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+0800</pubDate><guid>https://guijiagi.com/posts/latency-optimization-llm-serving/</guid><description>从首Token延迟到生成速度，LLM服务延迟优化的完整技术栈</description></item><item><title>2026 AI命令行工具集：终端中的AI力量</title><link>https://guijiagi.com/posts/ai-tools-command-line-2026/</link><pubDate>Thu, 02 Jul 2026 11:34:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-tools-command-line-2026/</guid><description>2026年最实用的AI命令行工具集，让AI成为你的终端助手</description></item><item><title>AI成本优化2026实战</title><link>https://guijiagi.com/posts/ai-cost-optimization-2026/</link><pubDate>Thu, 02 Jul 2026 11:34:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-cost-optimization-2026/</guid><description>从模型选择到缓存策略，AI系统成本优化的全方位实战指南</description></item><item><title>LocalAI 2026自托管指南：完全掌控你的AI</title><link>https://guijiagi.com/posts/local-ai-2026-self-host/</link><pubDate>Thu, 02 Jul 2026 11:33:00 +0800</pubDate><guid>https://guijiagi.com/posts/local-ai-2026-self-host/</guid><description>2026年LocalAI自托管AI服务部署指南，替代OpenAI 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+0800</pubDate><guid>https://guijiagi.com/posts/lora-fine-tuning-step-by-step/</guid><description>从环境搭建到模型部署，LoRA微调的完整手把手教程</description></item><item><title>CrewAI生产实践2026：打造AI梦之队</title><link>https://guijiagi.com/posts/crewai-production-2026/</link><pubDate>Thu, 02 Jul 2026 11:29:00 +0800</pubDate><guid>https://guijiagi.com/posts/crewai-production-2026/</guid><description>2026年CrewAI框架在生产环境中的最佳实践与经验分享</description></item><item><title>微调数据准备最佳实践</title><link>https://guijiagi.com/posts/fine-tuning-data-preparation/</link><pubDate>Thu, 02 Jul 2026 11:29:00 +0800</pubDate><guid>https://guijiagi.com/posts/fine-tuning-data-preparation/</guid><description>从数据采集到质量控制，LLM微调数据准备的完整最佳实践</description></item><item><title>AutoGen 2026多智能体：协作AI的新范式</title><link>https://guijiagi.com/posts/autogen-2026-multi-agent/</link><pubDate>Thu, 02 Jul 2026 11:28:00 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o3、GPT-5.5和DeepSeek-R2在推理任务上的表现</description></item><item><title>Agent自动化运维：从Self-healing到Auto-scaling</title><link>https://guijiagi.com/posts/agent-automated-ops-self-healing-auto-scaling/</link><pubDate>Tue, 30 Jun 2026 11:15:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-automated-ops-self-healing-auto-scaling/</guid><description>全面介绍Agent系统的自动化运维体系，涵盖自愈机制、自动扩缩容、智能告警、故障预测及AIOps实践</description></item><item><title>AI音乐生成2026：Suno vs Udio vs MusicGen横评</title><link>https://guijiagi.com/posts/ai-music-generation-2026-suno-udio-musicgen/</link><pubDate>Tue, 30 Jun 2026 11:15:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-music-generation-2026-suno-udio-musicgen/</guid><description>2026年主流AI音乐生成工具全面对比评测，Suno v4、Udio 2和Meta MusicGen的作曲能力、声音质量和实际应用</description></item><item><title>Prompt安全加固：防注入、防泄露、防操纵</title><link>https://guijiagi.com/posts/prompt-security-hardening-injection-leak-manipulation/</link><pubDate>Tue, 30 Jun 2026 11:15:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-security-hardening-injection-leak-manipulation/</guid><description>系统介绍Prompt安全加固的完整方案，覆盖防注入、防System Prompt泄露、防用户操纵三大核心领域，提供工程化实现</description></item><item><title>中文大模型能力测试：二十项专业领域实测</title><link>https://guijiagi.com/posts/chinese-llm-20-domain-test/</link><pubDate>Tue, 30 Jun 2026 11:15:00 +0800</pubDate><guid>https://guijiagi.com/posts/chinese-llm-20-domain-test/</guid><description>覆盖20个专业领域的中文大模型能力深度实测与排名</description></item><item><title>Agent成本优化实战：从Token到基础设施的全面降本</title><link>https://guijiagi.com/posts/agent-cost-optimization-token-to-infra/</link><pubDate>Tue, 30 Jun 2026 11:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-cost-optimization-token-to-infra/</guid><description>从Token消耗、模型选择、缓存策略到基础设施优化的Agent系统全链路成本优化实战指南</description></item><item><title>AI视频生成2026：Sora 2 vs Runway Gen-4 vs 可灵3.0</title><link>https://guijiagi.com/posts/ai-video-generation-2026-sora-vs-runway-kling/</link><pubDate>Tue, 30 Jun 2026 11:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-video-generation-2026-sora-vs-runway-kling/</guid><description>2026年主流AI视频生成模型全面横评，从技术架构到实际应用，深入对比Sora 2、Runway Gen-4和快手可灵3.0的能力边界</description></item><item><title>代码大模型2026排行：SWE-Bench Pro时代</title><link>https://guijiagi.com/posts/code-llm-2026-ranking/</link><pubDate>Tue, 30 Jun 2026 11:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/code-llm-2026-ranking/</guid><description>2026年代码大模型排行榜，以SWE-Bench Pro为核心基准的全面对比</description></item><item><title>多语言Prompt工程：中文Prompt的特殊技巧</title><link>https://guijiagi.com/posts/multilingual-prompt-engineering-chinese/</link><pubDate>Tue, 30 Jun 2026 11:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/multilingual-prompt-engineering-chinese/</guid><description>深入探讨多语言Prompt工程的挑战与技巧，重点关注中文Prompt的特殊处理方法，包括语义差异、文化适配与混合语言策略</description></item><item><title>Agent链路追踪：OpenTelemetry与Jaeger实战</title><link>https://guijiagi.com/posts/agent-distributed-tracing-opentelemetry-jaeger/</link><pubDate>Tue, 30 Jun 2026 11:05:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-distributed-tracing-opentelemetry-jaeger/</guid><description>以实战视角解析Agent系统中OpenTelemetry链路追踪的完整实现，涵盖Span设计、上下文传播、Jaeger可视化及性能分析</description></item><item><title>AI数字人2026：虚拟主播的制作与部署</title><link>https://guijiagi.com/posts/ai-digital-human-2026/</link><pubDate>Tue, 30 Jun 2026 11:05:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-digital-human-2026/</guid><description>2026年AI数字人技术的全面指南，涵盖虚拟主播的制作流程、技术栈选型、部署方案和商业应用</description></item><item><title>Prompt版本控制与A/B测试：数据驱动的Prompt优化</title><link>https://guijiagi.com/posts/prompt-version-control-ab-testing/</link><pubDate>Tue, 30 Jun 2026 11:05:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-version-control-ab-testing/</guid><description>详细介绍如何对Prompt实施版本控制与A/B测试，建立数据驱动的Prompt优化流程，包含统计方法与工程实现</description></item><item><title>端侧大模型部署：手机/Edge/IoT全场景选型</title><link>https://guijiagi.com/posts/edge-device-llm-deployment/</link><pubDate>Tue, 30 Jun 2026 11:05:00 +0800</pubDate><guid>https://guijiagi.com/posts/edge-device-llm-deployment/</guid><description>端侧大模型部署全场景指南，覆盖手机、边缘设备和IoT的模型选型与优化</description></item><item><title>Agent日志架构：结构化日志与分布式追踪</title><link>https://guijiagi.com/posts/agent-logging-architecture-distributed-tracing/</link><pubDate>Tue, 30 Jun 2026 11:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-logging-architecture-distributed-tracing/</guid><description>深入探讨Agent系统的日志架构设计，涵盖结构化日志标准、日志聚合、分布式追踪关联及日志分析实践</description></item><item><title>Prompt模板管理：企业级Prompt工程实践</title><link>https://guijiagi.com/posts/prompt-template-management-enterprise/</link><pubDate>Tue, 30 Jun 2026 11:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-template-management-enterprise/</guid><description>系统介绍企业级Prompt模板管理方案，涵盖模板设计、版本控制、权限管理、性能监控等核心实践</description></item><item><title>大模型API价格战2026：性价比排行</title><link>https://guijiagi.com/posts/llm-api-price-war-2026/</link><pubDate>Tue, 30 Jun 2026 11:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-api-price-war-2026/</guid><description>2026年大模型API价格全面对比，性价比排行榜与选型指南</description></item><item><title>开源AI生态2026：HuggingFace与社区力量</title><link>https://guijiagi.com/posts/open-source-ai-ecosystem-2026/</link><pubDate>Tue, 30 Jun 2026 11:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/open-source-ai-ecosystem-2026/</guid><description>2026年开源AI生态系统的全面梳理，涵盖HuggingFace、模型开源趋势、社区力量和开源AI的经济可持续性</description></item><item><title>Agent监控告警最佳实践：从指标到告警全链路</title><link>https://guijiagi.com/posts/agent-monitoring-alerting-best-practices/</link><pubDate>Tue, 30 Jun 2026 10:55:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-monitoring-alerting-best-practices/</guid><description>系统阐述Agent系统监控告警体系的设计与实现，涵盖指标体系、告警规则、告警路由、值班排班及告警治理全链路</description></item><item><title>AI与人类协作设计：从工具到伙伴</title><link>https://guijiagi.com/posts/ai-human-collaboration-design/</link><pubDate>Tue, 30 Jun 2026 10:55:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-human-collaboration-design/</guid><description>2026年AI与人类协作设计的最新实践，探讨从工具范式到伙伴范式的转变，以及协作设计的框架与方法论</description></item><item><title>Few-shot Prompting 2026：示例选择与排列优化</title><link>https://guijiagi.com/posts/few-shot-prompting-2026-example-selection/</link><pubDate>Tue, 30 Jun 2026 10:55:00 +0800</pubDate><guid>https://guijiagi.com/posts/few-shot-prompting-2026-example-selection/</guid><description>深入探讨2026年Few-shot Prompting的最佳实践，涵盖示例选择算法、排列优化策略、跨语言Few-shot等进阶技术</description></item><item><title>GLM-5系列评测：智谱AI的全栈实力</title><link>https://guijiagi.com/posts/glm-5-series-evaluation/</link><pubDate>Tue, 30 Jun 2026 10:55:00 +0800</pubDate><guid>https://guijiagi.com/posts/glm-5-series-evaluation/</guid><description>全面评测智谱AI的GLM-5系列，国产大模型的另一重要力量</description></item><item><title>Agent路由架构：从简单路由到智能路由</title><link>https://guijiagi.com/posts/agent-routing-architecture-intelligent-routing/</link><pubDate>Tue, 30 Jun 2026 10:50:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-routing-architecture-intelligent-routing/</guid><description>深入探讨Agent系统的路由架构演进，涵盖规则路由、语义路由、学习型路由的设计与实现，以及多模型智能调度策略</description></item><item><title>AI哲学思考：智能的本质与边界</title><link>https://guijiagi.com/posts/ai-philosophy-essence-of-intelligence/</link><pubDate>Tue, 30 Jun 2026 10:50:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-philosophy-essence-of-intelligence/</guid><description>从哲学视角审视2026年AI发展，探讨智能的本质、意识的边界、认识论的挑战和存在论的追问</description></item><item><title>Gemma 3评测：谷歌轻量开源模型</title><link>https://guijiagi.com/posts/gemma-3-evaluation/</link><pubDate>Tue, 30 Jun 2026 10:50:00 +0800</pubDate><guid>https://guijiagi.com/posts/gemma-3-evaluation/</guid><description>全面评测Google Gemma 3系列轻量开源模型，分析端侧部署表现</description></item><item><title>结构化输出技术：从JSON Mode到Function Calling</title><link>https://guijiagi.com/posts/structured-output-json-mode-to-function-calling/</link><pubDate>Tue, 30 Jun 2026 10:50:00 +0800</pubDate><guid>https://guijiagi.com/posts/structured-output-json-mode-to-function-calling/</guid><description>全面介绍2026年LLM结构化输出技术，包括JSON Mode、Function Calling、Constrained Decoding等方案及其实际应用</description></item><item><title>Agent限流与熔断：从令牌桶到自适应限流</title><link>https://guijiagi.com/posts/agent-rate-limiting-circuit-breaker/</link><pubDate>Tue, 30 Jun 2026 