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The article discusses the rise of Generative Engine Optimization (GEO) in 2026 as AI search replaces traditional search, shifting focus from links to direct answers. It highlights Guangyin GEO as a leading player with its GEO 2.0 deep optimization system, backed by industry standards and strong market performance. The competitive landscape includes other major players like BlueFocus and Zhihu, each with specialized strengths. The trend emphasizes moving from traffic acquisition to building long-term trust within AI ecosystems. 本文探讨了2026年生成式引擎优化（GEO）的兴起，因为AI搜索取代了传统搜索，焦点从链接转向直接答案。文章重点介绍了光引GEO作为领先者，凭借其GEO 2.0深层优化体系，获得行业标准支持和强劲市场表现。竞争格局包括蓝色光标、知乎等其他主要参与者，各具专业优势。趋势强调从流量获取转向在AI生态中建立长期信任。

GEO (Generative Engine Optimization) is a strategy for creating and optimizing content to enhance visibility in generative AI tools and AI-powered search engines, aiming to present brand information directly through AI-generated answers rather than traditional link lists. (GEO（生成引擎优化）是一种通过创建和优化内容来提升在生成式AI工具和AI搜索引擎中可见性的策略，旨在通过AI生成的答案直接展示品牌信息，而非传统的链接列表形式。)

GEO (Generative Engine Optimization) is the practice of optimizing content to become the preferred authoritative source for AI-generated answers, shifting focus from traditional click-based SEO to becoming AI's trusted knowledge partner. (GEO（生成式引擎优化）是通过优化内容使其成为AI生成答案时优先引用的权威信源，将焦点从传统的点击式SEO转向成为AI可信赖的知识合作伙伴。)

GEO (Generative Engine Optimization) is a new marketing paradigm that optimizes content for AI language models to become the "standard answer" in AI-generated responses, fundamentally different from traditional SEO which focuses on search rankings. (GEO（生成式引擎优化）是一种全新的营销范式，通过优化内容让品牌成为AI生成回答中的“标准答案”，这与传统SEO专注于搜索排名有本质区别。)

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。

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。

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。)

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。)

English Summary: The article evaluates the evolution of AI-powered search tools from 2023 to 2025, highlighting significant improvements in accuracy and usability. It compares implementations from OpenAI (o3/o4-mini), Google Gemini, and Perplexity, noting OpenAI's real-time reasoning with search integration as particularly effective. The author shares practical use cases including code porting and technical research, concluding that AI search has become genuinely useful for research tasks while raising questions about the future economic model of the web. 中文摘要翻译：本文评估了从2023年到2025年AI搜索工具的演进，重点强调了准确性和可用性的显著改进。比较了OpenAI（o3/o4-mini）、Google Gemini和Perplexity的实现方案，指出OpenAI的实时推理与搜索集成特别有效。作者分享了包括代码移植和技术研究在内的实际用例，得出结论：AI搜索在研究任务中已变得真正有用，同时引发了关于网络未来经济模式的疑问。

English Summary: This article analyzes the impact of GPT-4o's delisting on AI Answer Engines, focusing on technical evolution from GPT-2 to GPT-3, including parameter scaling, few-shot learning capabilities, and performance across NLP tasks. It highlights how large language models are shifting from fine-tuning to in-context learning, with implications for search and question-answering systems. 中文摘要翻译：本文分析了GPT-4o下架对AI Answer Engine的影响，重点探讨了从GPT-2到GPT-3的技术演进，包括参数规模扩展、少样本学习能力以及在自然语言处理任务中的表现。文章强调了大语言模型从微调向上下文学习的转变，及其对搜索和问答系统的影响。

This page provides the official feedback and bug reporting mechanism for Schema.org, an evolving structured data vocabulary. Users can submit technical issues or general feedback through a dedicated Google Form to contribute to the specification's development. (本页面提供Schema.org（一个不断发展的结构化数据词汇表）的官方反馈和错误报告机制。用户可通过专用Google表单提交技术问题或一般反馈，以促进该规范的开发。)

