Browser Use + Jev
グレゴール・ズニクの動的DOMアクションスペースを備えたフライト検索デモ。
A FIELD GUIDE TO JEV / VOL. 01
Jevのプロジェクト、実践ガイド、世界中のアイデアをひとつに。
グレゴール・ズニクの動的DOMアクションスペースを備えたフライト検索デモ。
01 / A small decision engine. A whole new way to build.
94 件の資料 / コレクション
絞り込みを解除AI-assisted summaries and translations. Check original sources for context and performance claims.
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。
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。
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。
Screen a folder of CVs with the TypeSafe Jev decision model: typed judgments, an editable policy, free re-scoring.
A GitHub Action using Jev for automated PR triage and classification.
A .NET 10 and React 19 application for fast, structured AI-powered ticket triage using TypeSafe Jev.
A project classifying 1,018 AI research papers using Jev, achieving low cost and fast processing for organizing academic work.
A setup where Jev selects between different AI models for specific tasks, streamlining model usage in a single terminal.
GitHub Action for issue triage that abstains: label, spam, needs-info and duplicate in one call, each applied only above a threshold you set, and nothing at all below it.
Short practical intro with a Python ticket-triage example.
Jev classifies 500 emails quickly and inexpensively, demonstrating its efficiency in processing tasks.
SQL with natural-language predicates, powered by TypeSafe's Jev. Filter, rank, classify and score rows by meaning — batched, cached and cost-guarded.
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.
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.
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.
A Jev-powered system that analyzes PDFs page by page to determine which require OCR processing.
A system tested on 1,500 emails for classification effectiveness. Demonstrates potential for automated email management.
GitHub project for content moderation with category-specific probability thresholds. Enables automated content filtering.
Diffusion-style pixel art out of a classifier: 256 parallel per-pixel Jev questions plus refinement passes.
Async LangGraph workflow that gets a typed Jev Choice (invoice or general) and routes each inbound email to the matching handler.
Three composable judgment pipelines on TypeSafe's Jev: support-ticket triage, observability alert triage, and a deploy-risk gate.
An image classifier using OCR and Jev processes 900 images in 40 seconds.
A GitHub project routing tasks by selecting model tier, tools, skill, and effort.
Calibration and confidence-based routing measured on Banking77: 80.2% accuracy at $0.103 per 500 decisions.