10:45:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-rate-limiting-circuit-breaker/</guid><description>全面剖析Agent系统的限流与熔断机制设计，涵盖令牌桶算法、自适应限流、多级熔断及降级策略</description></item><item><title>AI经济学2026：自动化对就业与工资的影响</title><link>https://guijiagi.com/posts/ai-economics-2026/</link><pubDate>Tue, 30 Jun 2026 10:45:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-economics-2026/</guid><description>2026年AI自动化对全球就业市场、工资水平、产业结构和劳动力转型的深度经济分析</description></item><item><title>Mistral Large 3评测：欧洲AI的代表</title><link>https://guijiagi.com/posts/mistral-large-3-evaluation/</link><pubDate>Tue, 30 Jun 2026 10:45:00 +0800</pubDate><guid>https://guijiagi.com/posts/mistral-large-3-evaluation/</guid><description>全面评测Mistral Large 3，分析这家欧洲AI公司的旗舰模型表现</description></item><item><title>Prompt工程进阶：Chain-of-Thought的变体与实践</title><link>https://guijiagi.com/posts/prompt-engineering-cot-variants/</link><pubDate>Tue, 30 Jun 2026 10:45:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-engineering-cot-variants/</guid><description>深入讲解Chain-of-Thought的各种变体，包括CoT、CoT-SC、ToT、GoT等，以及2026年最新实践技巧与代码实现</description></item><item><title>Agent循环检测与超时控制：从死循环到任务超时</title><link>https://guijiagi.com/posts/agent-cycle-detection-timeout-control/</link><pubDate>Tue, 30 Jun 2026 10:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-cycle-detection-timeout-control/</guid><description>系统讲解Agent系统中的循环检测算法与超时控制机制，涵盖状态追踪、循环识别、多级超时策略及自恢复方案</description></item><item><title>AI滥用风险防控：从深度伪造到自动化攻击</title><link>https://guijiagi.com/posts/ai-misuse-prevention-deepfake-to-automation/</link><pubDate>Tue, 30 Jun 2026 10:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-misuse-prevention-deepfake-to-automation/</guid><description>系统性分析2026年AI滥用的主要形式，包括深度伪造、自动化社会工程攻击、AI生成恶意软件等，提供全面的防控方案</description></item><item><title>Llama 4系列评测：Meta开源旗舰的表现</title><link>https://guijiagi.com/posts/llama-4-series-evaluation/</link><pubDate>Tue, 30 Jun 2026 10:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/llama-4-series-evaluation/</guid><description>全面评测Llama 4系列模型，从405B到8B的开源旗舰表现分析</description></item><item><title>具身智能2026：人形机器人从实验室到工厂</title><link>https://guijiagi.com/posts/embodied-intelligence-2026/</link><pubDate>Tue, 30 Jun 2026 10:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/embodied-intelligence-2026/</guid><description>2026年具身智能与人形机器人的最新进展，从Figure 03到Tesla Optimus Gen 3，分析技术突破与商业化路径</description></item><item><title>Agent多租户架构：资源隔离与成本分摊</title><link>https://guijiagi.com/posts/agent-multi-tenant-architecture/</link><pubDate>Tue, 30 Jun 2026 10:35:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-multi-tenant-architecture/</guid><description>深入探讨Agent系统的多租户架构设计，涵盖资源隔离策略、租户配额管理、成本分摊模型及安全边界设计</description></item><item><title>AI内容审核系统设计：多级过滤与实时拦截</title><link>https://guijiagi.com/posts/ai-content-moderation-system-design/</link><pubDate>Tue, 30 Jun 2026 10:35:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-content-moderation-system-design/</guid><description>详细介绍2026年AI内容审核系统的架构设计，包括多级过滤管道、实时拦截机制、误判率控制与人工复核流程</description></item><item><title>AI驱动科学发现2026：从AlphaFold到材料模拟</title><link>https://guijiagi.com/posts/ai-driven-scientific-discovery-2026/</link><pubDate>Tue, 30 Jun 2026 10:35:00 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+0800</pubDate><guid>https://guijiagi.com/posts/embedding-models-2026-benchmark/</guid><description>深度测评2026年主流Embedding模型在中文检索场景的表现，包括BGE、GTE、text-embedding-3等10款模型</description></item><item><title>GraphRAG 2026：知识图谱增强检索的实践指南</title><link>https://guijiagi.com/posts/graphrag-2026-knowledge-graph-retrieval-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/graphrag-2026-knowledge-graph-retrieval-guide/</guid><description>深入探讨GraphRAG在2026年的最新实践，从知识图谱构建到图谱增强检索的完整架构与代码实现</description></item><item><title>GraphRAG 2026：知识图谱增强检索的实践指南</title><link>https://guijiagi.com/posts/graphrag-2026-practice-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/graphrag-2026-practice-guide/</guid><description>深入解析GraphRAG在2026年的最新实践，从知识图谱构建到社区检测检索，附完整代码示例与性能基准</description></item><item><title>LoRA微调2026：从数据准备到部署的全流程</title><link>https://guijiagi.com/posts/lora-finetuning-2026-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-finetuning-2026-guide/</guid><description>2026年LoRA微调的最新实践指南，涵盖数据工程、训练配置、评估优化到生产部署的完整流程</description></item><item><title>QLoRA量化微调实战：显存减半效果不减</title><link>https://guijiagi.com/posts/qlora-quantized-finetuning-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/qlora-quantized-finetuning-guide/</guid><description>QLoRA让7B模型微调只需8GB显存，70B模型只需24GB，本文详解QLoRA原理与实战配置</description></item><item><title>RAG vs Long Context：何时用检索增强何时用长上下文</title><link>https://guijiagi.com/posts/rag-vs-long-context-comparison/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-vs-long-context-comparison/</guid><description>深度对比RAG与长上下文模型的优劣势，通过实测数据给出清晰的选型决策框架</description></item><item><title>RAG分块策略深度对比：语义分块 vs 文档感知 vs 层级分块</title><link>https://guijiagi.com/posts/rag-chunking-strategies-comparison/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-chunking-strategies-comparison/</guid><description>分块是RAG系统中影响检索质量最关键的环节，本文深度对比六种分块策略的原理、效果与适用场景</description></item><item><title>RAG评估框架：RAGAS指标体系与自定义评估</title><link>https://guijiagi.com/posts/rag-evaluation-framework-ragas/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-evaluation-framework-ragas/</guid><description>系统介绍RAGAS评估框架的核心指标体系、自动化评估流程，以及如何构建自定义评估pipeline</description></item><item><title>RAG评估框架：RAGAS指标体系与自定义评估</title><link>https://guijiagi.com/posts/ragas-evaluation-framework-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/ragas-evaluation-framework-guide/</guid><description>系统介绍RAGAS评估框架的指标体系、实现方法，以及如何构建自定义RAG评估流水线</description></item><item><title>RAG生产排坑指南：幻觉、漏检、延迟三大难题</title><link>https://guijiagi.com/posts/rag-production-pitfalls-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 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+0800</pubDate><guid>https://guijiagi.com/posts/multimodal-rag-architecture/</guid><description>多模态RAG让AI能同时理解和检索图片与文字，本文详解四种架构设计与Claude 4/GPT-5的实践方案</description></item><item><title>向量数据库2026选型：Milvus vs Pinecone vs Weaviate vs Qdrant</title><link>https://guijiagi.com/posts/vector-database-2026-comparison/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/vector-database-2026-comparison/</guid><description>从性能、功能、成本、生态四个维度深度对比四大向量数据库，附2026年最新基准测试数据</description></item><item><title>向量数据库2026选型：Milvus vs Pinecone vs Weaviate vs Qdrant</title><link>https://guijiagi.com/posts/vector-database-comparison-2026/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/vector-database-comparison-2026/</guid><description>2026年主流向量数据库全面对比，从性能基准测试到场景选型建议，帮你做出最佳选择</description></item><item><title>CrewAI 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+0800</pubDate><guid>https://guijiagi.com/posts/agent-orchestration-patterns-2026/</guid><description>系统梳理2026年Agent编排的核心模式，从简单串行到复杂图式编排，包含代码示例、选型决策树和反模式警示</description></item><item><title>Agent记忆系统设计：短期、长期与情景记忆的实现</title><link>https://guijiagi.com/posts/agent-memory-system-design/</link><pubDate>Tue, 30 Jun 2026 09:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-memory-system-design/</guid><description>系统讲解Agent记忆系统的三层架构设计，涵盖短期工作记忆、长期语义记忆和情景记忆的实现方案与工程实践</description></item><item><title>Agent可观测性：追踪、日志与指标的统一方案</title><link>https://guijiagi.com/posts/agent-observability-unified-solution/</link><pubDate>Tue, 30 Jun 2026 09:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-observability-unified-solution/</guid><description>构建Agent系统的统一可观测性方案，涵盖分布式追踪、结构化日志、实时指标监控和异常检测的工程实践</description></item><item><title>Agent生产部署Checklist：50个必查项</title><link>https://guijiagi.com/posts/agent-production-deployment-checklist/</link><pubDate>Tue, 30 Jun 2026 09:30:00 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30 Jun 2026 09:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/crewai-2026-production-deployment/</guid><description>基于6个月生产环境实践，深度解析CrewAI 2026的多Agent协作模式、角色编排策略及生产部署经验</description></item><item><title>Dify 2026：开源AI应用开发平台的崛起</title><link>https://guijiagi.com/posts/dify-2026-open-source-ai-platform/</link><pubDate>Tue, 30 Jun 2026 09:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/dify-2026-open-source-ai-platform/</guid><description>深度解析Dify 2026版本的工作流引擎、RAG管道、模型管理及企业级部署方案，揭示其成为开源AI平台首选的原因</description></item><item><title>LangGraph 2026：图式Agent工作流的最佳实践</title><link>https://guijiagi.com/posts/langgraph-2026-graph-agent-workflow-best-practices/</link><pubDate>Tue, 30 Jun 2026 09:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/langgraph-2026-graph-agent-workflow-best-practices/</guid><description>深入解析LangGraph 2026版本的图式Agent工作流引擎，从状态管理到条件路由的完整实践指南</description></item><item><title>LangGraph 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2026龙虾智能体框架的架构设计、Skill生态、多模型支持及实际部署案例</description></item><item><title>Semantic Kernel 2026：微软AI编排框架的成熟之路</title><link>https://guijiagi.com/posts/semantic-kernel-2026-enterprise-ai-orchestration/</link><pubDate>Tue, 30 Jun 2026 09:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/semantic-kernel-2026-enterprise-ai-orchestration/</guid><description>回顾Semantic Kernel从实验性项目到企业级AI编排框架的演进，分析其核心抽象、插件系统和与Azure生态的深度集成</description></item><item><title>多Agent系统架构设计：通信、协调与冲突解决</title><link>https://guijiagi.com/posts/multi-agent-system-architecture-design/</link><pubDate>Tue, 30 Jun 2026 09:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/multi-agent-system-architecture-design/</guid><description>深入探讨多Agent系统的三大核心问题：Agent间通信协议、任务协调策略及冲突检测与解决机制，附实际系统架构案例</description></item><item><title>AI Agent 在能源行业的优化方案：从电网调度到新能源消纳</title><link>https://guijiagi.com/posts/ai-agent-energy-industry/</link><pubDate>Tue, 30 Jun 2026 09:25:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-agent-energy-industry/</guid><description>深度解析AI Agent在能源行业的五大应用场景，含电网调度、新能源消纳、油气勘探、能耗管理、碳交易等真实案例</description></item><item><title>AI Agent 在农业领域的智能化应用：从精准种植到智慧畜牧</title><link>https://guijiagi.com/posts/ai-agent-agriculture/</link><pubDate>Tue, 30 Jun 2026 09:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-agent-agriculture/</guid><description>全面解析AI Agent在农业领域的六大应用场景，含精准种植、病虫害防治、智慧畜牧、农业供应链等真实案例</description></item><item><title>MoE混合专家模型深度解析：路由机制与负载均衡</title><link>https://guijiagi.com/posts/moe-expert-routing-load-balance/</link><pubDate>Tue, 30 Jun 2026 09:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/moe-expert-routing-load-balance/</guid><description>深入解析MoE混合专家模型的路由机制、负载均衡策略以及在2026年的最新进展</description></item><item><title>MoE混合专家模型深度解析：路由机制与负载均衡</title><link>https://guijiagi.com/posts/moe-mixture-of-experts-routing-load-balancing/</link><pubDate>Tue, 30 Jun 2026 09:20:00 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+0800</pubDate><guid>https://guijiagi.com/posts/kv-cache-principles/</guid><description>深入解析KV Cache的工作原理、内存计算、优化策略与2026年最新技术进展</description></item><item><title>分词器原理与工程实践：BPE vs SentencePiece vs Unigram</title><link>https://guijiagi.com/posts/tokenizer-principles-and-practice/</link><pubDate>Sun, 28 Jun 2026 11:03:00 +0800</pubDate><guid>https://guijiagi.com/posts/tokenizer-principles-and-practice/</guid><description>深入解析BPE、WordPiece、Unigram、SentencePiece四种分词算法的原理、优缺点与2026年工程实践</description></item><item><title>位置编码深度对比：RoPE vs ALiBi vs NoPE 实测分析</title><link>https://guijiagi.com/posts/positional-encoding-comparison/</link><pubDate>Sun, 28 Jun 2026 11:02:00 +0800</pubDate><guid>https://guijiagi.com/posts/positional-encoding-comparison/</guid><description>深入对比RoPE旋转位置编码、ALiBi线性偏置、NoPE无位置编码三种方案的数学原理、长文本外推能力与实测性能</description></item><item><title>注意力机制全景解析：Self/Cross/Multi-Query/Latent Attention</title><link>https://guijiagi.com/posts/attention-mechanisms-panorama/</link><pubDate>Sun, 28 Jun 2026 11:01:00 +0800</pubDate><guid>https://guijiagi.com/posts/attention-mechanisms-panorama/</guid><description>系统解析2026年主流注意力机制变体：Self-Attention、Cross-Attention、Multi-Query、Grouped-Query、Latent Attention的原理、数学推导与工程实现</description></item><item><title>Agent 工具发现机制：从静态注册到动态发现</title><link>https://guijiagi.com/posts/agent-tool-discovery-mcp/</link><pubDate>Sun, 28 Jun 2026 11:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-tool-discovery-mcp/</guid><description>探讨Agent工具发现机制的演进，从静态注册到MCP协议的动态发现，构建灵活的工具生态</description></item><item><title>AgentBuilder：国产 Agent 开发平台对比</title><link>https://guijiagi.com/posts/agentbuilder-china-platform-comparison/</link><pubDate>Sun, 28 Jun 2026 11:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agentbuilder-china-platform-comparison/</guid><description>深度对比 2026 年国产 Agent 开发平台，涵盖百度 AppBuilder、阿里百炼、腾讯混元、字节扣子等主流平台</description></item><item><title>AutoGPT 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Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/codex-project-init/</guid><description>Codex项目初始化指南</description></item><item><title>Codex与CI/CD集成实践</title><link>https://guijiagi.com/posts/codex-ci-cd-integration/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/codex-ci-cd-integration/</guid><description>Codex与CI/CD集成实践</description></item><item><title>Codex重构智能体实践</title><link>https://guijiagi.com/posts/codex-refactoring-agent/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/codex-refactoring-agent/</guid><description>Codex重构智能体实践</description></item><item><title>Codex自动化测试生成</title><link>https://guijiagi.com/posts/codex-test-generation/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/codex-test-generation/</guid><description>Codex自动化测试生成</description></item><item><title>CrewAI多Agent开源方案</title><link>https://guijiagi.com/posts/crewai-multi-agent-opensource/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/crewai-multi-agent-opensource/</guid><description>CrewAI多Agent开源方案</description></item><item><title>Cursor Agent模式评测</title><link>https://guijiagi.com/posts/cursor-agent-mode-review/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/cursor-agent-mode-review/</guid><description>Cursor Agent模式评测</description></item><item><title>Devin AI Agent实测报告</title><link>https://guijiagi.com/posts/devin-ai-agent-review/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/devin-ai-agent-review/</guid><description>Devin AI Agent实测报告</description></item><item><title>Dify vs FastGPT平台对比</title><link>https://guijiagi.com/posts/dify-vs-fastgpt/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/dify-vs-fastgpt/</guid><description>Dify vs FastGPT平台对比</description></item><item><title>Dify平台评测与部署指南</title><link>https://guijiagi.com/posts/dify-platform-review/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/dify-platform-review/</guid><description>Dify平台评测与部署指南</description></item><item><title>FastGPT部署与实践</title><link>https://guijiagi.com/posts/fastgpt-deployment/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/fastgpt-deployment/</guid><description>FastGPT部署与实践</description></item><item><title>Few-Shot Prompting最佳实践</title><link>https://guijiagi.com/posts/few-shot-prompting-best-practices/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/few-shot-prompting-best-practices/</guid><description>从示例选择到格式优化的Few-Shot Prompting全面实践指南</description></item><item><title>Gemini Deep Research评测</title><link>https://guijiagi.com/posts/gemini-deep-research-eval/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/gemini-deep-research-eval/</guid><description>Gemini Deep Research评测</description></item><item><title>GitHub Copilot Agent能力分析</title><link>https://guijiagi.com/posts/copilot-agent-capability/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/copilot-agent-capability/</guid><description>GitHub Copilot Agent能力分析</description></item><item><title>Google Gemini Agent更新解读</title><link>https://guijiagi.com/posts/google-gemini-agent-update-2026/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/google-gemini-agent-update-2026/</guid><description>全面解读Google Gemini Agent 2026年重大更新，技术能力与生态布局双线分析</description></item><item><title>Google Gemini Agent更新解读</title><link>https://guijiagi.com/posts/google-gemini-agent-update/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/google-gemini-agent-update/</guid><description>Google Gemini Agent最新更新解读</description></item><item><title>GraphRAG图检索增强生成</title><link>https://guijiagi.com/posts/graphrag-graph-retrieval/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/graphrag-graph-retrieval/</guid><description>GraphRAG图检索增强生成</description></item><item><title>GraphRAG图检索增强生成</title><link>https://guijiagi.com/posts/graphrag-knowledge-graph-rag/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/graphrag-knowledge-graph-rag/</guid><description>GraphRAG图检索增强生成的原理、架构与实践，突破传统向量检索的局限性</description></item><item><title>Haystack框架评测</title><link>https://guijiagi.com/posts/haystack-framework-review/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/haystack-framework-review/</guid><description>Haystack框架评测</description></item><item><title>Hermes 3架构深度解析</title><link>https://guijiagi.com/posts/hermes-3-architecture/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/hermes-3-architecture/</guid><description>Hermes 3架构深度解析</description></item><item><title>Hermes Agent部署实践</title><link>https://guijiagi.com/posts/hermes-agent-deployment/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/hermes-agent-deployment/</guid><description>Hermes Agent部署实践</description></item><item><title>Hermes JSON模式实践</title><link>https://guijiagi.com/posts/hermes-json-mode/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/hermes-json-mode/</guid><description>Hermes JSON模式实践</description></item><item><title>Hermes Prompt模板设计</title><link>https://guijiagi.com/posts/hermes-prompt-template/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/hermes-prompt-template/</guid><description>Hermes Prompt模板设计</description></item><item><title>Hermes vs Llama对比评测</title><link>https://guijiagi.com/posts/hermes-vs-llama-comparison/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/hermes-vs-llama-comparison/</guid><description>Hermes vs Llama对比评测</description></item><item><title>Hermes多语言能力分析</title><link>https://guijiagi.com/posts/hermes-multilingual/</link><pubDate>Sat, 27 Jun 2026 15:00:00 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+0800</pubDate><guid>https://guijiagi.com/posts/langgraph-agent-workflow/</guid><description>LangGraph Agent工作流评测</description></item><item><title>LlamaIndex发展现状与规划</title><link>https://guijiagi.com/posts/llamaindex-development/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llamaindex-development/</guid><description>LlamaIndex发展现状与规划</description></item><item><title>LLM代码生成能力评测</title><link>https://guijiagi.com/posts/llm-code-gen-eval/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-code-gen-eval/</guid><description>LLM代码生成能力评测</description></item><item><title>LLM代码生成能力评测：从基准到实战</title><link>https://guijiagi.com/posts/llm-code-generation-evaluation/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-code-generation-evaluation/</guid><description>深入解析大语言模型代码生成能力的评测方法论，涵盖主流基准与实战评估</description></item><item><title>LLM评测榜单可信度分析</title><link>https://guijiagi.com/posts/llm-benchmark-credibility/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-benchmark-credibility/</guid><description>LLM评测榜单可信度分析</description></item><item><title>LoRA微调参数调优指南</title><link>https://guijiagi.com/posts/lora-finetune-tuning/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-finetune-tuning/</guid><description>LoRA微调参数调优指南</description></item><item><title>LoRA微调参数调优指南</title><link>https://guijiagi.com/posts/lora-finetuning-tuning-guide/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-finetuning-tuning-guide/</guid><description>LoRA微调的核心参数调优指南，从rank选择到学习率设置的全面实践</description></item><item><title>MultiOn浏览器智能体评测</title><link>https://guijiagi.com/posts/multion-browser-agent/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/multion-browser-agent/</guid><description>MultiOn浏览器智能体评测</description></item><item><title>OpenAgent平台架构与使用</title><link>https://guijiagi.com/posts/openagent-platform/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/openagent-platform/</guid><description>OpenAgent平台架构与使用</description></item><item><title>OpenAI最新Agent产品发布分析</title><link>https://guijiagi.com/posts/openai-agent-product-launch-2026/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/openai-agent-product-launch-2026/</guid><description>深度解读OpenAI 2026年最新Agent产品线发布，分析其技术架构与商业策略</description></item><item><title>OpenAI最新Agent产品发布分析</title><link>https://guijiagi.com/posts/openai-agent-product-launch/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/openai-agent-product-launch/</guid><description>OpenAI最新Agent产品深度分析</description></item><item><title>OpenClaw