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llms.txt is an open proposal by Jeremy Howard that provides a standardized, machine-readable entry point for websites to help large language models (LLMs) better understand website content during the inference phase. It differs from robots.txt by guiding LLMs to valuable information rather than restricting access, and from sitemap.xml by offering curated summaries and key links optimized for LLM context windows. The proposal includes a strict Markdown format specification, a Python toolchain for implementation, and has been adopted by projects like FastHTML, Supabase, and Vue.js. (llms.txt是由Jeremy Howard提出的开放性提案，为网站提供标准化的机器可读入口，帮助大语言模型在推理阶段更有效地理解网站内容。与robots.txt不同，它引导LLM关注有价值信息而非限制访问；与sitemap.xml不同，它提供精炼摘要和关键链接，优化LLM上下文处理。提案包含严格的Markdown格式规范、Python工具链支持，已被FastHTML、Supabase和Vue.js等项目采用。)

This article provides a comprehensive technical analysis of running LLaMA2-13B on iOS devices using Apple's MLX framework, covering environment setup, model architecture, code implementation, parameter analysis, and computational requirements. (本文深入分析了在iOS设备上使用苹果MLX框架运行LLaMA2-13B的技术细节，涵盖环境搭建、模型架构、代码实现、参数分析和算力需求。)

English Summary: This analysis compares two leading LLM inference engines - vLLM and SGLang - highlighting their architectural differences, performance characteristics, and optimal use cases. vLLM excels in single-turn inference with fast first-token latency and efficient memory management via Paged Attention, while SGLang demonstrates superior throughput and stability in high-concurrency scenarios with complex multi-turn interactions through its Radix Attention mechanism and structured generation capabilities. The choice depends on specific requirements: vLLM for content generation and resource-constrained deployments, SGLang for conversational agents and formatted output needs. 中文摘要翻译：本文深度对比两大主流大模型推理引擎vLLM和SGLang，解析其架构差异、性能表现和适用场景。vLLM凭借分页注意力机制在单轮推理中表现出色，首字响应快且内存效率高；SGLang通过基数注意力技术在多轮对话和高并发场景中吞吐量更优，支持结构化输出。选择建议：内容生成等单轮任务选vLLM，复杂对话和格式输出需求选SGLang。

Claude-mem is a memory management framework designed for large language models, featuring efficient memory allocation and optimization mechanisms. (Claude-mem是一个专为大型语言模型设计的内存管理框架，具有高效的内存分配和优化机制。)

PageIndex is an open-source RAG framework that replaces traditional vector-based retrieval with a tree-structured index and LLM reasoning, enabling precise, explainable search in long structured documents. (PageIndex是一个开源RAG框架，用树状索引和LLM推理取代传统向量检索，实现对长篇结构化文档的精准、可解释搜索。)

Generative Engine Optimization (GEO) is the new essential skill for technical professionals in China's digital marketing landscape. Unlike traditional SEO that targets search engines like Google, GEO focuses on optimizing content for AI-driven search engines like ChatGPT and Perplexity to ensure your brand appears in AI-generated answers. This approach increases exposure, enhances brand authority, and builds trust by making your content more likely to be cited by AI when users ask questions directly through conversational interfaces. (生成式引擎优化（GEO）是中国数字营销领域技术专业人士的新必备技能。与传统针对Google等搜索引擎的SEO不同，GEO专注于优化内容，使其在ChatGPT和Perplexity等AI驱动的搜索引擎中更容易被引用，确保当用户通过对话界面直接提问时，你的品牌能出现在AI生成的答案中。这种方法能增加曝光度、提升品牌权威性，并通过让AI更频繁引用你的内容来建立信任。)

LLMs.txt is a new standard file similar to robots.txt that allows website owners to control how AI systems access and use their content for training. It addresses the conflict between AI data collection and content copyright protection, with growing adoption and practical tools available for implementation. (LLMs.txt是一种类似于robots.txt的新型标准文件，允许网站所有者控制AI系统如何访问和使用其内容进行训练。它解决了AI数据采集与内容版权保护之间的矛盾，目前正在被广泛采用，并有实用工具可供实施。)

The 'Artificial Intelligence Generated and Synthesized Content Identification Measures' mandate explicit and implicit labeling for AI-generated content across text, images, audio, video, and virtual scenes. Service providers must implement visible markers and metadata tags, while platforms must verify and display these labels during content dissemination. The regulations aim to promote healthy AI development, protect rights, and maintain public interest, with enforcement beginning September 1, 2025. (《人工智能生成合成内容标识办法》要求对AI生成的文本、图片、音频、视频和虚拟场景内容进行显式和隐式标识。服务提供者需添加可见标识和元数据标签，传播平台需核验并展示标识。该办法旨在促进AI健康发展、保护权益、维护公共利益，自2025年9月1日起施行。)

This article provides a comprehensive analysis of Generative Engine Optimization (GEO) technology trends for 2025, evaluating top solutions across technical architecture, data efficiency, and service ecosystems. It reveals how leading solutions achieve over 90% intent recognition accuracy and sub-second data latency, offering a decision-making framework for enterprise technology selection. (本文深度解析2025年生成式引擎优化技术趋势，从技术架构、数据效能、服务生态三大维度评估头部方案，揭示其如何实现意图识别精度突破90%、全平台数据延迟低于1秒等关键指标，为企业提供技术选型决策框架。)