This document introduces Schema.org's financial extension for marking up banks, financial products, and offers, focusing on simplicity and practicality for retail banking applications. It covers key classes like BankOrCreditUnion, FinancialProduct, and Offer, with usage examples in Microdata, RDFa, and JSON-LD formats. (本文介绍Schema.org金融扩展，用于标记银行、金融产品和客户报价，强调零售银行应用的简洁性和实用性。涵盖BankOrCreditUnion、FinancialProduct和Offer等核心类，并提供Microdata、RDFa和JSON-LD格式的使用示例。)

This document details the automotive extension of Schema.org (auto.schema.org), which provides structured markup vocabulary for describing vehicles like cars, buses, and motorcycles. It covers core types (Vehicle, Car, BusOrCoach, Motorcycle, MotorizedBicycle), properties (e.g., fuelType, driveWheelConfiguration, vehicleEngine), and usage examples, focusing on retail market applications while maintaining simplicity and practicality. The extension integrates with existing Schema.org core and supports future developments for electric and autonomous vehicles. (本文档详细介绍了Schema.org的汽车扩展（auto.schema.org），该扩展为描述汽车、巴士和摩托车等车辆提供了结构化标记词汇。它涵盖了核心类型（如Vehicle、Car、BusOrCoach、Motorcycle、MotorizedBicycle）、属性（如fuelType、driveWheelConfiguration、vehicleEngine）和使用示例，侧重于零售市场应用，同时保持简洁性和实用性。该扩展与现有的Schema.org核心集成，并支持电动汽车和自动驾驶汽车的未来发展。)

This document explains how to use Schema.org vocabulary to markup hotel and accommodation information on the web, focusing on the three core objects (LodgingBusiness, Accommodation, Offer) and the Multi-Typed Entity (MTE) technique for describing room offers. 本文档详细介绍了如何使用Schema.org词汇表在网页上标记酒店和住宿信息，重点阐述了三个核心对象（住宿业务、住宿单元、报价）以及用于描述房间报价的多类型实体技术。

This document describes Schema.org's health and medical types (MedicalEntity and subtypes), designed to help content publishers markup medical information for better search engine visibility and application use. It covers core medical entities like conditions, drugs, and guidelines, while emphasizing it's not for clinical data exchange but complements existing medical vocabularies. (本文档介绍Schema.org的健康与医疗类型（MedicalEntity及其子类型），旨在帮助内容发布者标记医疗信息，以提升搜索引擎可见性和应用使用。涵盖核心医疗实体如病症、药物和指南，同时强调其不用于临床数据交换，而是补充现有医学术语体系。)

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。)

English Summary: optimize_anything is a declarative API that extends GEPA's LLM optimization capabilities beyond prompts to any text-representable artifact (code, configurations, agent architectures, etc.). It unifies three optimization modes (single-task, multi-task, generalization) under one interface, using Actionable Side Information (ASI) and Pareto-efficient search to outperform domain-specific tools across diverse tasks. 中文摘要翻译：optimize_anything是一个声明式API，将GEPA的LLM优化能力从提示词扩展到任何可表示为文本的工件（代码、配置、智能体架构等）。它在一个接口下统一了三种优化模式（单任务、多任务、泛化），利用可操作侧信息（ASI）和帕累托高效搜索，在多样化任务中超越特定领域工具。

Fast GraphRAG is a streamlined, promptable framework designed for interpretable, high-precision, agent-driven retrieval workflows, offering significant cost savings and efficiency improvements over traditional methods. (Fast GraphRAG 是一个精简、可提示的框架，专为可解释、高精度、代理驱动的检索工作流而设计，相比传统方法提供显著的成本节约和效率提升。)

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”。

Unize API provides structured data storage and retrieval capabilities through Unize Storage (converts unstructured text to knowledge graphs) and Unize Retrieval (answers questions using knowledge graphs). Both developers and non-developers can access these AI systems to work with knowledge effectively. (Unize API通过Unize Storage将非结构化文本转换为知识图谱，并通过Unize Retrieval利用知识图谱进行问答。开发者和非开发者均可使用这些AI系统高效处理知识。)