MCP协议集成</title><link>https://guijiagi.com/posts/openclaw-mcp-integration/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/openclaw-mcp-integration/</guid><description>OpenClaw MCP协议集成</description></item><item><title>OpenClaw定时任务调度</title><link>https://guijiagi.com/posts/openclaw-cron-scheduling/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/openclaw-cron-scheduling/</guid><description>OpenClaw定时任务调度</description></item><item><title>OpenClaw技能开发指南</title><link>https://guijiagi.com/posts/openclaw-skill-development/</link><pubDate>Sat, 27 Jun 2026 15:00:00 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+0800</pubDate><guid>https://guijiagi.com/posts/openwebui-llm-interface/</guid><description>OpenWebUI大模型交互界面</description></item><item><title>Operator智能体实测</title><link>https://guijiagi.com/posts/operator-agent-review/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/operator-agent-review/</guid><description>Operator智能体实测</description></item><item><title>Pika Labs 2026新版评测</title><link>https://guijiagi.com/posts/pika-labs-2026/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/pika-labs-2026/</guid><description>Pika Labs 2026新版评测</description></item><item><title>Prompt版本管理实践</title><link>https://guijiagi.com/posts/prompt-version-management/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-version-management/</guid><description>将Prompt纳入版本管理的工程实践，从Git工作流到自动化测试的完整方案</description></item><item><title>Prompt工程团队协作流程</title><link>https://guijiagi.com/posts/prompt-engineering-team-workflow/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-engineering-team-workflow/</guid><description>Prompt工程团队协作流程</description></item><item><title>Prompt模板管理系统设计</title><link>https://guijiagi.com/posts/prompt-template-management-system/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-template-management-system/</guid><description>构建可扩展的Prompt模板管理系统，实现Prompt的版本化、参数化和可观测管理</description></item><item><title>Prompt模板管理系统设计</title><link>https://guijiagi.com/posts/prompt-template-management/</link><pubDate>Sat, 27 Jun 2026 15:00:00 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市场格局与趋势分析</title><link>https://guijiagi.com/posts/ai-agent-market-2026/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-agent-market-2026/</guid><description>全面梳理 2026 年 AI Agent 市场的竞争格局、技术趋势和商业化路径，洞察 AGI 时代的发展方向</description></item><item><title>2026 AI 芯片竞赛：从训练到推理的全面博弈</title><link>https://guijiagi.com/posts/ai-chip-competition-2026/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-chip-competition-2026/</guid><description>2026 年，AI 芯片战场从训练侧向推理侧全面延伸。NVIDIA、AMD、华为昇腾等玩家在新格局下展开激烈角逐。</description></item><item><title>2026 全球 AI 监管政策全景扫描</title><link>https://guijiagi.com/posts/ai-regulation-global-2026/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-regulation-global-2026/</guid><description>全面梳理 2026 年全球主要经济体的 AI 监管政策进展，涵盖欧盟 AI Act、美国行政令、中国算法法规及国际合作动态</description></item><item><title>2026 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视频制作全流程：从脚本到成片</title><link>https://guijiagi.com/posts/ai-video-production-pipeline/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-video-production-pipeline/</guid><description>全面拆解 AI 视频制作的全链路工作流，涵盖脚本生成、分镜规划、视频生成、后期处理和质量评估，对比主流工具优劣。</description></item><item><title>AI 智能体伦理框架：从原则到实践</title><link>https://guijiagi.com/posts/ai-agent-ethics-framework/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-agent-ethics-framework/</guid><description>系统构建 AI 智能体伦理治理框架，从抽象原则到工程实现，覆盖价值观对齐、安全护栏、责任归属和审计追踪全链路。</description></item><item><title>Codex 进化论：从代码补全到自主编程</title><link>https://guijiagi.com/posts/codex-code-agent-evolution/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/codex-code-agent-evolution/</guid><description>追溯 OpenAI Codex 从简单代码补全到自主编程智能体的进化历程，解析其技术架构演变与未来发展方向。</description></item><item><title>Devin AI 软件工程师评测</title><link>https://guijiagi.com/posts/devin-agent-review/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/devin-agent-review/</guid><description>深入评测 Cognition AI 的 Devin 软件工程师智能体，从实际项目角度分析其能力边界、工作流程与适用场景</description></item><item><title>Embedding 模型横评：2026 中文场景实测</title><link>https://guijiagi.com/posts/embedding-model-comparison/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/embedding-model-comparison/</guid><description>针对 2026 年主流 Embedding 模型在中文场景下的全面横评，涵盖 MTEB 基准测试与真实业务场景实测</description></item><item><title>GitHub Copilot Agent 模式深度体验</title><link>https://guijiagi.com/posts/github-copilot-agent/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/github-copilot-agent/</guid><description>深度体验 GitHub Copilot Agent 模式，从自动代码补全到自主任务执行，探索 AI 编程助手的范式跃迁</description></item><item><title>Hermes 函数调用实战：构建工具增强型智能体</title><link>https://guijiagi.com/posts/hermes-function-calling-guide/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/hermes-function-calling-guide/</guid><description>深入解析 NousResearch Hermes 系列模型的函数调用能力，通过实战案例演示如何构建可靠的工具增强型智能体。</description></item><item><title>Kubernetes 上部署 AI 智能体：从容器到生产</title><link>https://guijiagi.com/posts/kubernetes-agent-deployment/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/kubernetes-agent-deployment/</guid><description>全面介绍在 Kubernetes 上部署 AI 智能体的完整流程，涵盖容器化、编排配置、GPU 调度、自动伸缩和生产级运维实践。</description></item><item><title>LLM-as-Judge 评估方法实战</title><link>https://guijiagi.com/posts/llm-judge-evaluation/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-judge-evaluation/</guid><description>系统介绍 LLM-as-Judge 评估方法的原理、实现与最佳实践，涵盖评分维度设计、Prompt 构建与结果分析</description></item><item><title>MCP 协议深度解析：从架构到实现</title><link>https://guijiagi.com/posts/mcp-protocol-deep-dive/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/mcp-protocol-deep-dive/</guid><description>深入剖析 Model Context Protocol (MCP) 的架构设计、通信机制、工具调用流程，并通过代码示例展示如何从零实现一个 MCP Server 与 Client。</description></item><item><title>Nous Hermes 系列模型全面评测</title><link>https://guijiagi.com/posts/nous-hermes-model-review/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/nous-hermes-model-review/</guid><description>从推理能力、指令遵循、多语言表现等维度，全面评测 NousResearch Hermes 系列开源大模型。</description></item><item><title>OpenAI Agents SDK 实战指南</title><link>https://guijiagi.com/posts/openai-agents-sdk-guide/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/openai-agents-sdk-guide/</guid><description>从架构设计到生产部署，全面解析 OpenAI Agents SDK 的核心概念、设计模式与实战最佳实践。</description></item><item><title>OpenClaw 智能体平台深度评测</title><link>https://guijiagi.com/posts/openclaw-agent-platform-review/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/openclaw-agent-platform-review/</guid><description>全面评测 OpenClaw 智能体平台的架构设计、核心能力、MCP 协议支持与实际开发体验，揭示这款新兴 AGI 平台的真实实力与不足。</description></item><item><title>Prompt 迭代优化：从经验到工程化</title><link>https://guijiagi.com/posts/agent-prompt-iteration/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-prompt-iteration/</guid><description>将 Prompt 优化从手工试错提升为系统工程化流程，涵盖版本管理、A/B 测试、评估闭环与自动化迭代</description></item><item><title>Prompt 链式设计：从简单到复杂的推理阶梯</title><link>https://guijiagi.com/posts/prompt-chain-design/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-chain-design/</guid><description>系统讲解 Prompt 链式设计的原理、模式与工程实践，构建从简单推理到复杂任务分解的完整方法论</description></item><item><title>Prompt 注入攻击防御实战指南</title><link>https://guijiagi.com/posts/prompt-injection-defense/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-injection-defense/</guid><description>系统讲解 Prompt 注入攻击的原理、分类与防御策略，包含红队测试方法、代码实现和生产级防御架构。</description></item><item><title>RAG vs 微调：2026 年的场景选择指南</title><link>https://guijiagi.com/posts/rag-vs-fine-tuning-2026/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-vs-fine-tuning-2026/</guid><description>RAG 和微调不是非此即彼的选择，而是互补的知识注入策略。本文从 2026 年的实践视角，给出全场景决策框架。</description></item><item><title>ReAct vs Plan-and-Execute：智能体推理范式对比</title><link>https://guijiagi.com/posts/react-vs-plan-execute/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/react-vs-plan-execute/</guid><description>深入对比 ReAct 与 Plan-and-Execute 两种主流智能体推理范式的工作原理、优劣势及适用场景，指导工程选型。</description></item><item><title>Runway Gen-3 AI 视频创作完全指南</title><link>https://guijiagi.com/posts/runway-gen3-guide/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/runway-gen3-guide/</guid><description>深入解析 Runway Gen-3 Alpha 的核心能力、创作工作流与实战技巧，助你快速掌握下一代 AI 视频生成技术。</description></item><item><title>Sora 视频生成实战指南</title><link>https://guijiagi.com/posts/sora-video-generation-guide/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/sora-video-generation-guide/</guid><description>OpenAI Sora 视频生成模型的实战指南，涵盖提示词技巧、参数调优、创意工作流与行业应用案例</description></item><item><title>多模态智能体设计：图文音视一体化架构</title><link>https://guijiagi.com/posts/multimodal-agent-design/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/multimodal-agent-design/</guid><description>探讨多模态智能体的架构设计，涵盖视觉语言模型、语音交互、视频理解与跨模态推理，附完整系统架构与代码实现。</description></item><item><title>多智能体协作模式：从层级到对等网络</title><link>https://guijiagi.com/posts/multi-agent-collaboration-patterns/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/multi-agent-collaboration-patterns/</guid><description>系统梳理多智能体协作的主流模式，从层级式到对等网络，结合 AutoGen、CrewAI 等框架解析架构设计与工程实践。</description></item><item><title>向量数据库选型：智能体记忆系统实践</title><link>https://guijiagi.com/posts/agent-memory-vector-db/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-memory-vector-db/</guid><description>从实际 Agent 记忆系统需求出发，深入对比 Pinecone、Milvus、Qdrant 等主流向量数据库的架构、性能与适用场景。</description></item><item><title>智能体 A/B 测试框架设计与实现</title><link>https://guijiagi.com/posts/agent-a-b-testing-framework/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-a-b-testing-framework/</guid><description>系统介绍 AI 智能体 A/B 测试框架的设计思路、核心组件、统计方法与工程实现，帮助团队科学评估智能体迭代效果。</description></item><item><title>智能体 UX 设计原则：打造人机协作体验</title><link>https://guijiagi.com/posts/agent-ux-design-principles/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-ux-design-principles/</guid><description>AGI 智能体的 UX 设计不是传统产品的延伸，而是一种全新的人机协作范式。从信任建立到控制感设计，七条核心原则与实践指南。</description></item><item><title>智能体安全检查清单：上线前必做 20 项</title><link>https://guijiagi.com/posts/agent-security-checklist/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-security-checklist/</guid><description>智能体产品上线前的安全检查清单，涵盖提示注入防护、权限控制、数据安全、合规审查等 20 项关键检查</description></item><item><title>智能体测试策略：从单元到端到端</title><link>https://guijiagi.com/posts/agent-testing-strategies/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-testing-strategies/</guid><description>系统介绍 AI Agent 测试的方法论、框架与最佳实践，覆盖单元测试、集成测试、E2E 测试和评估驱动开发全流程。