comArticlelangchain-typesafe
LangChain integration package (0.0.1a2 pre-release, MIT) providing TypeSafeClassifier as a Runnable plus experimental agent middleware: ModelRouterMiddleware, SkillsMiddleware and AutoModeMiddleware for tool-risk gating.
LangChainOpenlangchain-typesafe on pypi.orgModeration Guard
Moderation playground that screens text in one System One request: parallel Noul probabilities for profanity, hate, sexual content, violence, illegal help, self-harm and spam or jailbreak, plus a severity Score. The verdict comes from policy thresholds in your code, review at 0.35 and action at 0.70.
guard-jev.vercel.appOpenModeration Guard on guard-jev.vercel.appProjectLLM guardrailsPattern: Composite scoring
Combines several independent score dimensions into a single ranking or decision number.
Pattern: Confidence-gated routing
Uses the returned confidence value as a routing input: act on high confidence, confirm on medium, escalate to a human or an LLM on low.
Pattern: Intent routing
Classifies an incoming request into an intent with a Choice question and dispatches it to the matching handler.
Pattern: Speculative fan-out
Sends many questions in one call, including speculative ones, and lets application code decide afterwards which answers matter.
Cookbookr/accelerate: New TypeSafe AI Jev model playing Minecraft (WIP)
Work-in-progress demo of Jev driving a Minecraft agent, including fleeing zombies at night, as a test of fast structured decisions in a game loop. URL taken from a fetched awesome-jev README; thread not opened.
DiscussionReal-time controlr/PiCodingAgent: Anyone else testing out TypeSafe AI's new System One model?
Thread where builders describe using Jev as an agent tool-use safety layer and plan a prompt-complexity model router on top of it. URL taken from a fetched awesome-jev README; thread not opened.
r/singularity: TypeSafe AI releases AI model called Jev
Launch thread framing Jev as a low-hallucination, low-cost decision model for software rather than a chat model. URL taken verbatim from a fetched awesome-jev README; the thread itself could not be opened, so score, author and date are unconfirmed.
Room, powered by Jev
Interactive 3D room that reacts to a few words: Jev sets lighting, curtains, music, TV, volume and the desk lamp, and the app renders the result. Shows the raw API response and an estimated per-call cost, with presets that make no call at all.
jev-room.moe136231.chatgpt.siteOpenRoom, powered by Jev on jev-room.moe136231.chatgpt.siteProjectSystem One (official docs concept page)
Official documentation page defining the System One model class and how it differs from an LLM. States the model evaluates a state and returns typed answers with calibrated probabilities, and that images, audio and video are not supported.
ArticleTetris x TypeSafe
Tetris you can play yourself or hand over to the TypeSafe Choice primitive, which picks every move. Score, lines and level are tracked while it plays.
jev-omega.vercel.appOpenTetris x TypeSafe on jev-omega.vercel.appProjectReal-time controlThe Bitterest Lesson
TypeSafe blog post arguing the case for machine-native models over chat-shaped ones.
TypeSafe AIOpenThe Bitterest Lesson on typesafe.aiArticle
The Neuron Daily: TypeSafe's JEV System One model skips the chatbot part of AI
Newsletter issue covering the Jev launch and what it means for teams currently using LLMs as classifiers. Reports TypeSafe's 20-200x faster and 40-400x cheaper figures.
The Neuron DailyOpenThe Neuron Daily: TypeSafe's JEV System One model skips the chatbot part of AI on theneurondaily.comArticleTypeSafe Agent Skill (docs)
Official docs for the TypeSafe agent skill, which gives a coding agent context on the three question types, architectural patterns and evaluation practice. Installs via Claude Code plugin, npx skills, or manual copy.
TypeSafe AI's Jev Is Not an LLM, And That May Be the Point
News analysis arguing the significance of Jev is the category rather than the benchmark numbers, since it targets decisions software makes internally rather than user-facing conversation.
ArticleTypeSafe console playground
Official browser playground for composing state and typed questions against Jev, with shareable links. Access is gated behind the early-access waitlist.
ToolTypeSafe HTTP API reference
Reference for POST https://api.typesafe.ai/v1/systemone, covering the state/model/questions request body, the answer shapes, and error codes 401, 422, 429 and 529.
Cookbook
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