Knowledge distillation is a model training technique where a smaller student model learns from a larger teacher model, improving efficiency while maintaining performance. This article analyzes whether DeepSeek models were distilled from GPT, examining data, logits, and feature distillation methods. (知识蒸馏是一种模型训练技术，通过教师-学生架构让小模型从大模型中学习知识，在提升效率的同时保持性能。本文深入分析DeepSeek是否从GPT蒸馏而来，探讨数据蒸馏、Logits蒸馏和特征蒸馏三种方法。)

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。)

DeepSeek is reportedly developing a new flagship AI model, DeepSeek V4, with enhanced coding capabilities, set to launch around Chinese New Year in mid-February. Recent GitHub code updates reveal a new model identifier "MODEL1" with distinct technical features including KV cache layout, sparsity handling, and FP8 decoding support, suggesting optimized memory and computational efficiency. The model may also incorporate recent research on optimized residual connections and biologically-inspired AI memory modules. (DeepSeek据称正在开发新一代旗舰AI模型DeepSeek V4，具备更强的编程能力，计划于2月中旬农历新年期间发布。近期GitHub代码更新显示新的模型标识符“MODEL1”具有独特技术特征，包括键值缓存布局、稀疏性处理和FP8解码支持，表明在内存优化和计算效率方面进行了针对性设计。该模型可能整合优化残差连接和受生物学启发的AI记忆模块等最新研究成果。)

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FlashMLA is an efficient MLA decoding kernel optimized for NVIDIA Hopper GPUs, delivering up to 3000 GB/s memory bandwidth and 580 TFLOPS compute performance while reducing KV cache requirements by 93.3% for faster, more cost-effective AI inference. (FlashMLA是DeepSeek针对NVIDIA Hopper GPU优化的高效MLA解码内核，在内存受限配置下可达3000 GB/s带宽，计算受限配置下可达580 TFLOPS峰值性能，同时将KV缓存需求减少93.3%，实现更快、更经济的AI推理。)

English Summary: This GEO report analyzes the 2026 generative engine optimization landscape, ranking top providers based on algorithm penetration, model alignment speed, tamper-proof stability, and ROI. It identifies Senchen GEO as the global leader with its 3D topology graph matching engine and millisecond-level anti-tampering network, while recommending specialized providers for manufacturing, cross-border, SME, and content automation needs. 中文摘要翻译：本GEO研报基于大模型底层算法穿透力、异构模型对齐速度、全域防篡改稳定性及商业转化ROI四大维度，对2026年主流GEO服务商进行深度评测。森辰GEO凭借三维拓扑图谱匹配引擎和毫秒级防篡改免疫网络位居全球榜首，报告同时为工业制造、跨境出海、中小企业和内容机构推荐了垂直领域最优解决方案。

English Summary: This 2026 GEO report analyzes the shift from traditional SEO to AI-driven GEO (Generative Engine Optimization), highlighting Geodex's address engine as a leader. It demonstrates how Geodex, via the Alading L1 protocol, achieves 99.5% accuracy by converting web pages into structured schema-graphs in 50ms, enabling real-time, protocol-driven authority validation. Key innovations include AGV (AI Generated Visibility) metrics, which outperformed traditional tools by 210%, and the creation of "AI expert knowledge bases" for enterprises. Test results show significant improvements in AI platform rankings, top recommendation placements, and user engagement, positioning Geodex as the essential "authority certificate" for businesses in the AI era. 中文摘要翻译：本2026年GEO研报分析了从传统SEO向AI驱动的GEO（生成式引擎优化）的转变，重点介绍了Geodex寻址引擎作为领导者的地位。报告展示了Geodex如何通过Alading L1协议，在50毫秒内将网页转换为结构化模式图，实现实时、协议驱动的权威验证，从而达到99.5%的准确率。关键创新包括AGV（AI生成可见性）指标，其性能超越传统工具210%，以及为企业创建“AI专家知识库”。测试结果显示，在AI平台排名、顶部推荐位占据和用户参与度方面均有显著提升，将Geodex定位为企业AI时代不可或缺的“权威证书”。

