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Source channel @yxvmcom · Post #21 · Nov 10

#Features 我们打开了一项新的功能,此功能目前处于测试阶段,我们将此功能命名为 AnyLAN,你可以使用它快速的建立内网,并且不消耗你的公网流量。 目前此功能分为2个场景: 1. 同节点内网 2. 不同节点内网(2个节点或以上) 我们这里提供一份简易的教程供大家参考:https://yxvm.com/index.php?rp=/knowledgebase/2/How-to-use-AnyLAN.html 需要开启此功能,你必须购买相应产品(目前免费) LAN (必须同节点持有2个以上VPS才可购买): https://yxvm.com/cart.php?pid=44&promocode=DLCH0P1DN7 AnyLAN(必须俩个或以上节点持有VPS才可购买):https://yxvm.com/cart.php?pid=45&promocode=83YHPHA6QG *LAN 限速500Mbps AnyLAN限速100Mbps

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GitHub Trends

@githubtrending · Post #15163 · 09/24/2025, 07:30 PM

#python#document_analysis#layout_analysis#ocr#parser#pdf#pdf_converter#pdf_parser#python#vlm_ocr Dolphin is a smart AI tool that can analyze and understand complex document images, like pages with text, tables, formulas, and pictures. It works in two steps: first, it figures out the layout and reading order of the page; then, it quickly parses each element using special prompts. This makes it fast and accurate for turning document images into structured data like JSON or Markdown. You can use pre-trained models and easy code to process single pages, PDFs, or specific elements. This helps you save time and effort when extracting information from complicated documents efficiently. https://github.com/bytedance/Dolphin

GitHub Trends

@githubtrending · Post #15573 · 03/19/2026, 11:30 AM

#java#a11y#accessibility#ai#bounding_box#document_parsing#eaa#html#json#markdown#ocr#ocr_recognition#pdf#pdf_accessibility#pdf_converter#pdf_extraction#pdf_parser#pdf_ua#rag#tables#tagged_pdf OpenDataLoader PDF is a free, open-source tool (Apache 2.0) that tops benchmarks with 0.90 accuracy for extracting structured data like Markdown, JSON (with bounding boxes), and HTML from any PDF—digital, scanned, or complex with tables, formulas, charts, and OCR in 80+ languages. It runs locally on CPU (0.05s/page fast mode), filters AI prompt injections for safety, integrates with LangChain/RAG, and automates accessibility tagging to Tagged PDF. You save time and costs on parsing for AI pipelines or compliance (vs. $50–200/manual doc), getting precise, private results for better LLM apps and legal standards. https://github.com/opendataloader-project/opendataloader-pdf