Interactive comparison of 21 decision models, with direct-provider and OpenRouter routes measured separately on 1,100 public jev-bench items. The charts plot accuracy against benchmark cost, official prices and median latency, with Jev 1.13 and 1.14 measured separately.
Doug Does' benchmark dashboard for JevBench: 534 questions, 48 ranked models, composite scores that already include cost, head-to-head comparison, latency and a Pareto frontier. Jev 1.13.0 tops the table at 74.40.
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A 6.5M-parameter independent chess policy model built from Jev's public description: it returns a calibrated distribution over legal moves in one forward pass, with a self-reported held-out top-1 of 36.5%, ECE 0.036 and Elo 1239 plus or minus 51.
FRIDA-Decisions answers Russian text with structured decisions in a single encoder pass — choice, ordinal score, yes/no or ranking — and scores 0.893 on the 735-item razvilka benchmark, effectively level with TypeSafe Jev's 0.897 (McNemar p = 0.84).
Run outputs behind the JevGPT paper's tables: 830 BrowseComp-Plus records at 90.48% accuracy and 105 NeuCLIRBench topics at 0.5390 nDCG@20, produced by a GPT-5.5 System Two paired with Jev-1.13.0 as System One. Per-item cost, latency and encrypted trajectory files are included. CC BY 4.0.
cnfeichen's reproducible Chinese reranking benchmark for Jev over hard negatives from C-MTEB/T2Reranking: a 4-level Jev Score rubric reaches 0.546 nDCG@5 on 100 queries against 0.413 for BM25 with jieba. Independent community research, not affiliated with TypeSafe. MIT.
A CV/job fit-scoring benchmark from gw0: 250 synthetic CVs, 404 job postings and 5,000 pairs, each judged on 17 typed questions with a score and a confidence, built to train and evaluate the Jev-like jobfit-model.
A full-stack, permission-aware document library from Extend: it browses document trees and parsed output with Extend UI Finder, searches the folder hierarchy with JEV, and answers source-grounded questions with inline citations through the AI SDK.
A local deep-research tool that turns your own PDF, DOCX, PPTX, image, TXT, or Markdown files into a structured report, using TypeSafe's Jev to rerank Qdrant chunks and verify evidence coverage while an LLM plans and writes.
A Chrome and Edge side-panel extension that sorts your existing bookmarks so you can find a saved tool by describing its task. Import classification uses 920+ built-in site patterns with no API key; the Jev decision model is an optional re-ranker for Smart search.
An MCP server and CLI web crawler for AI agents in which a small decision model picks every link and passage, so the agent's LLM reads only what answers its question. On 50 WebWalkerQA tasks the README claims 40% correct answers at about $0.025 per task.
Natural-language lookup over the US App Store top charts: describe an ambiguous feature, a look, or a name and get matching apps with how likely each one is, scored by a single Jev request over a local shortlist.
A single-node memory engine for AI agents in Rust and Python: memories are woven from fact, emotion, temporal, relation, confidence, and frequency filaments into a mesh graph, retrieved by context resonance, and claimed to improve decision accuracy with Jev.
A full-stack monitoring product that collects posts from X, Reddit, Hacker News, RSS, and GitHub, ranks what matters, and turns it into cited AI summaries; its repo description says the feed is based on Jev, though the README does not detail the integration.
Local-first MCP memory server for IDEs: tree-sitter indexing into a local ChromaDB store, plus an optional Jev layer that screens retrieved passages for relevance, evidence, contradiction and prompt injection.
Training code for SearchJev, a fast calibrated System-1 model for search agents: given a search state and a decision schema, it returns typed predictions over legal field values in one forward pass of a Qwen3.5 backbone.
A Claude skill and API from tomascupr that turns any video link into one typed JSON document: summary, chapters, scenes, cast, key moments, the emotional arc, brands, on-screen text, and the full transcript. Jev then judges those fields so your code can branch on probabilities.
A search API over about 203 million OpenAlex English title and abstract records, running DenseOn embedding search by meaning and a tantivy index for exact words behind one HTTP service. Its whole-field topic maps use TypeSafe's Jev to judge archetype and cited papers.
A zero-embedding approach to video intelligence from DeepAleph: jevreel lays a video out on labelled contact sheets, one frame per second and sixty frames to an image, and has Jev judge every sheet in parallel. Your coding agent gets moments, times and probabilities back over MCP.
An agent skill plus Python CLI that turns long interviews and podcasts into evidence-based highlight reels: the agent reads the transcript, Jev scores the text evidence, and local FFmpeg cuts a rough cut that fits your taste and target duration.
An independent agent skill and Python CLI for local video remixing: shot-aware slots for beats and original narration are filled under Jev judgments and constrained orchestration, then rendered into a reviewable cut with originality and asset-license reports.
A local web app where Claude Code runs the hunt and Jev judges every competitor post, surfacing the ones most likely to work for your audience. It adds saved-post analysis, transcription, an Ask AI view and a My Studio planning page.
A carbon-aware load scheduler that shifts deferrable electricity loads — EV charging, water heating, laundry, cooling — into the cleanest grid hours without missing a deadline. Jev classifies loads and ranks which matters first; OR-Tools CP-SAT keeps deadlines and physics hard while carbon is the objective.
A provider-neutral CLI, MCP server and Claude Code plugin that asks yes/no, pick-one, rate or rank questions about text and gets probabilities instead of paragraphs. Backends include Jev (TypeSafe or via OpenRouter), OpenAI's Decisions API, and Claude with an existing Claude Code login. MIT, Node 20+.
