Introducing System One Models & Jev
Launch post defining System One models and announcing Jev in early access, with claimed 70-500ms latency versus 3-329s for frontier LLMs and $0.042/MTok input pricing.
Article
Examples built on System One models (page 15 of 16)
Entries 673 to 720 of 743, in the hub's default order. The introduction is on page 1.
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Showing 673 to 720 of 743
Mini-Vibe Check: TypeSafe's Jev Judged Everything I've Written in 0.7 Seconds
Every's head of evals runs Jev across his own writing. Argues the model turns fuzzy questions into probabilities fast and cheaply enough to check an AI agent's work as it goes.
Mike TaylorOpenMini-Vibe Check: TypeSafe's Jev Judged Everything I've Written in 0.7 Seconds on every.toArticleLLM guardrailsTypeSafe AI Emerges From Stealth With $40M in Funding
Official press release announcing a $40M seed round led by DCVC alongside the Jev launch. Fetch returned HTTP 403, so details were taken from search snippets and secondary coverage.
TypeSafe AI (press release)OpenTypeSafe AI Emerges From Stealth With $40M in Funding on businesswire.comArticletypesafe-sdk on PyPI
PyPI listing for the official Python SDK, version 0.6.0, MIT licensed. Requires Python 3.10+.
TypeSafe AIOpentypesafe-sdk on PyPI on pypi.orgAI: too good to be true, too bad to be useful
TypeSafe AI blog post from June 2026, three months before the Jev launch. The body did not render when fetched; community lists describe it as the company's argument against using preference-optimized chat models for automation.
Articlesales-conversion-model-reinf-learning (claimed 2025 Jev precursor)
MIT-licensed PPO model over BAAI/bge-m3 sequence embeddings that emits turn-by-turn conversion probabilities without generating text (arXiv 2503.23303). Its author claims on Hacker News it is the same non-autoregressive, schema-constrained architecture Jev later shipped, a year earlier.
DeepMostInnovations (nandakishor_ml)Opensales-conversion-model-reinf-learning (claimed 2025 Jev precursor) on huggingface.co
@ai-sdk/typesafe-ai (Vercel AI SDK provider)
Official AI SDK provider exposing Jev through the experimental evaluate API in AI SDK 7. Supports Choice (1-255 options), Score (2-10 ordered levels) and Boolean/Noul, batching all questions in one request against the same state.
Companion, a Jev demo
A 3D robot companion driven by Jev: one call per turn answers nine typed questions, and code decides whether to act, ask or shrug. The panel shows the tracked mood and what the robot decided.
jev-demo.vercel.appOpenCompanion, a Jev demo on jev-demo.vercel.appCookbook: Agent skill suggestion
Picks which agent skill to load for a given task by scoring candidate skills against the current state.
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.orgmeraGPT Decider 1 cookbooks
meraGPT's official cookbook for its Decider 1 model: seven recipes, each with a runnable curl, Python and TypeScript request and the unedited output. They cover support triage, confidence gating, phishing and output guardrails, RAG passage filtering, picking an agent's first tool and asking many questions in one call.
Moderation 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.
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