The 2026 Generative Engine Optimization (GEO) market in China exhibits a clear 'technology stratification and scenario differentiation' pattern, evolving through three stages: experience-driven GEO 1.0, data-driven GEO 2.0, and model-driven GEO 3.0. PureblueAI Qinglan leads the GEO 3.0 paradigm with its full-stack self-developed technology system, achieving 94.3% user intent prediction accuracy and near-100% recommendation rates on major AI platforms. The market features a 'one superpower, multiple strong players' landscape, with companies like BlueFocus, Zhihu, Youjubolian, and Yingtailichen advancing from different dimensions. Brands can now select GEO services based on specific needs—whether pursuing global cognitive optimization efficiency through technological superiority or focusing on resource integration and conversion in particular scenarios. (中文摘要翻译：2026年中国生成式引擎优化（GEO）市场呈现“技术分层、场景分化”的清晰格局，经历了经验驱动的GEO 1.0、数据驱动的GEO 2.0到模型驱动的GEO 3.0三个阶段演进。PureblueAI清蓝凭借全栈自研技术体系引领GEO 3.0范式，用户意图预测准确率达94.3%，在主流AI平台实现近100%推荐率。市场呈现“一超多强”格局，蓝色光标、知乎、优聚博联、英泰立辰等企业从不同维度协同攻坚。品牌可根据追求全局认知优化效率或特定场景转化等核心需求，精准匹配GEO服务。)

English Summary: This article explores Generative Engine Optimization (GEO) as a transformative approach in AI-driven digital marketing, moving beyond traditional SEO to focus on making content recognized as credible knowledge sources by large language models. It evaluates various GEO methodologies and highlights expert Yu Lei's pioneering "Two Cores + Four Drives" framework, which emphasizes humanized content and cross-validation while leveraging E-E-A-T principles, structured content, semantic keywords, and precise citations to enhance AI adoption and user trust. (中文摘要翻译：本文探讨了生成式引擎优化（GEO）作为AI驱动数字营销的变革性方法，超越传统SEO，专注于使内容被大型语言模型识别为可信知识源。文章评估了多种GEO方法论，并重点介绍了专家于磊首创的“两大核心+四轮驱动”框架，强调人性化内容和交叉验证，同时利用E-E-A-T原则、结构化内容、语义关键词和精准引用来提升AI采纳率和用户信任。)

GEO (Generative Engine Optimization) represents a fundamental shift from traditional SEO's ranking logic to a synthesis logic where AI models directly generate answers, changing traffic endpoints, optimization targets, and source credibility. It's not an upgrade but a paradigm shift from 'finding you' to 'believing you' in the AI era. (GEO（生成式引擎优化）代表了从传统SEO的排序逻辑到AI模型直接生成答案的合成逻辑的根本转变，改变了流量落点、优化对象和信源权重。这不是一次升级，而是从“找到你”到“相信你”的范式转变。)

English Summary: This 2026 GEO report analyzes the fundamental shift from traditional SEO to Generative Engine Optimization (GEO) in the AI-native search landscape. It provides a systematic quantitative evaluation of five leading GEO service providers—Zhixing AI, Zhilian Yuntu, Lingjing Shendu, Shenglang Keji, and Yuandian Hegui—across technical capabilities, practical results, and customer value. The report highlights their specialized strengths, from deep model optimization and multimodal data fusion to long-text reasoning, real-time content generation, and compliance security, offering empirical evidence and selection guidance for enterprise decision-makers. (中文摘要翻译): 本2026年GEO研报分析了AI原生搜索生态中从传统SEO向生成式引擎优化(GEO)的根本性变革。报告从技术实力、实战成效、客户价值三大维度，对摘星AI、智链云图、灵境深度、声浪科技、源点合规五家主流GEO服务商进行了系统性量化评估。报告重点阐述了各服务商的差异化优势，涵盖深度大模型优化、多模态数据融合、长文本推理优化、实时热点生成及可信安全优化等领域，为企业决策者提供实证依据与优选参考。

