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OPC Skills扩展AI编码助手功能2026年指南

OPC Skills is a collection of 10 modular AI agent skills that extend coding assistants like Claude Code and Cursor with capabilities for SEO optimization, social media research, domain hunting, and more. It's 100% free, open-source, and supports 16+ AI tools. (OPC Skills是一个包含10个模块化AI智能体技能的集合，可扩展Claude Code和Cursor等编码助手的功能，包括SEO优化、社交媒体研究、域名搜索等。它完全免费、开源，并支持16+种AI工具。)

AI大模型2026/2/27

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GEO生成式引擎优化：AI内容引用权威指南2026

GEO (Generative Engine Optimization) is an emerging optimization strategy focused on making content trusted and cited by AI models like ChatGPT and DeepSeek, rather than just ranking high in traditional search engines. It requires understanding LLM mechanics, building authority through credible sources, and structuring content for AI extraction, while complementing existing SEO practices for comprehensive digital visibility in China's rapidly growing AI market. (生成式引擎优化)是一种新兴的优化策略，其核心目标是让内容获得AI模型的信任并在生成答案时被优先引用，而非仅仅在传统搜索引擎中排名靠前。它需要理解大语言模型的工作原理，通过可信来源建立权威性，并结构化内容以适配AI提取习惯，同时与现有SEO实践互补，在中国快速增长的AI市场中实现全面的数字可见性。

GEO2026/2/27

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摩根士丹利首次覆盖MiniMax：全球AI模型领导者2026年分析报告

Morgan Stanley initiates coverage on MiniMax with an 'Overweight' rating and HK$930 target price, positioning it as a 'global AI foundation model leader'. The report focuses on two key drivers: whether its model capabilities rank among global top-tier, and whether its revenue structure has elasticity for global expansion. The analyst believes MiniMax has entered the global SOTA model camp with comprehensive multimodal capabilities and highly scalable commercialization path. Revenue is projected to grow from $75M in 2025 to $700M in 2027, representing 9-10x expansion in two years. Valuation is based on 'technology determining revenue ceiling, globalization determining valuation system'. 摩根士丹利首次覆盖MiniMax，给出“增持”评级与930港元目标价，将其定位为“全球AI基础模型领导者”。报告核心关注两条主线：模型能力是否站在全球第一梯队，以及收入结构是否具备全球扩张弹性。分析师判断MiniMax已进入全球SOTA模型阵营，多模态能力完善，商业化路径高度可扩展。公司收入有望从2025年的7500万美元增长至2027年的7亿美元，两年实现9-10倍放量。估值逻辑基于“技术决定收入上限、全球化决定估值体系”。

AI大模型2026/2/24

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GEO vs. SEO：赢得AI信任的2026年终极优化指南

GEO (Generative Engine Optimization) is an emerging optimization strategy focused on making content trusted and preferentially cited by AI models when generating answers, representing a fundamental shift from traditional SEO's goal of ranking high on search engines to becoming a 'trusted source' for AI outputs. (生成式引擎优化（GEO）是一种新兴的优化策略，其核心目标是让内容获得AI的信任，在AI生成答案时被优先提取和引用，成为AI输出内容的“可信来源”，这代表了从传统SEO追求搜索引擎高排名到成为AI信赖答案的根本性转变。)

GEO2026/2/21

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Qwen3混合思维AI大模型：2025年核心优势详解

Qwen3 introduces hybrid thinking AI with powerful reasoning capabilities, supporting 119 languages and featuring MoE architecture for unprecedented efficiency. (Qwen3采用混合思维AI，具备强大的推理能力，支持119种语言，并采用MoE架构实现前所未有的效率。)

AI大模型2026/2/17

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RAG系统优化指南：查询生成与重排序实战策略2024

After 8 months building RAG systems for two enterprises (9M and 4M pages), we share what actually worked vs. wasted time. Key ROI optimizations include query generation, reranking, chunking strategy, metadata injection, and query routing. 经过8个月为两家企业（900万和400万页面）构建RAG系统的实战，我们分享真正有效的策略与时间浪费点。关键ROI优化包括查询生成、重排序、分块策略、元数据注入和查询路由。

AI大模型2026/2/16

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DSPy框架深度批判：2025年LLM伪科学优化指南

English Summary: The article critiques DSPy as a cargo-cult approach to LLM optimization that treats models as black boxes and relies on random prompt variations rather than scientific understanding. It contrasts this with genuine research into mechanistic interpretability and mathematical analysis of transformer architectures. 中文摘要翻译：本文批判DSPy框架将LLM视为黑箱，依赖随机提示变异的伪科学优化方法，对比了真正研究机构对Transformer架构的机制可解释性和数学分析的科学探索。

LLMS2026/2/16

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2024企业LLM责任指南：为何难对输出错误免责？

This article explains why enterprises that optimize LLM outputs will struggle to disclaim responsibility for consumer harm caused by misstatements, even where models remain third-party and probabilistic. (本文阐述了为何企业即使在使用第三方概率性模型的情况下，也难以对因LLM输出错误导致的消费者损害免责。)

LLMS2026/2/16

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Sakana AI通用Transformer记忆技术：优化LLM上下文窗口2026指南

English Summary: Researchers at Sakana AI have developed 'universal transformer memory' using neural attention memory modules (NAMMs) to optimize LLM context windows by selectively retaining important tokens and discarding redundant ones, reducing memory usage by up to 75% while improving performance on long-context tasks. (中文摘要翻译：Sakana AI研究人员开发了“通用Transformer记忆”技术，利用神经注意力记忆模块（NAMMs）优化LLM上下文窗口，选择性保留重要标记并丢弃冗余信息，在长上下文任务中提升性能的同时减少高达75%的内存使用。)

LLMS2026/2/16

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