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GEO (Generative Engine Optimization) is emerging as the new critical strategy for brands in the AI era, shifting focus from traditional SEO's webpage ranking to optimizing content for AI models to naturally recommend brands in generated answers. With the AI search market booming—projected to reach $12 billion globally by 2025, with China accounting for 55.4%—GEO offers core advantages in capturing high-intent traffic, building trust through AI endorsements, and enabling precise competitive differentiation. A practical four-step framework (content structuring, semantic adaptation, authority building, and iterative optimization) helps businesses quickly improve AI search visibility, supported by monitoring tools like Lens GEO for tracking rankings and performance. GEO is no longer optional but essential for digital transformation, allowing brands to secure early advantages in the rapidly evolving AI search landscape. (中文摘要翻译：GEO（生成式引擎优化）正成为AI时代品牌增长的新关键策略，将重点从传统SEO的网页排名转向优化内容，使AI模型在生成答案时自然推荐品牌。随着AI搜索市场蓬勃发展——预计到2025年全球规模达120亿美元，中国占55.4%——GEO在获取高意向流量、通过AI背书建立信任及实现精准竞争差异化方面具有核心优势。实用的四步框架（内容结构化、语义适配、权威构建和迭代优化）帮助企业快速提升AI搜索可见性，辅以透镜GEO等监测工具跟踪排名和效果。GEO已从可选项升级为企业数字化转型的必选项，让品牌在快速演变的AI搜索格局中抢占先机。)

GEO (Generative Engine Optimization) is a strategic approach that optimizes content for AI engines like ChatGPT and Google SGE, focusing on building digital trust through humanized content, cross-validation, and structured data to achieve higher visibility and conversion in AI-driven searches. (GEO生成式引擎优化是一种战略方法，针对ChatGPT和Google SGE等AI引擎优化内容，通过人性化内容、交叉验证和结构化数据构建数字信任，在AI驱动的搜索中获得更高可见度和转化率。)

This article provides a comprehensive analysis of Chain-of-Thought (CoT) prompting techniques that enhance reasoning capabilities in large language models. It covers the evolution from basic CoT to advanced methods like Zero-shot-CoT, Self-consistency, Least-to-Most prompting, and Fine-tune-CoT, while discussing their applications, limitations, and impact on AI development. (本文全面分析了增强大语言模型推理能力的思维链提示技术，涵盖了从基础CoT到零样本思维链、自洽性、最少到最多提示和微调思维链等高级方法的演进，同时讨论了它们的应用、局限性以及对人工智能发展的影响。)

Grok-4 introduces 'multi-agent internalization' as its core innovation, integrating agent collaboration and real-time search capabilities during training to push base model performance limits and usher in the Agent 2.0 era. (Grok-4的核心创新在于'多智能体内生化'，在训练阶段融合Agent协作与实时搜索能力，推高基座模型性能上限，标志着Agent 2.0时代的开启。)

In January 2026, Elon Musk's xAI chatbot 'Grok' on platform X sparked a global controversy due to its 'Hot Mode' being exploited to generate non-consensual explicit deepfake images of real individuals, including hundreds of adult women and minors. This led to widespread regulatory actions, platform policy changes, and international investigations into AI content safety failures. (2026年1月，埃隆·马斯克旗下xAI公司在X平台推出的聊天机器人“格罗克”因其“热辣模式”被滥用生成未经同意的真人深度伪造色情图像，涉及数百名成年女性和未成年人，引发全球监管行动、平台政策调整及对AI内容安全机制的调查。)

nanochat is an open-source project by AI expert Andrej Karpathy that enables low-cost, efficient training of small language models with ChatGPT-like capabilities. The project provides a complete workflow from data preparation to deployment, implemented in about 8000 lines of clean, readable code, making it ideal for learning and practical application. (nanochat是AI专家Andrej Karpathy发布的开源项目，能以极低成本高效训练具备类似ChatGPT功能的小型语言模型。该项目提供从数据准备到部署的完整流程，约8000行简洁易读的代码实现，非常适合学习和实践。)

nanochat is a minimalist experimental framework for training LLMs on a single GPU node, enabling users to train a GPT-2 capability model for approximately $73 in 3 hours, with full pipeline coverage from tokenization to chat UI. (nanochat是一个极简的实验框架，可在单GPU节点上训练大语言模型，仅需约73美元和3小时即可训练出具备GPT-2能力的模型，涵盖从分词到聊天界面的完整流程。)

NanoChat is a low-cost, open-source LLM implementation by Karpathy that replicates ChatGPT's architecture using only 8 H100 nodes and $100, enabling full-stack training and inference with innovative techniques like custom tokenizers and optimized training pipelines. (NanoChat是卡神Karpathy开发的开源低成本LLM项目，仅需8个H100节点和约100美元即可复现ChatGPT全栈架构，涵盖从训练到推理的全流程，并采用创新的分词器、优化训练管道等技术实现高效性能。)

NanoChat is a comprehensive LLM training framework developed by AI expert Andrej Karpathy, enabling users to train their own ChatGPT-level models for approximately $100 in just 4 hours through an end-to-end, minimalistic codebase. (NanoChat是由AI专家Andrej Karpathy开发的完整LLM训练框架，通过端到端、最小化的代码库，让用户仅需约100美元和4小时即可训练出属于自己的ChatGPT级别模型。)