</description></item><item><title>智能体负载均衡与并发控制</title><link>https://guijiagi.com/posts/agent-load-balancing/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-load-balancing/</guid><description>深入探讨 AI 智能体系统中的负载均衡策略与并发控制机制，涵盖流量调度、限流降级和弹性伸缩</description></item><item><title>智能体工具设计模式</title><link>https://guijiagi.com/posts/agent-tool-design-patterns/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-tool-design-patterns/</guid><description>系统梳理 AI 智能体工具设计的核心模式、最佳实践与反模式，助力构建可靠可扩展的工具调用体系</description></item><item><title>智能体工作流编排：从 DAG 到动态执行</title><link>https://guijiagi.com/posts/agent-workflow-orchestration/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-workflow-orchestration/</guid><description>深入探讨智能体工作流编排的演进路径，从静态 DAG 到动态执行图，涵盖 LangGraph 等主流框架的架构设计与实践</description></item><item><title>智能体幻觉缓解策略：从检测到修复</title><link>https://guijiagi.com/posts/agent-hallucination-mitigation/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-hallucination-mitigation/</guid><description>系统梳理 AGI 智能体幻觉问题的成因，提供从检测、缓解到修复的全链路策略，附实战代码与架构建议。</description></item><item><title>智能体可观测性平台搭建指南</title><link>https://guijiagi.com/posts/agent-observability-platform/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-observability-platform/</guid><description>从零搭建 AI 智能体的可观测性平台，涵盖 Trace 追踪、Token 监控、质量评估与告警体系，附完整代码实现。</description></item><item><title>智能体流式响应架构设计</title><link>https://guijiagi.com/posts/agent-streaming-response/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-streaming-response/</guid><description>深入探讨 AGI 智能体的流式响应架构，从 SSE 到 WebSocket，从模型骨架层到用户交互层的完整设计实录。</description></item><item><title>智能体评估数据集构建方法论</title><link>https://guijiagi.com/posts/agent-eval-dataset-construction/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-eval-dataset-construction/</guid><description>如何为 AGI 智能体构建科学、可复现的评估数据集？从任务设计到标注规范，从偏差控制到动态更新，一份完整的方法论指南。</description></item><item><title>Claude Opus 4.1 发布：超长上下文的工业级应用</title><link>https://guijiagi.com/posts/claude-opus-41-%E5%8F%91%E5%B8%83-%E8%B6%85%E9%95%BF%E4%B8%8A%E4%B8%8B%E6%96%87%E7%9A%84%E5%B7%A5%E4%B8%9A%E7%BA%A7%E5%BA%94%E7%94%A8/</link><pubDate>Fri, 26 Jun 2026 00:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/claude-opus-41-%E5%8F%91%E5%B8%83-%E8%B6%85%E9%95%BF%E4%B8%8A%E4%B8%8B%E6%96%87%E7%9A%84%E5%B7%A5%E4%B8%9A%E7%BA%A7%E5%BA%94%E7%94%A8/</guid><description>Anthropic 发布 Claude Opus 4.1，重点提升工业级长文档处理与 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越狱技术全景：从 Prompt 注入到多轮诱导</title><link>https://guijiagi.com/posts/jailbreak-techniques-2026/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/jailbreak-techniques-2026/</guid><description>系统梳理 2024-2026 年 LLM 越狱攻击的主流范式，包含详细的技术分类、代码示例与防御策略对比。</description></item><item><title>2026 大模型基准测试横评：GPT-5.5 vs Claude 4 vs Gemini 2.5 vs DeepSeek V4</title><link>https://guijiagi.com/posts/llm-benchmark-2026/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-benchmark-2026/</guid><description>全面对比 2026 年主流大语言模型在 MMLU、GPQA、HumanEval、MATH 等基准测试中的表现，附实战性能分析与选型建议。</description></item><item><title>Advanced Prompt Techniques：进阶提示工程技术与实战</title><link>https://guijiagi.com/posts/advanced-prompt-techniques/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/advanced-prompt-techniques/</guid><description>深度探讨提示工程中的高阶技术，包括 Expert Prompting、Contrastive、Meta Prompting 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+0800</pubDate><guid>https://guijiagi.com/posts/dify-vs-fastgpt-vs-ragflow/</guid><description>深度对比 Dify、FastGPT、RagFlow 三大开源 AI 应用平台，从架构设计、RAG 能力、工作流编排到部署体验的全方位评测。</description></item><item><title>Embedding 模型选型指南：bge vs e5 vs OpenAI vs Cohere</title><link>https://guijiagi.com/posts/embedding-model-guide/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/embedding-model-guide/</guid><description>深度对比 2026 年主流 Embedding 模型：BGE-M3、E5-Mistral、OpenAI text-embedding-3、Cohere embed-v4 在 RAG、语义搜索、聚类等场景的表现与选型建议。</description></item><item><title>Few-shot Prompt Engineering：示例驱动的高效 Prompt 设计</title><link>https://guijiagi.com/posts/few-shot-prompt-engineering/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/few-shot-prompt-engineering/</guid><description>深入探索 Few-shot 提示工程，掌握通过示例驱动大模型行为的技术原理和最佳实践。</description></item><item><title>Flash Attention 3 原理：GPU 内存层次的最优利用</title><link>https://guijiagi.com/posts/flash-attention-3/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/flash-attention-3/</guid><description>深度解析 Flash Attention 3 如何通过异步执行、warp 专用化和 FP8 支持，在 H100 GPU 上实现比 Flash Attention 2 快 1.5-2 倍的性能。</description></item><item><title>Gemini Deep Research：Google 的自主研究智能体</title><link>https://guijiagi.com/posts/gemini-deep-research-2026/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/gemini-deep-research-2026/</guid><description>全面解析 Google Gemini Deep Research 功能的技术架构、工作流程和实际应用效果。</description></item><item><title>GraphRAG 解析：知识图谱增强的检索增强生成</title><link>https://guijiagi.com/posts/graph-rag-explained/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/graph-rag-explained/</guid><description>深入解析 GraphRAG 的完整架构：实体抽取、关系构建、社区检测、层级摘要与多跳检索，对比传统 RAG 的优劣，附 Neo4j + LLM 的完整代码实现。</description></item><item><title>LangChain vs 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API 费用的方法</title><link>https://guijiagi.com/posts/llm-cost-optimization/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-cost-optimization/</guid><description>从模型选择、Prompt 精简、缓存策略到流量路由，系统介绍 10 种可落地的 LLM API 成本优化方法，附带代码示例与量化对比。</description></item><item><title>LLM 创意评估方法：从主观评分到自动化指标</title><link>https://guijiagi.com/posts/llm-creative-eval/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-creative-eval/</guid><description>系统介绍 LLM 创意写作能力评估方法，涵盖人工评分体系、自动化创意指标、LLM-as-Judge 方法及多维创意评估框架。</description></item><item><title>LLM 缓存策略：语义缓存与多级缓存架构</title><link>https://guijiagi.com/posts/llm-caching-strategy/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-caching-strategy/</guid><description>系统解析 LLM 应用的缓存策略，涵盖语义缓存、多级缓存架构与性能优化实践</description></item><item><title>LLM 幻觉测量方法：从人工标注到自动检测</title><link>https://guijiagi.com/posts/hallucination-measurement/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/hallucination-measurement/</guid><description>系统介绍 LLM 幻觉（Hallucination）的测量与检测方法，涵盖幻觉分类、人工标注体系、自动检测算法与评估框架。</description></item><item><title>LLM 评估流水线搭建：从数据集到报告</title><link>https://guijiagi.com/posts/llm-eval-pipeline/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-eval-pipeline/</guid><description>系统讲解 LLM 评估流水线的完整构建方法，涵盖数据集构建、评估指标选择、自动化执行、结果分析与可视化报告生成。</description></item><item><title>LLM 上下文长度扩展：从 YARN 到 NTK-aware 插值</title><link>https://guijiagi.com/posts/llm-context-length/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-context-length/</guid><description>系统梳理大语言模型上下文窗口扩展的技术方案，涵盖位置插值、NTK-aware、YaRN、LongRoPE 等方法及其工程实现。</description></item><item><title>LLM 水印技术：AI 生成内容的溯源方案</title><link>https://guijiagi.com/posts/llm-watermarking/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-watermarking/</guid><description>深入解析 LLM 文本水印的技术原理、主流方案对比、检测方法及抗攻击性分析。</description></item><item><title>LLM 网关设计：统一接入层与多模型路由</title><link>https://guijiagi.com/posts/llm-gateway-design/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-gateway-design/</guid><description>系统化讲解 LLM 网关的架构设计，涵盖多模型路由、负载均衡、限流降级与成本控制</description></item><item><title>LLM 知识蒸馏：从大模型到小模型的能力迁移</title><link>https://guijiagi.com/posts/llm-knowledge-distillation/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-knowledge-distillation/</guid><description>深入讲解 LLM 知识蒸馏的完整体系：黑盒蒸馏、白盒蒸馏、在线/离线蒸馏，涵盖 Logit 蒸馏、中间层蒸馏、指令蒸馏、响应蒸馏等方法，附 PyTorch 代码实现与效果对比。</description></item><item><title>LoRA vs DoRA vs QLoRA：参数高效微调三剑客对比</title><link>https://guijiagi.com/posts/lora-vs-dora-vs-qlora/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-vs-dora-vs-qlora/</guid><description>深入对比 LoRA、DoRA、QLoRA 三种参数高效微调方法的原理、数学公式、代码实现与效果差异，给出不同场景下的最佳选择建议和完整的实验数据对比。</description></item><item><title>Microsoft Copilot Studio：企业级 Agent 构建平台</title><link>https://guijiagi.com/posts/copilot-studio-enterprise/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/copilot-studio-enterprise/</guid><description>深入剖析 Microsoft Copilot Studio 的架构设计、开发流程和企业级部署最佳实践。</description></item><item><title>Mixture of Depths：让 Transformer 学会跳过冗余层</title><link>https://guijiagi.com/posts/mixture-of-depths/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/mixture-of-depths/</guid><description>深度解析 Mixture of Depths (MoD) 如何通过动态跳过冗余 Transformer 层，在保持性能的同时大幅提升推理效率。</description></item><item><title>MoE 混合专家架构深度解析：从稀疏激活到专家路由</title><link>https://guijiagi.com/posts/moe-architecture-deep/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/moe-architecture-deep/</guid><description>深入剖析 Mixture of Experts 架构的原理、路由机制、训练策略及工程实现，涵盖 GShard、Switch Transformer、Mixtral 等代表性工作。</description></item><item><title>Ollama 生态全景：本地大模型运行的最佳实践</title><link>https://guijiagi.com/posts/ollama-ecosystem/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ollama-ecosystem/</guid><description>深入解析 Ollama 全链路生态：从安装配置、模型管理、API 调用到高级集成，手把手教你打造私有化大模型部署方案。</description></item><item><title>Open WebUI 部署：打造自己的 ChatGPT 界面</title><link>https://guijiagi.com/posts/open-webui-deploy/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/open-webui-deploy/</guid><description>从安装部署到高级配置，手把手教你用 Open WebUI 搭建功能完备的私有化 AI 对话平台，支持多模型、RAG、多用户管理。</description></item><item><title>OpenAI IPO 估值 9650 亿：GPT-5.5 时代的资本博弈</title><link>https://guijiagi.com/posts/openai-gpt55-ipo/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/openai-gpt55-ipo/</guid><description>OpenAI 启动 IPO 流程，估值 9650 亿美元，GPT-5.5 同步发布。深度解析资本结构、技术路线与市场影响。</description></item><item><title>OpenAI 兼容 API 生态：统一接入层的标准之争</title><link>https://guijiagi.com/posts/openai-compatible-api/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/openai-compatible-api/</guid><description>全面解析 OpenAI 兼容 API 生态：从标准规范到主流实现方案对比，帮助企业构建统一的 LLM 接入层。</description></item><item><title>Output Control &amp; Formatting：精确控制 AI 输出的全面指南</title><link>https://guijiagi.com/posts/output-control-and-formatting/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/output-control-and-formatting/</guid><description>系统掌握大模型输出控制技术，从格式约束到结构化输出，实现 AI 输出的精确管控。</description></item><item><title>Prompt Decomposition：复杂任务的解构艺术</title><link>https://guijiagi.com/posts/prompt-decomposition/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-decomposition/</guid><description>掌握 Prompt 分解技术，将复杂任务拆解为可管理的子任务，提升 AI 输出的质量和可靠性。