Estrazione dati e documenti

typesafe-computer-use traduce un obiettivo scritto in azioni sul Mac, leggendo lo schermo tramite OCR e controlli di accessibilità. TypeSafe Jev sceglie azione, bersaglio e sito; il codice calcola i fatti e un modello separato scrive il testo libero. Servono una chiave TypeSafe e i permessi di registrazione schermo e accessibilità. La CLI permette una simulazione prima del controllo effettivo.

JevChat-Windows legge conversazioni WeChat e KakaoTalk con OCR locale e propone risposte accanto alla finestra. Jev valuta intenzione e tensione e ordina tre alternative; un modello linguistico separato le scrive. Configura chiavi distinte per decisione e scrittura su Windows 10 1903+ o Windows 11.

La libreria identifica moduli IRS e tipi di pagina dal testo estratto, usando un registro JSON di descrizioni. Usa classifyPage con righe di testo, jevBackend e criteria.json. Le funzioni PDF richiedono Poppler e il backend una chiave TypeSafe.

DocJev classifica PDF, DOCX e PPTX o divide fascicoli in intervalli di pagine categorizzati, con esportazione PDF facoltativa. Installa con uv, esporta TYPESAFE_API_KEY ed esegui docjev classify o split con regole di categoria. I formati Office richiedono LibreOffice.

Estrae documenti, li indicizza in Qdrant, pianifica una scaletta e ricerca ogni sezione nella raccolta locale. Jev valuta e riordina frammenti e lacune; un altro LLM pianifica, interroga e scrive, mentre Python gestisce i tentativi.

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