Jevのリリース投稿
ドイゴ・アレイダがJevを紹介し、TypeSafeの構造化された意思決定モデルを説明します。リリース映像を視聴し、オリジナルスレッドでモデルのアプローチ、例、および著者によるパフォーマンスを確認してください。
ドイゴ・アレイダがJevを紹介し、TypeSafeの構造化された意思決定モデルを説明します。リリース映像を視聴し、オリジナルスレッドでモデルのアプローチ、例、および著者によるパフォーマンスを確認してください。
TypeSafeの公式リリース発表では、ラボの紹介とビルダー向けのJevへの指向が含まれています。ドイゴ・アレイダのリリース映像とオリジナルの紹介が含まれます。
TypeSafeはJevの公開アクセスを発表し、待機リストなしで利用可能にしています。公式コンソールは、独自のアプリケーションで型付き意思決定を試すための出発点です。
TypeSafeはJevの型付き出力アプローチを強調しています。アプリケーションは状態と質問を提供し、確率、選択肢、スコアに基づいて動作します。リンクされた発表では、Venice APIの統合も紹介されています。
Start with the Launch Post. Explore four official signals.
35 件の資料
AI-assisted summaries and translations. Check original sources for context and performance claims.
リアルタイムでプロンプトの難易度を分類し、簡単なプロンプトには高速モードを有効にしてユーザー体験を向上させる。
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Higgsfieldでビデオや画像生成タスクに最適なAIモデルを選択するデモ。
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A community post detailing a dashboard that classifies X posts across eight dimensions using Jev for content research.
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A task using Jev to score 3,000 kids' snacks with multiple criteria in 28 seconds.
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DuckDB extension that classifies rows in CSV, Parquet, or DuckDB tables with Jev, about 10 seconds per 1,000 rows.
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A community post presents a codebase complexity classifier using Jev, aiming to address overengineered code.
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A demonstration of Jev's capability to quickly categorize a large volume of Hacker News posts.
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A Jev-powered system that analyzes PDFs page by page to determine which require OCR processing.
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Analyzes 100,000 X posts in 20.4 seconds using 14 yes/no questions per post for viral potential assessment.
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Jev classifies 500 emails quickly and inexpensively, demonstrating its efficiency in processing tasks.
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Jev is a System One model on OpenRouter that provides decisions with probabilities.
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A community example demonstrating how Jev can classify company invoices into accounting categories rapidly.
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An ad blocker extension using AI for real-time classification and removal of ads.
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Jev enables natural language search for Zillow listings, classifying properties by non-standard filters.
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Jev enables flexible, type-safe classification as a fundamental programming primitive.
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Jev demonstrated over 5x faster performance than other models in testing.
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Jevと統合されたSuperXは、ポストスコアリングを高速化し、1秒未満で61の質問を分析し、高いウイルス性の予測精度を実現。
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An image classifier using OCR and Jev processes 900 images in 40 seconds.
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Jev evaluates the pacing of a frontier-AI essay written by @DarioAmodei.
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Classifies tax documents using an LLM pipeline for efficient processing.
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A community post about using Jev for incident triage in Box, returning boolean results with probabilities.
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A project using Jev for skill routing in an agent system, based on community development.
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Jev offers structured decision-making for security workflows with high speed.
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A project classifying 1,018 AI research papers using Jev, achieving low cost and fast processing for organizing academic work.
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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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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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A prototype that classifies symptoms and updates diagnoses using medical ontologies during live clinical consultations.
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A 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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Matched-precision comparison against a private fine-tuned classifier.
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Near-real-time scoring of TikTok and Instagram hooks against about 100 personas.
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A system tested on 1,500 emails for classification effectiveness. Demonstrates potential for automated email management.
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Jev enables Grok Bot to control real Chrome for faster, automated tasks instead of slow look-and-click methods.
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A 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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A demo showing how to make open source models behave like Jev using inference engineering and scoring endpoints for decision-making.
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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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