</description></item><item><title>Prompt Rules &amp; Knowledge：规则约束与知识注入的艺术</title><link>https://guijiagi.com/posts/prompt-rules-and-knowledge/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-rules-and-knowledge/</guid><description>系统性地掌握 Prompt 中规则约束和领域知识注入的技术，构建可靠可控的 AI 应用。</description></item><item><title>Prompt 工程化生产实践：版本管理与 A/B 测试</title><link>https://guijiagi.com/posts/prompt-engineering-production/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-engineering-production/</guid><description>将 Prompt 从手工艺品变为工程产物：版本控制、灰度发布、A/B 测试、回归评测的完整生产实践。</description></item><item><title>Prompt 模板设计：构建可复用的工业级提示模板</title><link>https://guijiagi.com/posts/prompt-templates-design/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-templates-design/</guid><description>系统讲解 Prompt 模板的设计原则、最佳实践和工业级可复用模板系统构建方案。</description></item><item><title>QLoRA 微调实战：4bit 量化下的高效训练</title><link>https://guijiagi.com/posts/fine-tuning-qlora-guide/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/fine-tuning-qlora-guide/</guid><description>深入解析 QLoRA 的 NF4 量化、双重量化、分页优化器原理，提供完整的 LLaMA-3 QLoRA 微调代码实战，涵盖数据准备、训练配置、显存优化技巧与常见问题排查。</description></item><item><title>RAG 生产环境 12 大坑及解决方案</title><link>https://guijiagi.com/posts/rag-production-pitfalls/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-production-pitfalls/</guid><description>从向量检索失效到多跳推理失败，总结 RAG 系统在生产环境中常见的 12 个陷阱及其工程解决方案。</description></item><item><title>RAG 重排序指南：Cohere Rerank vs bge-reranker vs Cross-Encoder</title><link>https://guijiagi.com/posts/rag-reranking-guide/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-reranking-guide/</guid><description>深入对比三种主流 RAG 重排序方案：Cohere Rerank API、BAAI bge-reranker、自训练 Cross-Encoder，涵盖原理、部署方式、代码实现、延迟测试与效果对比，给出不同场景的最佳选择。</description></item><item><title>Ring Attention：突破百万 Token 上下文的分布式注意力</title><link>https://guijiagi.com/posts/ring-attention-long-context/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ring-attention-long-context/</guid><description>深度解析 Ring Attention 如何通过环形通信将注意力计算分布到多 GPU，实现百万级 token 上下文窗口。</description></item><item><title>RLHF vs DPO vs GRPO：三种对齐算法深度对比</title><link>https://guijiagi.com/posts/rlhf-dpo-grpo/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rlhf-dpo-grpo/</guid><description>从 RLHF 的 PPO 到 DPO 的无奖励模型，再到 GRPO 的群组相对策略优化，深度解析三种主流 LLM 对齐算法的原理与优劣。</description></item><item><title>Role Playing &amp; Persona Design：角色扮演与人格设计的 Prompt 艺术</title><link>https://guijiagi.com/posts/role-playing-and-persona-design/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/role-playing-and-persona-design/</guid><description>深入探索 LLM 角色扮演技术，系统掌握人设 Prompt 设计方法，打造个性化的 AI 角色体验。</description></item><item><title>SGLang 探理引擎：结构化生成的高性能方案</title><link>https://guijiagi.com/posts/sglang-inference/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/sglang-inference/</guid><description>深入解析 SGLang 推理引擎的核心技术原理、性能优势与生产部署实践，涵盖 RadixAttention、结构化生成、并发处理等关键特性。</description></item><item><title>Tokenizer 全面对比：BPE vs WordPiece vs Unigram vs SentencePiece</title><link>https://guijiagi.com/posts/tokenizer-comparison/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/tokenizer-comparison/</guid><description>深入对比四种主流分词算法的原理、训练方法、优缺点及工程实现，涵盖 BPE、WordPiece、Unigram Language Model 和 SentencePiece。</description></item><item><title>vLLM 生产部署指南：高吞吐推理引擎</title><link>https://guijiagi.com/posts/vllm-production-guide/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/vllm-production-guide/</guid><description>从架构原理到生产部署，全面解析 vLLM 高吞吐推理引擎的核心技术、性能调优与运维方案。</description></item><item><title>本地 LLM 部署选型：Ollama vs vLLM vs LM Studio vs TGI</title><link>https://guijiagi.com/posts/local-llm-deployment/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/local-llm-deployment/</guid><description>全面对比 2026 年四大本地 LLM 部署方案：Ollama、vLLM、LM Studio、TGI 的性能、易用性、并发能力与生产环境适用性分析。</description></item><item><title>超越 Transformer：Mamba/SSM/RWKV 架构深度对比</title><link>https://guijiagi.com/posts/transformer-alternatives-2026/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/transformer-alternatives-2026/</guid><description>深度解析 Mamba、S4/S6、RWKV 等替代 Transformer 的新一代架构，从状态空间模型到线性注意力，全面对比优劣。</description></item><item><title>持续预训练实践：领域大模型的训练方法论</title><link>https://guijiagi.com/posts/continuous-pretraining/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/continuous-pretraining/</guid><description>系统讲解大模型持续预训练（CPT）的完整方法论：领域数据构建、训练策略、灾难性遗忘缓解、学习率调度、数据混合比例、评估体系，附完整训练代码与企业级实践指南。</description></item><item><title>大模型量化技术全景：从 INT8 到 GPTQ 与 AWQ</title><link>https://guijiagi.com/posts/quantization-techniques/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/quantization-techniques/</guid><description>系统梳理大语言模型量化技术，涵盖 PTQ 与 QAT、对称与非对称量化、GPTQ、AWQ、SmoothQuant 等方法的原理与实现。</description></item><item><title>大模型训练稳定性：梯度爆炸、Loss Spike 与恢复策略</title><link>https://guijiagi.com/posts/training-stability/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/training-stability/</guid><description>深入分析大语言模型训练中的稳定性问题，涵盖梯度爆炸/消失、Loss Spike、数值不稳定等原因诊断与解决方案。</description></item><item><title>代码大模型横评：Codex vs Claude Code vs DeepSeek-Coder vs Qwen-Coder</title><link>https://guijiagi.com/posts/code-model-comparison/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/code-model-comparison/</guid><description>深度横评 2026 年四大代码大模型：GPT-5.5-Code、Claude Code、DeepSeek-Coder-V3、Qwen2.5-Coder 在代码生成、调试、重构、代码审查等场景的实战表现。</description></item><item><title>多模态大模型选型：GPT-5V vs Gemini vs Qwen-VL vs LLaVA</title><link>https://guijiagi.com/posts/multimodal-model-selection/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/multimodal-model-selection/</guid><description>全面对比 2026 年主流多模态大模型：GPT-5o、Gemini 2.0 Pro、LLaVA-1.6、Qwen-VL2、InternVL3 在图像、视频、文档理解上的表现与选型建议。</description></item><item><title>多模态模型评估：视觉理解与跨模态推理</title><link>https://guijiagi.com/posts/multimodal-eval-method/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/multimodal-eval-method/</guid><description>系统介绍多模态大模型评估方法，涵盖视觉理解、跨模态推理、图文一致性、OCR、视频理解等核心维度的评估框架与实现。</description></item><item><title>多智能体编排架构：从中心化到去中心化的设计模式</title><link>https://guijiagi.com/posts/multi-agent-orchestration/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/multi-agent-orchestration/</guid><description>深入探讨多智能体系统的编排架构，涵盖中心化、去中心化与混合模式的设计模式、通信协议与工程实践</description></item><item><title>高级 RAG 模式：HyDE、CRAG、Self-RAG 与 FLARE</title><link>https://guijiagi.com/posts/advanced-rag-patterns/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/advanced-rag-patterns/</guid><description>深入解析四种高级 RAG 模式：HyDE 假设文档嵌入、CRAG 纠正性检索增强生成、Self-RAG 自反思检索增强生成、FLARE 主动检索增强生成，附完整代码实现与性能对比。</description></item><item><title>开源 vs 闭源大模型：2026 终局之战</title><link>https://guijiagi.com/posts/open-source-vs-closed/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/open-source-vs-closed/</guid><description>2026 年开源与闭源大模型的全面对比，从性能追赶到生态之争，谁将赢得 AI 的未来</description></item><item><title>连续批处理：vLLM 高吞吐推理的核心技术</title><link>https://guijiagi.com/posts/continuous-batching/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/continuous-batching/</guid><description>深度解析 vLLM 连续批处理（Continuous Batching）如何通过动态请求调度和 PagedAttention 实现高吞吐 LLM 推理服务。</description></item><item><title>美国拟国有化头部 AI：产业政策大转向</title><link>https://guijiagi.com/posts/us-ai-nationalization/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/us-ai-nationalization/</guid><description>美国国会讨论 AI 国家安全法案，拟对头部 AI 公司实施国有化管理，引发科技界激烈辩论。</description></item><item><title>欧盟 AI Act 落地：对全球 AI 产业的连锁影响</title><link>https://guijiagi.com/posts/eu-ai-act-impact/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/eu-ai-act-impact/</guid><description>欧盟 AI 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+0800</pubDate><guid>https://guijiagi.com/posts/flash-attention-3-guide/</guid><description>全面解析 Flash Attention 1/2/3 的演进、IO 复杂度分析及性能数据</description></item><item><title>Function Calling 生产实践：从 Demo 到可靠工具调用</title><link>https://guijiagi.com/posts/function-calling-production/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/function-calling-production/</guid><description>Function Calling 在生产环境中的完整实践指南，覆盖 Schema 设计、参数验证、错误恢复、并行调用与安全沙箱</description></item><item><title>Gemini 3.5 Flash 评测：低延迟 Agent 时代到来</title><link>https://guijiagi.com/posts/gemini-3-5-flash-review/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/gemini-3-5-flash-review/</guid><description>Google 发布 Gemini 3.5 Flash，首 token 延迟 280ms，原生多模态生成，深度集成搜索，Agent 场景全面覆盖</description></item><item><title>Gemini Deep Research 评测：Google 的 AI 研究助手</title><link>https://guijiagi.com/posts/gemini-deep-research-review/</link><pubDate>Thu, 25 Jun 2026 10:00:00 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+0800</pubDate><guid>https://guijiagi.com/posts/ai-observability-guide/</guid><description>从 Tracing 到 Metrics 的 AI 应用可观测性完整方案，含工具选型与实现</description></item><item><title>AI 行业报告解读：McKinsey/Gartner/IDC 怎么说</title><link>https://guijiagi.com/posts/ai-industry-report-2026/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-industry-report-2026/</guid><description>McKinsey AI状态报告、Gartner Hype Cycle、IDC预测、Stanford AI Index四大权威报告深度解读</description></item><item><title>AI 学术突破 2026：值得关注的论文</title><link>https://guijiagi.com/posts/ai-academic-breakthroughs/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-academic-breakthroughs/</guid><description>Scaling Law新发现、推理能力突破、多模态进展、效率优化与对齐研究的前沿论文解读</description></item><item><title>Anthropic 2026 动态：Claude 4 后的布局</title><link>https://guijiagi.com/posts/anthropic-news-2026/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/anthropic-news-2026/</guid><description>Claude 4系列、MCP生态、企业客户、安全研究、融资与估值全方位解读Anthropic 2026年战略</description></item><item><title>DeepSeek 模型家族选择指南</title><link>https://guijiagi.com/posts/deepseek-model-guide/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/deepseek-model-guide/</guid><description>深入解析 DeepSeek-V3 与 R1 的架构设计、性能表现、成本分析与适用场景。</description></item><item><title>GLM 系列模型选择指南：智谱的模型矩阵</title><link>https://guijiagi.com/posts/glm-model-guide/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/glm-model-guide/</guid><description>解析智谱 GLM-4 系列模型架构、视觉能力、代码模型、API 使用与部署方案。</description></item><item><title>Google Gemma 模型指南：轻量级开源选择</title><link>https://guijiagi.com/posts/gemma-model-guide/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/gemma-model-guide/</guid><description>全面解析 Google Gemma 2/3 系列架构、规格、多模态支持与部署方案。