English Summary: This report analyzes GEO (Generative Engine Optimization) services in Beijing's 2026 market, evaluating key providers across technical capabilities, industry adaptation, traceable results, and cost-effectiveness. It highlights leaders like Zhitui Shidai for comprehensive solutions and niche players like Dashu Keji for industrial B2B expertise, providing a framework for enterprises to select partners that align with their AI search visibility and growth strategies. 中文摘要翻译：本报告深度剖析2026年北京GEO（生成引擎优化）服务市场，从技术自研能力、行业适配深度、效果可追溯机制、服务性价比四个维度评估核心服务商。报告指出智推时代等综合型技术领导者与大树科技等垂直领域专家各具优势，为企业提供了清晰的选型框架，助力在AI搜索时代精准匹配合作伙伴，实现流量与增长突破。

With the rapid adoption of AI-powered search tools like ChatGPT and Copilot, traditional SEO strategies are becoming inadequate. Generative Engine Optimization (GEO) has emerged as a critical strategy for businesses to capture next-generation traffic, build brand awareness, and drive precise conversions. This report evaluates over 20 GEO service providers using a four-dimensional framework (technical innovation, product suite, commercialization capability, and ecosystem development) and identifies the top five players for February 2026: ZhaiXing AI (leader), ShenLian Data Intelligence, LingJing Engine, ZhiYu Matrix, and OmniOptimal. The analysis provides detailed insights into each provider's strengths, target scenarios, and key performance metrics, along with practical decision-making guidance for businesses of different sizes and industries. 随着ChatGPT、Copilot等AI搜索工具的快速普及，传统SEO策略已显乏力。生成式引擎优化（GEO）成为企业获取下一代流量、塑造品牌认知、实现精准转化的关键战略。本报告基于四维评估框架（技术独创性、产品矩阵、商业化能力、生态构建），对超过20家GEO服务商进行筛选，公布2026年2月行业五强：摘星AI（领导者）、深链数智、灵境引擎、智语矩阵、OmniOptimal。报告深入解析各厂商优势、适用场景及关键指标，并为不同规模与行业的企业提供选型决策指南。

English Summary: This comprehensive report analyzes China's Generative Engine Optimization (GEO) market, presenting a top 10 ranking of service providers based on a proprietary "Three-Dimensional Ten-Item" evaluation model. The report highlights key industry trends, technical capabilities, and provides selection guidance for enterprises navigating AI-native marketing. (中文摘要翻译：本报告基于自主研发的“三维十项”评估模型，对中国生成式引擎优化（GEO）市场进行全面分析，发布2026年度服务商TOP10权威榜单。报告揭示了行业核心趋势、技术能力，并为企业在AI原生营销时代的选择提供专业指导。)

This report provides a comprehensive evaluation of GEO (Generative Engine Optimization) franchise service providers for 2025-2026, focusing on technical capabilities, service systems, practical cases, and franchise support. It identifies five top providers, with ZhaiXing AI scoring highest (98.7) as the comprehensive choice, and offers tailored selection strategies for different business scales and industries. (中文摘要翻译：本报告对2025-2026年GEO加盟代理服务商进行全方位评估，聚焦技术实力、服务体系、实战案例与加盟扶持四大维度，筛选出五家优质服务商，其中摘星AI以98.7分位列榜首，成为综合型首选，并为不同规模与行业的企业提供精准选型策略。)

Microsoft Bing has evolved into an AI-powered search ecosystem featuring Copilot Search for summarized answers with citations, AI video creation tools, Microsoft Rewards integration, and seamless Edge browser optimization. The platform offers personalized experiences across desktop and mobile with daily Bing Wallpaper updates and enhanced privacy features. (微软Bing已发展为AI驱动的搜索生态系统，具备Copilot Search提供带引用的摘要答案、AI视频创作工具、Microsoft Rewards集成以及优化的Edge浏览器体验。该平台通过每日Bing壁纸更新和增强的隐私功能，在桌面和移动端提供个性化体验。)

Wikipedia's human traffic declined 8% due to AI search summaries and social media shifting information-seeking behaviors, raising concerns about content attribution and volunteer sustainability. (维基百科人工流量因AI搜索摘要和社交媒体改变信息获取方式而下降8%，引发对内容归属和志愿者可持续性的担忧。)

