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GEO (Generative Engine Optimization) is the new paradigm for content visibility in the AI era, shifting focus from keyword ranking for search engines to semantic understanding and authority building f

RAG-Anything is an all-in-one RAG framework developed by HKUDS at the University of Hong Kong, open-sourced on GitHub. It aims to enhance LLM performance by integrating retrieval and generation, addre

This comprehensive guide explains Retrieval-Augmented Generation (RAG) as a solution to large language models' knowledge cutoff and hallucination issues, detailing its five-generation evolution, pract

This article provides a comprehensive technical guide to edge detection in computer vision, covering both traditional methods (Sobel, Canny, LoG) and deep learning approaches (HED, RCF, GAN-based, Transformer-based). It includes implementation details, code examples in PyTorch, and training considerations. 原文翻译：本文全面介绍了计算机视觉中的边缘检测技术路线，涵盖传统方法（Sobel、Canny、LoG）和深度学习方法（HED、RCF、基于GAN、基于Transformer），包括实现细节、PyTorch代码示例和训练注意事项。

free-claude-code is an open-source project that enables free use of Claude Code by proxying requests to free or low-cost model services like NVIDIA NIM, while retaining Claude Code's full engineering

DeepSeek V4 预览版于2026年4月发布，提供Pro（1.6T参数）和Flash（284B参数）两个版本，均支持1M tokens上下文和领先的Agent能力。其核心Agent能力包括Function Calling（支持并行/嵌套工具调用）、长上下文记忆、思考模式（推理增强）、结构化输出、多模态理解及主流Agent框架适配。API兼容OpenAI接口，支持流式/非流式调用。典型应用覆盖

RAG-Anything is an all-in-one multimodal RAG system that processes documents containing text, images, tables, and formulas. It features end-to-end processing pipelines, knowledge graph indexing, and c

This article explains how DeepSeek AI automates SEO tasks including content generation, keyword strategy, and batch production. It provides step-by-step instructions and examples for technical profess

This article explains how to optimize content for Generative Engine Optimization (GEO) in the DeepSeek ecosystem, focusing on making content model-friendly through structured formats, consistent knowl

This article explains the critical importance of structured outputs in LLM workflows, detailing how to implement them from scratch using JSON and Pydantic, and through the Gemini SDK, to build reliabl