</description></item><item><title>KV Cache 原理与优化：LLM 推理加速核心</title><link>https://guijiagi.com/posts/kv-cache-guide/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/kv-cache-guide/</guid><description>深入解析 KV Cache 的工作原理、内存计算与优化技术，包括 PagedAttention、量化 KV Cache 等</description></item><item><title>Llama 系列模型演进史：从 Llama 1 到 Llama 4</title><link>https://guijiagi.com/posts/llama-model-evolution/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llama-model-evolution/</guid><description>回顾 Meta Llama 系列四代模型的架构演进、许可证变化、社区生态与部署建议。</description></item><item><title>LLM 流式输出实现：SSE 与 WebSocket</title><link>https://guijiagi.com/posts/llm-streaming-implementation/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-streaming-implementation/</guid><description>SSE 与 WebSocket 两种流式输出方案的完整实现，含前端渲染与断线重连</description></item><item><title>Mistral 模型家族指南：欧洲的 AI 雄心</title><link>https://guijiagi.com/posts/mistral-model-guide/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/mistral-model-guide/</guid><description>解析 Mistral AI 的模型矩阵：从 Mistral 7B 到 Mixtral MoE，再到 Codestral 与 Mathstral。</description></item><item><title>OpenAI 2026 年六月动态：GPT-5 后的时代</title><link>https://guijiagi.com/posts/openai-news-jun2026/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/openai-news-jun2026/</guid><description>GPT-5后续更新、Operator进展、开发者工具、定价调整、组织变化全面追踪</description></item><item><title>Prompt 管理平台搭建指南</title><link>https://guijiagi.com/posts/prompt-management-platform/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-management-platform/</guid><description>从需求分析到架构设计，构建企业级 Prompt 管理与版本控制平台</description></item><item><title>Qwen3 系列模型选择指南：从 0.6B 到 235B</title><link>https://guijiagi.com/posts/qwen3-model-selection/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/qwen3-model-selection/</guid><description>全面解析 Qwen3 系列模型的架构、规格、性能与部署策略，帮助开发者选择最适合的通义千问模型。</description></item><item><title>RAG 系统生产部署全流程</title><link>https://guijiagi.com/posts/rag-production-deploy/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-production-deploy/</guid><description>从架构设计到容器化部署，完整的 RAG 生产环境落地指南</description></item><item><title>Transformer 架构深度解析：从 Attention 到 GPT</title><link>https://guijiagi.com/posts/transformer-architecture-deep/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/transformer-architecture-deep/</guid><description>深入剖析 Transformer 架构的每个组件，从自注意力机制到 GPT 解码器-only 架构的演进</description></item><item><title>代码模型横评：谁是最强编程助手？</title><link>https://guijiagi.com/posts/coding-model-comparison/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/coding-model-comparison/</guid><description>横向对比 DeepSeek-Coder-V2、Codestral、Qwen2.5-Coder、CodeGeeX-4、StarCoder2 等主流代码模型，包含基准测试与实战评测。</description></item><item><title>多语言 LLM 部署指南</title><link>https://guijiagi.com/posts/multi-language-llm-deploy/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/multi-language-llm-deploy/</guid><description>从语言检测到跨语言检索的完整多语言 LLM 工程化方案</description></item><item><title>分词原理与工程实践：BPE/SentencePiece/WordPiece</title><link>https://guijiagi.com/posts/tokenization-guide/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/tokenization-guide/</guid><description>从字符级到子词级分词，深入解析 BPE、WordPiece、SentencePiece 算法及工程实践</description></item><item><title>高级 RAG 架构模式：超越简单检索</title><link>https://guijiagi.com/posts/rag-architecture-advanced/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-architecture-advanced/</guid><description>多跳检索、自适应检索、Self-RAG 等高级 RAG 架构模式与选型决策</description></item><item><title>视觉语言模型选择指南：从 LLaVA 到 GPT-4V</title><link>https://guijiagi.com/posts/vision-model-selection/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/vision-model-selection/</guid><description>全面解析 VLM 架构演进、主流模型对比、OCR 与视频理解能力、部署成本与选型决策。</description></item><item><title>推测解码加速原理：Draft Model 验证范式</title><link>https://guijiagi.com/posts/speculative-decoding/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/speculative-decoding/</guid><description>深入解析推测解码如何通过 Draft Model 并行验证加速 LLM 自回归推理</description></item><item><title>中国 AI 芯片最新进展：昇腾、寒武纪、摩尔线程</title><link>https://guijiagi.com/posts/china-ai-chip-update/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/china-ai-chip-update/</guid><description>华为昇腾910B/910C、寒武纪思元、摩尔线程MTT S80等国产AI芯片最新进展与出口管制影响深度分析</description></item><item><title>注意力机制详解：从 Softmax 到 Flash Attention</title><link>https://guijiagi.com/posts/attention-mechanism-guide/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/attention-mechanism-guide/</guid><description>全面解析注意力机制的数学原理与工程优化，从基础公式到 Flash Attention 的 IO 感知设计</description></item><item><title>Agent 基准测试方法论：从设计到执行</title><link>https://guijiagi.com/posts/agent-benchmark-methodology/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-benchmark-methodology/</guid><description>系统讲解 Agent 基准测试的设计原则、测试集构建、评估指标体系及主流基准对比</description></item><item><title>Agent 评估框架横向对比：谁在衡量 Agent 的能力？</title><link>https://guijiagi.com/posts/agent-eval-comparison/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-eval-comparison/</guid><description>横向对比 AgentBench、SWE-bench、tau-bench、WebArena 等主流 Agent 评估框架，分析评估维度、数据集规模与适用场景</description></item><item><title>Agent 系统可扩展性设计：从单机到分布式</title><link>https://guijiagi.com/posts/agent-scalability/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-scalability/</guid><description>系统讲解 AI Agent 系统的可扩展性设计，涵盖水平扩展、状态外部化、无状态 Agent、负载均衡与分布式追踪</description></item><item><title>AGI 时间线：2030 前景展望</title><link>https://guijiagi.com/posts/agi-timeline-2030/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agi-timeline-2030/</guid><description>从 AGI 定义争议到当前里程碑、Scaling Law 争论、多模态融合与具身智能，系统分析 2030 年前 AGI 发展的可能路径。</description></item><item><title>AI 安全标准与合规：从 EU AI Act 到中国算法备案</title><link>https://guijiagi.com/posts/ai-safety-standards/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-safety-standards/</guid><description>系统梳理全球 AI 安全监管框架，涵盖 EU AI Act、中国生成式 AI 管理办法、美国行政令、ISO 42001、NIST AI RMF 及企业合规实践</description></item><item><title>AI 经济学：未来工作与财富分配</title><link>https://guijiagi.com/posts/ai-economy-future/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-economy-future/</guid><description>从自动化替代预测到生产力跃升、新职业诞生、UBI 讨论与财富集中风险，系统分析 AI 对经济结构的深层影响。</description></item><item><title>AI 客服系统构建指南：从知识库到多轮对话</title><link>https://guijiagi.com/posts/ai-customer-service-build/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-customer-service-build/</guid><description>完整指南：构建生产级 AI 客服系统，涵盖需求分析、知识库构建、意图识别、多轮对话管理、人工转接与满意度评估</description></item><item><title>AI 意识争论：机器能思考吗？</title><link>https://guijiagi.com/posts/ai-consciousness-debate/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-consciousness-debate/</guid><description>从图灵测试到中文屋，从功能主义到 IIT 理论，系统梳理 AI 意识争论的核心命题与当代 LLM 语境下的新思考。</description></item><item><title>AI 治理框架思考：在创新与安全之间寻找平衡</title><link>https://guijiagi.com/posts/ai-governance-framework/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-governance-framework/</guid><description>从治理三难到分级监管、国际协调、开源治理与企业自治，系统构建 AI 治理框架的思考与实践路径。</description></item><item><title>AutoGen 多智能体框架评测：微软的 Agent 雄心</title><link>https://guijiagi.com/posts/autogen-multi-agent-review/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/autogen-multi-agent-review/</guid><description>AutoGen v0.4 新架构解析、多 Agent 对话模式、GroupChat、代码执行、与 LangGraph/CrewAI 横向对比</description></item><item><title>FastChat 多模型对话平台部署实战</title><link>https://guijiagi.com/posts/fastchat-deployment/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/fastchat-deployment/</guid><description>FastChat 架构解析、多模型管理、Gradio Web UI、API 服务、分布式部署实战</description></item><item><title>Few-shot Prompt 设计艺术：示例即编程</title><link>https://guijiagi.com/posts/few-shot-prompt-design/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/few-shot-prompt-design/</guid><description>系统讲解 Few-shot Prompt 设计的原理、示例选择策略、顺序效应、格式设计与 Fine-tune 的边界判断。</description></item><item><title>Haystack 框架评测：deepset 的 RAG 之选</title><link>https://guijiagi.com/posts/haystack-review/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/haystack-review/</guid><description>深度评测 deepset Haystack 框架，涵盖 Pipeline 架构、Node 体系、RAG 最佳实践及生产部署经验</description></item><item><title>LiteLLM 多模型代理部署：统一管理所有 LLM API</title><link>https://guijiagi.com/posts/litellm-proxy-guide/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/litellm-proxy-guide/</guid><description>LiteLLM 代理完整部署指南：统一 API 格式、负载均衡、成本追踪、缓存、限流配置</description></item><item><title>LlamaIndex 框架评测：RAG 领域的瑞士军刀</title><link>https://guijiagi.com/posts/llama-index-review/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llama-index-review/</guid><description>深度评测 LlamaIndex 框架，涵盖核心架构、索引类型、查询引擎、Agent 模式及与 LangChain 的对比</description></item><item><title>LLM 红队测试实践：攻击即防御</title><link>https://guijiagi.com/posts/red-teaming-llm/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/red-teaming-llm/</guid><description>系统性介绍 LLM 红队测试方法论，涵盖攻击向量分析、自动化工具链、修复流程与真实案例</description></item><item><title>LoRA/QLoRA 高效微调实践：单卡训练大模型</title><link>https://guijiagi.com/posts/lora-qlora-practice/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/lora-qlora-practice/</guid><description>深入讲解 LoRA 低秩分解原理、QLoRA 量化、PEFT 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系统评估指南：从检索到生成的全链路评测</title><link>https://guijiagi.com/posts/rag-evaluation-guide/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-evaluation-guide/</guid><description>系统介绍 RAG 评估维度、RAGAS 框架、核心指标及自动化评估工具实践</description></item><item><title>SGLang 掐理引擎指南：超越 vLLM 的新选择</title><link>https://guijiagi.com/posts/sglang-inference-engine/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/sglang-inference-engine/</guid><description>SGLang 原理详解、与 vLLM/TensorRT-LLM 性能对比、安装使用、适用场景分析</description></item><item><title>聊天机器人生产部署全指南</title><link>https://guijiagi.com/posts/chatbot-production-deploy/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/chatbot-production-deploy/</guid><description>从 Docker 容器化到 Nginx 反向代理、SSL 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架构设计：低延迟智能体</title><link>https://guijiagi.com/posts/realtime-agent-architecture/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/realtime-agent-architecture/</guid><description>探讨实时 Agent 架构设计，涵盖 WebSocket/SSE 流式输出、流式推理、管道并行、延迟优化与边缘部署策略</description></item><item><title>思维链 Prompt 工程指南：让 LLM 学会一步步思考</title><link>https://guijiagi.com/posts/chain-of-thought-guide/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/chain-of-thought-guide/</guid><description>深入解析思维链（Chain-of-Thought）Prompt 工程技术，涵盖 Zero-shot CoT、Few-shot CoT、Self-Consistency、Tree-of-Thought 与 Graph-of-Thought 的原理、对比与实战。