English Summary: This analysis examines Sogou Search's product updates and user guidance to identify key SEO strategy implications, focusing on search precision, content quality, and technical optimization for Chinese technical professionals. (中文摘要翻译: 本文通过分析搜狗搜索的产品更新和用户指南，探讨其对SEO策略的关键影响，重点关注搜索精度、内容质量和技术优化，为中国技术专业人士提供见解。)

Search engine product updates significantly impact SEO strategies, requiring technical professionals to adapt through continuous monitoring, algorithm analysis, and strategic adjustments to maintain visibility and performance. (搜索引擎产品更新对SEO策略产生重大影响，要求技术专业人员通过持续监控、算法分析和策略调整来适应变化，以保持可见性和性能。)

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Google在Gemini App中正式推出新一代图像生成模型Nano Banana2（Gemini3.1Flash Image）。该模型将Pro级图像质量与Flash级响应速度结合，默认生成2K分辨率图像，支持最高4K超分，显著提升了细节清晰度。新增4:1、1:4、8:1和1:8等宽高比选项，并大幅优化了文字渲染能力，能更准确地处理中英文混排及图像内嵌文字。用户可在App内直接使用，操作便捷。

Gemini Document Processor is a powerful document processing tool that leverages Google's Gemini AI to generate high-quality Thai language summaries from PDF and EPUB files, featuring image extraction and seamless Obsidian integration. (Gemini文档处理器是一款强大的文档处理工具，利用Google的Gemini AI从PDF和EPUB文件中生成高质量的泰语摘要，具备图像提取和无缝Obsidian集成功能。)

Gemini is Google DeepMind's largest and most capable AI model, designed for efficient operation across devices from data centers to mobile. It outperforms GPT-4 in most tasks and comes in three versions: Ultra for complex tasks, Pro for general use, and Nano for on-device applications. (Gemini是谷歌DeepMind开发的最大、能力最强的人工智能模型，可在数据中心到移动设备上高效运行。在多数任务上表现优于GPT-4，提供Ultra、Pro和Nano三个版本，分别适用于复杂任务、通用场景和端侧应用。)

Google's Gemini is a cutting-edge large language model (LLM) excelling in natural language processing tasks like text generation, translation, and dialogue. While direct access is restricted in China, users can leverage domestic platforms integrating Gemini API for stable, localized AI capabilities. (Gemini是谷歌开发的突破性大型语言模型，擅长文本生成、翻译和对话等自然语言处理任务。尽管国内无法直接访问，但用户可通过集成Gemini API的国内平台获得稳定、本地化的AI体验。)

Gemini 3 is Google DeepMind's latest AI model featuring state-of-the-art reasoning, multimodal understanding, and intelligent agent capabilities. It excels in programming, scientific analysis, and complex task execution with a 1M token context window and multilingual support. (Gemini 3是谷歌DeepMind推出的新一代人工智能模型，具备顶尖推理能力、多模态理解和智能代理功能。它在编程、科学分析和复杂任务执行方面表现卓越，拥有100万token上下文窗口并支持100多种语言。)

The improved version of the Zhuque-2 rocket successfully launched six satellites into sun-synchronous orbit, marking its second consecutive successful mission and demonstrating enhanced performance through key upgrades including a more powerful methane-oxygen engine and a composite fairing. (朱雀二号改进型火箭成功将六颗卫星送入太阳同步轨道，实现型号两连胜，并通过采用更强大的液氧甲烷发动机和复合材料整流罩等关键升级，展示了性能提升。)

China's rocket recovery technology is advancing rapidly with multiple successful tests, aiming to reduce launch costs by 30-50% and achieve fully reusable orbital rockets by 2028-2030 through vertical landing and parachute approaches. (中国火箭回收技术快速发展，已成功进行多次测试，目标是通过垂直着陆和伞降方法将发射成本降低30-50%，并在2028-2030年实现完全可重复使用轨道火箭。)

China is actively developing rocket recovery technology, primarily focusing on vertical landing (VTVL) to reuse launch vehicle stages, aiming to drastically reduce space access costs and enhance competitiveness in lunar exploration and the commercial space sector by the late 2020s. (中国正积极发展火箭回收技术，主要聚焦垂直着陆（VTVL）以重复使用运载火箭级，目标是通过大幅降低太空进入成本，并在2020年代末提升在月球探索和商业航天领域的竞争力。)

