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iOS设备上运行LLaMA2-13B：基于苹果MLX框架的完整技术指南

iOS设备上运行LLaMA2-13B：基于苹果MLX框架的完整技术指南

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。)
LLMS2026/2/3
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SGLang vs. vLLM：两大主流大模型推理引擎深度对比与选型指南

SGLang vs. vLLM：两大主流大模型推理引擎深度对比与选型指南

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。
LLMS2026/2/3
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LLMs.txt 2024指南：AI网站访问控制新标准与实用工具
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LLMs.txt 2024指南：AI网站访问控制新标准与实用工具

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。)
LLMS2026/2/2
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PageIndex：基于推理的RAG新范式，让大语言模型智能检索专业文档

PageIndex：基于推理的RAG新范式，让大语言模型智能检索专业文档

PageIndex is a document indexing system that transforms lengthy PDFs into semantic tree structures optimized for LLMs, enabling reasoning-based retrieval that outperforms traditional vector similarity approaches. It's particularly effective for financial reports, regulatory documents, and technical manuals where domain expertise and multi-step reasoning are required. PageIndex是一个文档索引系统，可将冗长PDF转换为语义树结构，专为大语言模型优化，实现基于推理的检索，超越传统向量相似度方法。特别适用于需要领域专业知识和多步推理的财务报告、监管文件和技术手册。
LLMS2026/1/31
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ChatGPT流量下滑背后：AI大模型竞争加剧与用户期望演变

ChatGPT流量下滑背后：AI大模型竞争加剧与用户期望演变

English Summary: This analysis examines the potential reasons behind ChatGPT's traffic decline, including market saturation, increased competition from alternatives like Claude and Gemini, technical limitations in reasoning and accuracy, evolving user expectations, and the impact of monetization strategies. It also considers OpenAI's ongoing innovations and the broader AI landscape shifts. (中文摘要翻译: 本文深入分析了ChatGPT流量下降的潜在原因，涵盖市场饱和、来自Claude和Gemini等替代品的竞争加剧、模型在推理和准确性方面的技术局限、用户期望的演变、以及商业化策略的影响。同时考虑了OpenAI的持续创新和更广泛的AI格局变化。)
AI大模型2026/1/30
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FinRobot：金融AI代理平台如何革新量化交易与投资研究

FinRobot：金融AI代理平台如何革新量化交易与投资研究

FinRobot is an open-source AI agent platform built on large language models (LLMs), specifically designed for financial data analysis, quantitative trading, and investment research. It features a four-layer architecture optimized for financial AI tasks, integrates Financial Chain-of-Thought (CoT) reasoning, and provides modular AI agents for market prediction, document analysis, and trading strategy optimization. (FinRobot 是一款基于大语言模型的开源AI代理平台，专注于金融数据分析、量化交易和投资研究。它采用四层架构优化金融AI任务，集成金融链式思维推理，并提供模块化的市场预测、文档分析和交易策略优化代理。)
AI大模型2026/1/30
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PageIndex革命：基于推理的RAG框架如何超越向量搜索，实现98.7%准确率

PageIndex革命：基于推理的RAG框架如何超越向量搜索，实现98.7%准确率

PageIndex introduces a revolutionary reasoning-based RAG framework that eliminates dependency on vector similarity search and document chunking. It organizes documents into hierarchical tree structures, enabling LLMs to navigate like human experts through multi-step reasoning, achieving 98.7% accuracy on FinanceBench. (PageIndex推出革命性的基于推理的RAG框架，彻底摆脱向量相似度搜索和文档分块的依赖。它将文档组织成层次化树状结构，使大语言模型能够像人类专家一样通过多步推理进行导航，在FinanceBench基准测试中达到98.7%的准确率。)
AI大模型2026/1/28
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