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Recent activityA FIELD GUIDE TO JEV / VOL. 01
Lo mejor de Jev: proyectos reales, guías prácticas e ideas de toda la web.
THE OFFICIAL SIGNAL 4 HIGHLIGHTS
Diogo Almeida@CompleteSkepticLAUNCH POSTDiogo Almeida presenta Jev, el modelo de decisión estructurado de TypeSafe. Vea el film de lanzamiento y explore el hilo original para el enfoque del modelo, ejemplos y rendimiento informado por el autor.
TypeSafe AI@typesafeaiOFFICIALEl anuncio oficial de lanzamiento de TypeSafe presenta el laboratorio y orienta a los desarrolladores a Jev. Incluye el film de lanzamiento de Diogo Almeida y su introducción original.
TypeSafe AI@typesafeaiOFFICIALTypeSafe anuncia el acceso público a Jev sin lista de espera. La consola oficial es el punto de partida para probar decisiones tipadas en su propia aplicación.
JEVLAB ARTTypeSafe AI@typesafeaiOFFICIALTypeSafe destaca el enfoque de salida tipada de Jev: las aplicaciones proporcionan estado y preguntas, luego actúan sobre probabilidades, elecciones o puntajes. La anunciación vinculada también cubre la integración de la API de Venice.
JEVLAB ARTStart with the Launch Post. Explore four official signals.
94 recursos
AI-assisted summaries and translations. Check original sources for context and performance claims.
Kevin GrajedaClasificador que evalúa en tiempo real la dificultad de las instrucciones, permitiendo un modo rápido para instrucciones simples para mejorar la interacción del usuario.
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Higgsfield AI 🧩Demostración donde Jev selecciona los modelos de IA más adecuados para tareas de generación de video e imagen en Higgsfield.
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A guide explaining Jev's approach to making typed decisions rather than processing text.
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A GitHub Action using Jev for automated PR triage and classification.
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A prototype that classifies symptoms and updates diagnoses using medical ontologies during live clinical consultations.
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Jev is demonstrated as a classification model with practical examples.
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Marketing analytics platform whose feature flag routes brand-visibility classifiers off an LLM and onto Jev boolean decisions.
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A setup where Jev selects between different AI models for specific tasks, streamlining model usage in a single terminal.
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MovezAnalyzes 100,000 X posts in 20.4 seconds using 14 yes/no questions per post for viral potential assessment.
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ZachiAn ad blocker extension using AI for real-time classification and removal of ads.
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YumA community post detailing a dashboard that classifies X posts across eight dimensions using Jev for content research.
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Marc KöhlbruggeA real-time computer assistant using Jev and local Whisper to process voice input, classify actions, and interact with the screen through a Swift app for accessibility.
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iagolastA community example demonstrating how Jev can classify company invoices into accounting categories rapidly.
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x.comJev enables flexible, type-safe classification as a fundamental programming primitive.
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A Jev-powered system that analyzes PDFs page by page to determine which require OCR processing.
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Hamilton UlmerDuckDB extension that classifies rows in CSV, Parquet, or DuckDB tables with Jev, about 10 seconds per 1,000 rows.
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Runnable Jev examples through OpenRouter for immediate implementation.
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A tool that scores video ad shots in 1.5 seconds using Maxfusion and Jev, processing over 450 ads in under three minutes.
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vogelA system tested on 1,500 emails for classification effectiveness. Demonstrates potential for automated email management.
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Rob HallamSuperX integrado con Jev permite una puntuación rápida de publicaciones, analizando 61 preguntas por publicación en menos de un segundo con alta precisión en la predicción del potencial viral.
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x.comJev is a System One model on OpenRouter that provides decisions with probabilities.
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A community post presents a codebase complexity classifier using Jev, aiming to address overengineered code.
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1,800-point thread debating whether typed decisions replace LLM calls for classification, routing, and scoring.
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Benchmarking TypeSafe's Jev decision model as a cost-efficient LLM router on RouterArena.
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Comment-moderation playground: paste a comment, Jev decides what to do with it.
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Justine MooreJev enables natural language search for Zillow listings, classifying properties by non-standard filters.
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Self-hosted AI email classifier for Gmail powered by Jev. Create custom labels, organize your inbox, and filter spam with confidence and cost controls.
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Helper n8n community node for Jev by TypeSafe. Classify, route, and score text with questions you define, and get a probability for every answer so unsure items can go to review.
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A guide on using Jev to route between models and block risky tool calls.
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A developer project for lead scoring in Clay using Jev.
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A GitHub project routing tasks by selecting model tier, tools, skill, and effort.
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Chris AdcockJev enables Grok Bot to control real Chrome for faster, automated tasks instead of slow look-and-click methods.
