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FinRobot：开源金融AI智能体平台终极指南，开发效率提升90%

FinRobot is an open-source AI agent platform designed for financial applications, leveraging large language models (LLMs) to automate financial analysis, reduce development time by 90%, and support multi-agent collaboration through a four-layer architecture. It features quick deployment, intelligent data processing, and production-ready monitoring systems. (FinRobot是一个开源AI智能体平台，专为金融应用设计，利用大语言模型实现金融分析自动化，减少90%开发时间，并通过四层架构支持多智能体协作。具备快速部署、智能数据处理和生产级监控系统。)

AI大模型2026/1/25

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FinRobot：开源金融AI代理平台，基于大模型的智能分析与决策

FinRobot is an open-source AI agent platform specifically designed for financial applications, leveraging large language models (LLMs) to build specialized AI agents capable of complex financial analysis and decision-making. The platform employs Financial Chain-of-Thought (CoT) prompting to decompose intricate problems into logical steps, enhancing analytical capabilities. Its modular architecture includes layers for Financial AI Agents, Financial LLM Algorithms, LLMOps/DataOps, and Multi-source LLM Foundation Models, supporting diverse financial AI agents for market forecasting, document analysis, and trading strategies. FinRobot aims to democratize access to professional financial LLM tools, promoting widespread adoption of AI in financial decision-making. (FinRobot是一个专注于金融领域的开源AI代理平台，基于大型语言模型构建能够进行复杂分析和决策的金融专业AI代理。平台通过金融思维链提示技术将难题分解为逻辑步骤，增强分析能力。其模块化架构包括金融AI代理层、金融LLM算法层、LLMOps/DataOps层和多源LLM基础模型层，支持市场预测、文档分析和交易策略等多种金融专业AI代理。FinRobot通过开源项目让更多人能访问和使用金融专业LLM工具，促进AI在金融决策中的广泛应用。)

AI大模型2026/1/25

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AirLLM：4GB GPU上运行700亿参数大模型的开源框架

AirLLM is an open-source framework that enables running 70B-parameter large language models on a single 4GB GPU through layer-wise offloading and memory optimization techniques, democratizing access to cutting-edge AI without traditional compression methods. (AirLLM是一个开源框架，通过分层卸载和内存优化技术，使700亿参数的大语言模型能够在单个4GB GPU上运行，无需传统压缩方法即可实现前沿AI的普及化访问。)

AI大模型2026/1/25

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英国法学硕士（LL.M.）全攻略：顶尖院校113个课程深度解析

This guide provides comprehensive information about LLM (Master of Laws) programs in the United Kingdom, featuring 113 results from top institutions including Oxford, Cambridge, King's College London, and Edinburgh. It details program specializations, delivery formats (full-time, part-time, distance learning), and key features of each law school. (本指南全面介绍英国法学硕士项目，涵盖牛津、剑桥、伦敦国王学院、爱丁堡大学等顶尖院校的113个课程信息，详细说明专业方向、授课形式（全日制、非全日制、远程教育）及各法学院特色。)

LLMS2026/1/25

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知识图谱突破LLM局限：Graph RAG 2024指南

Graph RAG (Retrieval Augmented Generation) enhances LLM performance by integrating knowledge graphs with retrieval mechanisms, addressing limitations like domain-specific knowledge gaps and real-time information access. It combines entity extraction, subgraph retrieval, and LLM synthesis to provide accurate, context-aware responses. Graph RAG（检索增强生成）通过将知识图谱与检索机制结合，提升大语言模型性能，解决领域知识不足和实时信息获取等局限。它结合实体提取、子图检索和LLM合成，提供准确、上下文感知的响应。

LLMS2026/1/24

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Clippy本地运行大模型指南：2024怀旧桌面AI应用

Clippy is a desktop application that allows users to run various large language models locally on their computers with a nostalgic 1990s Microsoft Office-style interface, offering offline functionality, easy setup, and customizable model support. (Clippy是一款桌面应用程序，让用户能够在本地计算机上运行各种大语言模型，采用怀旧的1990年代Microsoft Office风格界面，提供离线功能、简易设置和可定制模型支持。)

LLMS2026/1/24

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LLMs.txt标准指南：2024年AI智能体结构化文档访问新方案

LLMs.txt and llms-full.txt are specialized document formats designed to provide Large Language Models (LLMs) and AI agents with structured access to programming documentation and APIs, particularly useful in Integrated Development Environments (IDEs). The llms.txt format serves as an index file containing links with brief descriptions, while llms-full.txt contains all detailed content in a single file. Key considerations include file size limitations for LLM context windows and integration methods through MCP servers like mcpdoc. (llms.txt和llms-full.txt是专为大型语言模型和AI智能体设计的文档格式，提供对编程文档和API的结构化访问，在集成开发环境中尤其有用。llms.txt作为索引文件包含带简要描述的链接，而llms-full.txt将所有详细内容整合在单个文件中。关键考虑因素包括LLM上下文窗口的文件大小限制以及通过MCP服务器的集成方法。)

LLMS2026/1/24

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高效LLM智能体构建指南：2024实用模式与最佳实践

English Summary: This comprehensive guide from Anthropic shares practical insights on building effective LLM agents, emphasizing simplicity over complexity. It distinguishes between workflows (predefined code paths) and agents (dynamic, self-directed systems), provides concrete patterns like prompt chaining, routing, and parallelization, and offers guidance on when to use frameworks versus direct API calls. The article stresses starting with simple solutions and adding complexity only when necessary, with real-world examples from customer implementations. 中文摘要翻译：本文是Anthropic分享的关于构建高效LLM智能体的实用指南，强调简单性优于复杂性。文章区分了工作流（预定义代码路径）和智能体（动态、自导向系统），提供了提示链、路由、并行化等具体模式，并就何时使用框架与直接API调用提供了指导。文章强调从简单解决方案开始，仅在必要时增加复杂性，并提供了客户实施的真实案例。

LLMS2026/1/24

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LLMs.txt生成器API弃用指南：从网站内容生成LLM训练文件的工具迁移路径

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。)

LLMS2026/1/24

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