A Python toolkit that encodes profile criteria as 64- or 256-value decision vectors with Clef or Jev, retrieves candidates with HNSW search, then checks mandatory conditions in both directions and ranks the rest by preference coverage. A 52-profile benchmark compares the vectors against BM25 and BGE-small.
A Claude Code-based job application tool that finds software internships and new-grad roles, fills application forms in a Chrome window, reads every answer back from the page and sends the forms you approve. JEV makes every fixed-answer choice for about 1 to 1.5 cents per 10 applications.
An installable Electron email client for Windows, with a macOS build target, that sorts mail with Jev: one call per message classifies it into Important and Other and rates relevance. TypeSafe, Vercel AI Gateway and OpenRouter endpoints all serve Jev; local sorting works without any key.
A TypeScript-first toolkit billed as the standard library for System One models: ready-made classify, extract, pickOne, rank and verify methods, a service-agent runtime that runs conversations turn by turn, and a one-message order parser. A Python port is in preview and the project is in alpha.
An unofficial Chromium extension that adds a find-in-page bar: literal matches highlight instantly and locally, and with your own TypeSafe API key Jev also finds passages by meaning after you pause typing. Uncertain results never auto-scroll, and paid searches start only while you are in the bar.
A Rust MCP server that recommends third-party agent skills: it retrieves deduplicated metadata with SQLite FTS5/BM25, sends one comparable typed Score per candidate in batches of at most 128 questions, and never installs or executes a skill.
A Next.js visual reference app for AI filmmakers, powered by Jev: describe a story and explore the filmmakers, colors, lighting and compositions that could bring it to life, drawn from 24 director profiles, 47 films and 282 image references.
A local System One decision layer for AI coding agents, shipped as an MCP server and Agent Skill, that offloads high-volume classification, checks, scores and ranking from frontier LLMs to sub-50ms local models with calibrated output.
An oh-my-pi extension that puts consequential agent decisions to Jev (jev-latest) through the TypeSafe API, checking visible actions and completion boundaries. Its ordinary-session entry is deliberately disabled: an MVP under verification, not a release.
A Chinese-language Next.js site that takes your situation and picks 3–5 fitting items from 671 suggestions in a life guide, ranked by Jev choice, noul and score questions with evidence grades, costs and sources shown.
A pi coding-agent extension that ranks long-term memory files against the current context with a yes/no classifier read from native token logits. It triggers on cold start, post-compaction and an on-demand tool, and offers a prior, not a verdict.
A hackathon onboarding system for ScaleupMap that replaces a static questionnaire with a Jev AI System-1 multi-factor scoring engine, producing a normalized six-dimensional cognitive weight vector and affinity scores across seven platform destinations.
An EV charging-stop planner: describe your situation in one sentence, set origin, destination, battery and arrival time, code filters candidates from Open Charge Map, and Jev picks the stop that fits your request.
A Python tool that reads every open issue in a GitHub repo and uses TypeSafe's Jev model to rank them for first-time contributors by how small, clear and self-contained they are, flagging issues someone has already claimed.
A Go CLI that scores how well a resume fits a job posting with Jev, in under a second and for about $0.0001, and shows which requirements the resume covers; the README claims roughly 18 times the speed of a frontier-model prompt.
A small Python sample that uses OpenThai-SystemOne to recommend which of Windows, macOS, Linux Desktop, Android and iOS/iPadOS best fits a user's needs. A generic decision() helper builds one Noul question per option and sorts the match scores.
A Chrome extension that answers questions about any YouTube video by asking Jev which transcript window holds the answer: a Choice over about 30-second window ids plus a Noul for whether the video covers it at all, then it jumps to the moment.
A provider-neutral .NET SDK for decision models — structured questions answered with probabilities instead of generated text. Its primary model is Cloudflare Clef, and settings switch it to Cloudflare Workers AI, OpenRouter or any compatible server.
A pi agent extension that routes TypeSafe's Jev classifier through OpenRouter to block destructive tool calls, verify test and build output, and rank workspace files for a task. It fails open and costs about $0.00002 per call.
A Python research project that uses Jev (TypeSafe AI, System 1) with Neo4j to traverse cybersecurity knowledge graphs probabilistically, choosing promising graph edges and scoring nodes against a goal instead of generating text.
A Netflix-style movie recommender whose picks come from Jev: four typed questions deep, ranked in about half a second, with a probability and explanation attached to every recommendation. Built with Next.js 16 and React 19.
A repository described as Jev use cases; the captured README documents an Austrian hotel ranking pipeline that merges Google Maps and Booking.com data, matches the ÖHV member list from meinhotel.at and computes a 0–100 guest score, with dashboards and Excel reports.
A marketing decision layer that knows when to stay silent, built with Jev and jev-state: Tacet counts and enforces limits in code, Jev chooses one next best action or none, and a person reviews any choice Jev is unsure about.
A sidecar plugin that puts fast typed-decision models such as TypeSafe Jev and Laya behind two tools over one wire format (POST /v1/systemone): judge asks typed questions about a state and returns probabilities, while semantic_find scores file chunks against a plain-language query.
AutoTrust's bf16 System 1 decision model on Qwen3.5-35B-A3B with about 3B active parameters: Decision Index 0.3 public 59.73, ERQA 51.3% in one pass with zero robot training, 66.5 GiB of weights.
A monorepo of roughly 20 latency-focused demos: moderation firehoses, rerankers, inbox triage, macOS launchers, and closed-loop control simulators, each with p50/p95 telemetry.