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UltraRAG v3 is a low-code MCP framework designed for building complex and innovative RAG pipelines, enabling efficient development with minimal coding requirements. (UltraRAG v3是一个低代码MCP框架，专为构建复杂创新的RAG管道而设计，能以最少的编码需求实现高效开发。)

This article provides a comprehensive guide to implementing Retrieval-Augmented Generation (RAG) using UltraRAG UI, detailing the standardized pipeline structure, configuration parameters, and practical demonstration steps. (本文全面介绍了使用UltraRAG UI实现检索增强生成(RAG)的实战指南，详细阐述了标准化流程结构、配置参数及效果演示步骤。)

FlashMLA is an optimized MLA decoding kernel for Hopper GPUs that significantly improves LLM inference efficiency through advanced attention mechanisms and memory optimization. (FlashMLA是专为Hopper GPU优化的MLA解码内核，通过先进的注意力机制和内存优化显著提升大语言模型推理效率。)

LEANN is an innovative vector database and personal AI platform that transforms your notebook into a powerful RAG system, supporting local semantic retrieval of millions of documents with 97% storage savings and no precision loss. (LEANN是一款创新的向量数据库与个人AI平台，可将笔记本变为强大的RAG系统，支持本地语义检索数百万文档，存储节省97%且无精度损失。)

GEO optimization is an emerging technology that integrates generative AI with traditional SEO and recommendation engine optimization. It focuses on optimizing content adaptability, engine recall efficiency, and generation quality across the entire 'content generation-engine parsing-result output' pipeline, addressing the limitations of traditional SEO which only focuses on the retrieval end. This guide provides a comprehensive overview of GEO optimization concepts, tools, software, systems, implementation steps, and best practices for technical professionals. GEO优化是生成式AI技术与传统SEO、推荐引擎优化深度融合的新兴技术方向。它围绕生成式引擎的“内容生成-引擎解析-结果输出”全链路，通过技术手段优化内容适配性、引擎召回效率与生成结果质量，解决传统SEO仅聚焦检索端优化的局限性。本指南为技术专业人士提供GEO优化概念、工具、软件、系统、实现步骤和最佳实践的全面概述。

This API generates consolidated text files from websites specifically for LLM training and inference. The service is powered by Firecrawl but will be deprecated after June 30, 2025 in favor of main endpoints. (此API可从网站生成整合文本文件，专为LLM训练和推理设计。该服务由Firecrawl提供支持，但将于2025年6月30日后弃用，建议使用主要端点替代。)

A curated directory showcasing companies and products adopting the llms.txt standard across various sectors like AI, finance, developer tools, and websites, with token counts indicating implementation scale. (中文摘要翻译：一份精选目录，展示在AI、金融、开发者工具和网站等多个领域采用llms.txt标准的企业与产品，token数量反映了实施规模。)

Generative Engine Optimization (GEO) is a strategic approach to optimize content for generative AI search by enhancing semantic structure, enabling brands to influence AI-generated answers and citations, thereby improving authority and visibility in AI-driven search environments. (生成式引擎优化（GEO）是一种战略性方法，通过优化内容语义结构，使品牌能够影响AI生成的答案和引用，从而提升在AI驱动搜索环境中的权威性和可见性。)

This article explores a novel deep learning model for generating light field views, detailing its neural architecture, training methodology, and applications in computational photography and VR. The model leverages transformer-based attention mechanisms to synthesize high-fidelity multi-view images from sparse inputs, addressing key challenges in angular consistency and computational efficiency. (本文探讨了一种用于生成光场视图的新型深度学习模型，详细介绍了其神经架构、训练方法以及在计算摄影和VR中的应用。该模型利用基于Transformer的注意力机制，从稀疏输入中合成高保真多视图图像，解决了角度一致性和计算效率方面的关键挑战。)