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Pre-alpha PostgreSQL extension for TypeSafe AI (Jev) categorical classification.
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SabrinaA demonstration of Jev's capability to quickly categorize a large volume of Hacker News posts.
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Calibration and confidence-based routing measured on Banking77: 80.2% accuracy at $0.103 per 500 decisions.
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Jev evaluates the pacing of a frontier-AI essay written by @DarioAmodei.
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Jiaqi GuA project using Jev for skill routing in an agent system, based on community development.
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Screen a folder of CVs with the TypeSafe Jev decision model: typed judgments, an editable policy, free re-scoring.
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A resource detailing a test of Jev on 12 real-world scenarios, including speed, cost, and practical applications.
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Eight minimal working examples of TypeSafe's Jev (a System One model) applied to mechanical and electrical engineering: CAD/CAE/CAM routing, FEM result triage, DFM screening, BOM alignment, hallucination-proof extraction. Zero dependencies.
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A GitHub project that uses Jev to triage and route posts from the live Bluesky firehose with human oversight.
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Fayaz AhmedAn image classifier using OCR and Jev processes 900 images in 40 seconds.
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Uses Jev to decide which AI model to use before generating output, based on prompt and quality requirements.
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Async LangGraph workflow that gets a typed Jev Choice (invoice or general) and routes each inbound email to the matching handler.
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An optimized inference engine to turn LLMs into Jev-like machines: optimized for quick, lightweight, and accurate decision-making, classification, and scoring.
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Riley BrownJev classifies 500 emails quickly and inexpensively, demonstrating its efficiency in processing tasks.
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An educational Jev-like visual inference experiment on Apple Silicon: shared context, direct candidate scoring, and local visual demos.
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Classifies tax documents using an LLM pipeline for efficient processing.
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A developer project exploring decisions using a classifier as a next-token predictor.
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Real-time Discord moderation bot: Jev evaluates messages and metadata in parallel to catch phishing, spam, and social engineering with a progressive escalation ladder.
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One direct Jev question per row against 12–14 Jev-scored dimensions with locally fitted weights on three classification tasks: 5,477 test rows, 25,174 Jev calls, $1.43. Decomposition wins on Japanese NLI (0.9076 vs 0.8373) but flags about 25× more hard benign rows as attacks (37.2% vs 1.5%).
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LlamaIndex reranker + router powered by TypeSafe Jev — typed scores/choices, cheaper than LLM-as-judge.
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Neon Function proxy for the Neon AI Gateway with TypeSafe Jev routing.
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Short practical intro with a Python ticket-triage example.
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HassanA project classifying 1,018 AI research papers using Jev, achieving low cost and fast processing for organizing academic work.
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Nick KhamiA demo showing how to make open source models behave like Jev using inference engineering and scoring endpoints for decision-making.
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A .NET 10 and React 19 application for fast, structured AI-powered ticket triage using TypeSafe Jev.
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Trinay HariA 26-sheet construction plan set was classified in 2.9 seconds using Jev with an LLM pipeline, costing $0.0052 and matching GPT-4.1 and GPT-6 Astra performance.
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GitHub project for content moderation with category-specific probability thresholds. Enables automated content filtering.
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OpenRouterJev demonstrated over 5x faster performance than other models in testing.
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Jev is utilized for decision-making processes on Cloudflare, including support routing and risk management.
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TypeSafe (Jev) vs DeepSeek-flash: side-by-side speed/token/cost/accuracy comparison across invoice extraction, email classification, and reranking.
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Classify your inbox with Jev (TypeSafe's System One model) — tag, move, flag, and notify, all config-driven.
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ACME live support-call scoring demo with TypeSafe AI, Effect, SQLite, React, Vite, and Turborepo.
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Nikunj KothariA task using Jev to score 3,000 kids' snacks with multiple criteria in 28 seconds.
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A community post about using Jev for incident triage in Box, returning boolean results with probabilities.
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An open-source project that helps select relevant Claude Code skills by prioritizing frequently used ones through Jev integration.
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x.comJev offers structured decision-making for security workflows with high speed.
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Classify Git commit diffs and messages with Jev. Bug fixes, security fixes/CWEs, and change types.
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Not every coding task needs your best model. Experimental Jev-powered model routing for Claude Code — V3 prototype runs today, V4 routes at the task boundary.
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Experimental semantic line search with TypeSafe Jev via OpenRouter. Python CLI with no runtime dependencies.
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Autonomous System-One Triage Engine & Benchmark powered by TypeSafe AI (Jev). 75ms inference, $0 output tokens, and RLCD epistemic safety gates.
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4 HIGHLIGHTS
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4 clicks / 7d
Decisions & routing
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