数据抽取与文档

typesafe-computer-use 根据文字目标操作 Mac，通过 OCR 和辅助功能控件读取桌面。 TypeSafe Jev 选择动作、目标和网站；代码提供事实，独立的写作模型负责自由文本。 需要 TypeSafe 密钥、屏幕录制和辅助功能权限。命令行支持先预演，再实际控制鼠标和键盘。

JevChat-Windows 通过本地 OCR 读取微信和 KakaoTalk 对话，在聊天窗口旁提供候选回复。 Jev 判断意图、紧张度并为三条候选排序；独立语言模型负责起草。 在 Windows 10 1903+ 或 Windows 11 上，分别配置判断服务与起草服务的密钥。

该库通过表单描述 JSON 名录，对提取的文本识别 IRS 表单和页面类型。 向 classifyPage 提供文本行、jevBackend 和 criteria.json。PDF 辅助功能还需 Poppler，后端需要 TypeSafe 密钥。

DocJev 可分类 PDF、DOCX 和 PPTX，或把合并文档拆为带类别的页码范围，并可导出 PDF。 用 uv 安装 Python 包，导出 TYPESAFE_API_KEY，再提供分类规则运行 docjev classify 或 split。Office 格式需要 LibreOffice。

工具提取文档内容并在 Qdrant 建索引，规划提纲后根据本地资料逐节研究。 Jev 判断并重排检索片段、评估证据缺口；独立大语言模型负责规划、查询和写作，Python 控制重试。

Data extraction for systematic reviews, quoted from the papers. Ask a trial report and its supplements your extraction form or a RoB 2, ROBINS-I, QUADAS-2 or TIDieR template; Jev points at the lines, every answer is a verbatim quote with its page, you check it and export the table. Files stay in your browser.

A Document OCR Router to help route pages based on content.

Open-source macOS AI computer use and native GUI automation on Apple silicon. Jev + OmniParser CoreML + Apple Vision OCR. Bring your own OpenRouter, Vercel AI Gateway, or TypesafeAI token.

Use Jev (TypeSafe's System One model) as a calibrated reranker: one call, up to 30 documents, a probability per document. Apache-2.0.

Route form submissions to the right people with Jev and AI SDK.

Windows 微信回复助手（非官方修改版）：本地离线 OCR 读屏 + 模型起草候选回复 + 一键填入微信输入框，可选自动发送。不 hook、不注入、不读微信数据库。

DeepSeek + TypeSafe JEV 微信回复助手：Windows 正式版、Apple Silicon macOS 预览版；本地 OCR 与人工可控回复

JEV Document Classification enables the rapid and cost-effective classification of text-based documents using AI, leveraging TypeSafe's "System One" model.

DeepSeek Harness (dsh) 插件:论文伴读工作台 —— PDF 转录 / 检索 / 流式问答 + 内置阅读器页面。

Semantic code and document search CLI — BM25 recall + TypeSafe Jev calibrated precision

Ask a PDF in your own words and watch the matching lines light up. React + pdf.js + TypeSafe Jev.

Local-first document evaluation workspaces powered by Jev

.NET client for TypeSafe System One (Jev) — typed questions in, typed answers with probabilities and confidence out. No prompt engineering, no output parsing.

Natural-language MCP tool dispatcher powered entirely by TypeSafe's Jev — no general-purpose LLM. Discovers a simple MCP server's tool signatures at runtime and uses Jev's typed primitives (Choice/Noul) to pick the right tool and extract its arguments straight out of the sentence.

A browser form for building requests to TypeSafe's Jev: pick a template, fill in the blanks, copy the request. No JSON, no install, runs locally.

An open-source, AI-powered resume builder designed to craft high-impact resumes. Features smart content enhancement using the STAR method, instant PDF previews, and seamless export. Built with Django, Htmx, OpenAI, and WeasyPrint.

MCP-first toolkit for TypeSafe/Jev — the System One decision model. One stdio server (jev mcp) serves any MCP-capable harness, backed by one local event log and Prometheus impact metrics you can scrape into your own Grafana.

Parsing the PDF and extracting the relevant information

Text/PDF - File categorization and sorting with Typesafe AI Jev or local calibrated decision model