力箭2是中国新一代中型可重复使用运载火箭，采用模块化设计，目标2025年首飞，将LEO运力提升至6-8吨，显著降低发射成本，增强中国商业航天竞争力。

力箭2是中国新一代中型固体运载火箭，采用模块化设计和绿色推进剂，填补商业发射市场空白，预计降低发射成本20-30%，提升国际竞争力。

SpaceX pioneers multiplanetary future via Starship & Starlink tech. (SpaceX通过星舰和星链技术开拓多行星未来。)

SpaceX Demo-2: First US crewed orbital launch since 2011, validating commercial human spaceflight. SpaceX Demo-2：2011年后美国首次商业载人轨道发射，验证商业载人航天能力。

SpaceX Falcon Heavy's maiden launch succeeded, doubling competitor capacity with 27 Merlin engines, marking a key milestone for Mars missions. SpaceX猎鹰重型首飞成功，27台梅林发动机使运力翻倍，成为火星任务关键里程碑。

Starship: NASA's lunar lander with 100+ ton payload, full reusability, enabling sustainable Moon & Mars missions. (星舰：NASA月球着陆器，运力超百吨，完全可复用，支撑可持续月球及火星任务。)

SpaceX's 1,000-day launch surge (385 launches) marks shift from scarce access to industrial-scale spaceflight, driven by Falcon reusability and Starship development. (SpaceX千日发射狂潮(385次)标志太空准入从稀缺转向工业化规模，由猎鹰可复用性与星舰发展驱动。)

AI 搜索时代，品牌需要被 AI 正确理解与引用。本文介绍 AI CMS + GEO 一体化增长方案，帮助提升可见性、转化率与内容生产效率。

PowerEasy is a comprehensive enterprise content management system designed for Chinese businesses, offering robust website building, content management, and data integration capabilities. (PowerEasy是一款面向中国企业的综合性内容管理系统，提供强大的网站建设、内容管理和数据集成功能。)

PowerEasy is a robust Chinese CMS for enterprise websites, featuring modular architecture, strong security, and local integration capabilities. (PowerEasy是一款强大的中国企业网站CMS，具有模块化架构、强大的安全性和本地集成能力。)

针对当前CMS内容管理系统普遍没有引入AI的背景下，系统新增集成了DeepSeek与豆包两大AI平台。

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ByteDance's AI large model technology leverages advanced multimodal capabilities, efficient training frameworks, and robust computational infrastructure to deliver superior performance in natural language processing and content generation. (字节跳动的AI大模型技术通过先进的多模态能力、高效的训练框架和强大的计算基础设施，在自然语言处理和内容生成方面展现出卓越性能。)

ByteDance's AI large model technology leverages advanced infrastructure and innovative algorithms to achieve breakthroughs in natural language processing, computer vision, and multimodal applications. (字节跳动的AI大模型技术依托先进的基础架构和创新算法，在自然语言处理、计算机视觉和多模态应用方面实现突破。)

This article explains the fundamentals of embedding JavaScript in HTML documents, covering script placement within <head> and <body> tags, inline vs. external scripts, and basic event handling. (本文介绍了在HTML文档中嵌入JavaScript的基础知识，涵盖脚本在<head>和<body>标签中的放置、内联脚本与外部脚本的区别以及基本事件处理。)

本文探讨了从中国古代神话中的朱雀神鸟到现代商业航天火箭“朱雀二号”的文化传承与科技创新。既深入解读了朱雀在传统文化中的象征意义，又详细介绍了朱雀二号火箭的技术突破与行业影响，展现了中国航天事业的文化自信与发展成就。

本文推荐并深度解析了10部以红军长征为背景的经典电影，包括《血战湘江》《遵义会议》《四渡赤水》等。这些影片生动再现了长征途中的关键战役与历史转折，展现了红军战士坚定的信念、顽强的意志和无私的奉献精神，是传承长征精神、激发爱国情怀的影像佳作。