</description></item><item><title>微调 vs Prompt 工程：何时该选哪个？</title><link>https://guijiagi.com/posts/fine-tuning-vs-prompt/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/fine-tuning-vs-prompt/</guid><description>系统对比 Prompt 工程与微调的适用场景、成本、效果，提供决策矩阵和混合方案</description></item><item><title>主流 LLM 排行榜深度分析：谁是真正的 No.1？</title><link>https://guijiagi.com/posts/llm-leaderboard-analysis/</link><pubDate>Wed, 24 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-leaderboard-analysis/</guid><description>对比 LMSYS、OpenCompass、HELM、SuperCLUE 等主流排行榜的机制差异，教你如何选择参考榜单</description></item><item><title>Microsoft Copilot 生态：从 Office 到 Windows 的 AI 无处不在</title><link>https://guijiagi.com/posts/microsoft-copilot-review/</link><pubDate>Wed, 24 Jun 2026 13:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/microsoft-copilot-review/</guid><description>微软 Copilot 生态全景：M365 Copilot、GitHub Copilot、Windows Copilot 的深度评测</description></item><item><title>CrewAI 多智能体框架实战：角色扮演与团队协作</title><link>https://guijiagi.com/posts/crewai-multi-agent-review/</link><pubDate>Wed, 24 Jun 2026 12:50:00 +0800</pubDate><guid>https://guijiagi.com/posts/crewai-multi-agent-review/</guid><description>CrewAI 框架的设计理念、核心概念和生产环境实践</description></item><item><title>AutoGPT 与 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Use</title><link>https://guijiagi.com/posts/claude-agents-review/</link><pubDate>Wed, 24 Jun 2026 12:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/claude-agents-review/</guid><description>Anthropic Claude 智能体生态全解析：MCP 协议、Computer Use、Artifacts 的能力与实践</description></item><item><title>OpenAI 智能体深度评测：从 GPT Store 到 Operator</title><link>https://guijiagi.com/posts/openai-agents-review/</link><pubDate>Wed, 24 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/openai-agents-review/</guid><description>OpenAI 智能体生态全解析：GPTs、Assistant API、Operator 的能力边界与适用场景</description></item><item><title>AI 对齐技术全景：从 RLHF 到 Constitutional AI</title><link>https://guijiagi.com/posts/ai-alignment-techniques/</link><pubDate>Wed, 24 Jun 2026 11:50:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-alignment-techniques/</guid><description>系统梳理 AI 对齐的技术路径、方法对比和实践选择</description></item><item><title>DeepSeek 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&lt;p>🌐 &lt;strong>本站三个域名（均为同一网站）&lt;/strong>：&lt;/p>
&lt;ul>
&lt;li>&lt;strong>&lt;a href="https://guijiagi.com">guijiagi.com&lt;/a>&lt;/strong> — 硅基 AGI&lt;/li>
&lt;li>&lt;strong>&lt;a href="https://ziyuanagi.com">ziyuanagi.com&lt;/a>&lt;/strong> — 字元科技&lt;/li>
&lt;li>&lt;strong>&lt;a href="https://siliconagi.cn">siliconagi.cn&lt;/a>&lt;/strong> — 智能体中国&lt;/li>
&lt;/ul>
&lt;p>🔔 其中 &lt;strong>ziyuanagi.com（字元科技）&lt;/strong> 和 &lt;strong>siliconagi.cn（智能体中国）&lt;/strong> 两个域名诚意转让，如有意向请联系邮箱。&lt;/p>&lt;/blockquote>
&lt;blockquote>
&lt;p>🔗 &lt;strong>&lt;a href="https://silicon-agi.com">智能体海外论坛 · silicon-agi.com&lt;/a>&lt;/strong> — AGI 技术交流论坛，独立运营。&lt;/p>&lt;/blockquote>
&lt;blockquote>
&lt;p>🌐 &lt;strong>多域名访问&lt;/strong>：&lt;a href="https://guijiagi.com">guijiagi.com&lt;/a> · &lt;a href="https://ziyuanagi.com">ziyuanagi.com&lt;/a>（字元科技） · &lt;a href="https://siliconagi.cn">siliconagi.cn&lt;/a>&lt;/p>&lt;/blockquote>
&lt;h3 id="-为什么做这个博客">🎯 为什么做这个博客？&lt;/h3>
&lt;p>AI 技术日新月异，从 ChatGPT 到 Agent 框架，从 Function Calling 到 MCP 协议，每一次迭代都在拓宽智能的边界。这个博客记录我在 AGI 智能体学习和测评过程中的思考与实践。&lt;/p>
&lt;p>我们正处于一个特殊的历史节点——&lt;strong>硅基智能正在从「工具」进化为「伙伴」&lt;/strong>。理解这个进化过程，就是理解人类文明的下一个纪元。&lt;/p>
&lt;h3 id="-内容方向">📚 内容方向&lt;/h3>
&lt;p>本站涵盖 &lt;strong>17 大板块&lt;/strong>，聚焦 Agent 框架、工具调用、多智能体协作、记忆系统、测评方法等核心话题，持续更新中。&lt;/p>
&lt;table>
&lt;thead>
&lt;tr>
&lt;th>板块&lt;/th>
&lt;th>核心话题&lt;/th>
&lt;/tr>
&lt;/thead>
&lt;tbody>
&lt;tr>
&lt;td>&lt;strong>行业快报&lt;/strong>&lt;/td>
&lt;td>AI 产业动态、融资并购、政策法规&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>技术原理&lt;/strong>&lt;/td>
&lt;td>Transformer、注意力机制、位置编码等核心架构拆解&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>实践指南&lt;/strong>&lt;/td>
&lt;td>LLM 选型、部署方案、开发技巧、生产实践&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>Prompt工程&lt;/strong>&lt;/td>
&lt;td>提示词设计、思维链、结构化输出、版本管理&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>架构设计&lt;/strong>&lt;/td>
&lt;td>多智能体协作、记忆架构、编排引擎、系统设计&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>测评方法&lt;/strong>&lt;/td>
&lt;td>Agent 能力评估、Benchmark 分析、评测方法论&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>RAG与微调&lt;/strong>&lt;/td>
&lt;td>检索增强生成、LoRA/QLoRA、数据管线、评估优化&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>模型选型&lt;/strong>&lt;/td>
&lt;td>开源/闭源模型对比、量化压缩、推理优化&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>安全对齐&lt;/strong>&lt;/td>
&lt;td>RLHF/DPO、越狱防御、红队测试、隐私保护&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>开源生态&lt;/strong>&lt;/td>
&lt;td>vLLM、SGLang、Ollama、LangChain 等开源项目&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>主流智能体&lt;/strong>&lt;/td>
&lt;td>OpenAI Agents、Claude、Gemini、Copilot 等&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>框架测评&lt;/strong>&lt;/td>
&lt;td>LangChain、AutoGen、CrewAI、Haystack 深度对比&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>龙虾智能体&lt;/strong>&lt;/td>
&lt;td>OpenClaw 架构、技能系统、记忆系统、部署指南&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>前沿思考&lt;/strong>&lt;/td>
&lt;td>AGI 路线图、意识辩论、后 LLM 时代、技术哲学&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>Codex智能体&lt;/strong>&lt;/td>
&lt;td>Codex CLI、代码审查、测试生成、CI/CD 集成&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>AI视频制作&lt;/strong>&lt;/td>
&lt;td>Sora、Runway、Pika、Kling 等视频生成工具&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>&lt;strong>爱马仕智能体&lt;/strong>&lt;/td>
&lt;td>Nous Hermes 架构、微调、函数调用、安全框架&lt;/td>
&lt;/tr>
&lt;/tbody>
&lt;/table>
&lt;blockquote>
&lt;p>💡 文章数量持续增长，可在&lt;a href="https://guijiagi.com/categories/">板块页&lt;/a>查看各板块最新篇数。&lt;/p></description></item><item><title>Agent 记忆系统 2026：从短期上下文到持久记忆的工程实践</title><link>https://guijiagi.com/posts/agent-%E8%AE%B0%E5%BF%86%E7%B3%BB%E7%BB%9F-2026-%E4%BB%8E%E7%9F%AD%E6%9C%9F%E4%B8%8A%E4%B8%8B%E6%96%87%E5%88%B0%E6%8C%81%E4%B9%85%E8%AE%B0%E5%BF%86%E7%9A%84%E5%B7%A5%E7%A8%8B%E5%AE%9E%E8%B7%B5/</link><pubDate>Sat, 20 Jun 2026 00:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-%E8%AE%B0%E5%BF%86%E7%B3%BB%E7%BB%9F-2026-%E4%BB%8E%E7%9F%AD%E6%9C%9F%E4%B8%8A%E4%B8%8B%E6%96%87%E5%88%B0%E6%8C%81%E4%B9%85%E8%AE%B0%E5%BF%86%E7%9A%84%E5%B7%A5%E7%A8%8B%E5%AE%9E%E8%B7%B5/</guid><description>深度解析 2026 年 Agent 记忆系统的最佳实践，涵盖向量数据库、知识图谱、分层记忆架构与端到端实现方案</description></item><item><title>Prompt 工程实战：从「求 AI」到「指挥 AI」</title><link>https://guijiagi.com/posts/prompt-engineering-practice/</link><pubDate>Fri, 19 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/prompt-engineering-practice/</guid><description>Prompt 不是「求 AI 帮忙」，而是「指挥 AI 干活」。本文用 20 个真实案例拆解 Prompt 工程的核心技巧。</description></item><item><title>主流 Agent 框架深度对比：LangChain vs AutoGen vs CrewAI</title><link>https://guijiagi.com/posts/agent-frameworks-comparison/</link><pubDate>Fri, 19 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-frameworks-comparison/</guid><description>从架构设计、多智能体协作、工具调用、记忆机制等维度，深度对比 LangChain、AutoGen、CrewAI 三大主流 Agent 框架，附选型决策树。</description></item><item><title>MCP vs A2A：AI Agent 通信协议的两大阵营</title><link>https://guijiagi.com/posts/mcp-vs-a2a-ai-agent-%E9%80%9A%E4%BF%A1%E5%8D%8F%E8%AE%AE%E7%9A%84%E4%B8%A4%E5%A4%A7%E9%98%B5%E8%90%A5/</link><pubDate>Fri, 19 Jun 2026 00:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/mcp-vs-a2a-ai-agent-%E9%80%9A%E4%BF%A1%E5%8D%8F%E8%AE%AE%E7%9A%84%E4%B8%A4%E5%A4%A7%E9%98%B5%E8%90%A5/</guid><description>深入对比 Anthropic MCP 与 Google A2A 协议的技术路线、生态系统与应用场景，分析未来 Agent 互操作性的标准之争</description></item><item><title>ReAct 模式：让 AI 学会「思考后再行动」</title><link>https://guijiagi.com/posts/react-pattern/</link><pubDate>Thu, 18 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/react-pattern/</guid><description>ReAct（Reasoning + Acting）是 Agent 最经典的推理范式。本文从原理拆解到代码实现，深入分析 ReAct 的优势、局限和进化方向。</description></item><item><title>GPT-5 深度评测：推理能力的质变时刻</title><link>https://guijiagi.com/posts/gpt5-deep-review/</link><pubDate>Thu, 18 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/gpt5-deep-review/</guid><description>GPT-5 到底强在哪？我们用 50 道推理题、20 个 Agent 任务和 10 个真实场景全面测试了 GPT-5。</description></item><item><title>AI 编程 Agent 2026 横评：Cursor vs GitHub Copilot vs Codex vs Claude Code</title><link>https://guijiagi.com/posts/ai-%E7%BC%96%E7%A8%8B-agent-2026-%E6%A8%AA%E8%AF%84-cursor-vs-github-copilot-vs-codex-vs-claude-code/</link><pubDate>Thu, 18 Jun 2026 00:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-%E7%BC%96%E7%A8%8B-agent-2026-%E6%A8%AA%E8%AF%84-cursor-vs-github-copilot-vs-codex-vs-claude-code/</guid><description>全面对比四大 AI 编程工具的能力边界、适用场景与性价比，基于 2026 年最新版本的实测数据</description></item><item><title>多智能体协作：从「单打独斗」到「团队作战」</title><link>https://guijiagi.com/posts/multi-agent-collaboration/</link><pubDate>Tue, 16 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/multi-agent-collaboration/</guid><description>单个 Agent 能力有限，多个 Agent 协作能解决更复杂的问题。本文探讨多智能体协作的核心模式、通信协议和工程挑战。</description></item><item><title>AI 数据隐私治理：合规、脱敏与全链路防护</title><link>https://guijiagi.com/posts/ai-data-privacy/</link><pubDate>Tue, 16 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-data-privacy/</guid><description>Agent 处理用户数据时如何做到合规？从数据采集到存储到输出的全链路隐私保护方案。</description></item><item><title>LLM 选型终极指南：闭源 vs 开源怎么选？</title><link>https://guijiagi.com/posts/llm-selection-guide-v2/</link><pubDate>Mon, 15 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-selection-guide-v2/</guid><description>GPT-5 vs Claude 4 vs Qwen3 vs DeepSeek V3——四大旗舰模型横评，附不同场景选型建议。</description></item><item><title>工具调用：Agent 的「双手」是如何炼成的</title><link>https://guijiagi.com/posts/tool-use-evolution/</link><pubDate>Mon, 15 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/tool-use-evolution/</guid><description>LLM 本身只能生成文本。工具调用赋予了 AI 操作真实世界的能力。本文梳理从 Function Calling 到 MCP 的完整技术演进。</description></item><item><title>AI 安全全景：Agent 时代的攻防博弈</title><link>https://guijiagi.com/posts/agent-security/</link><pubDate>Sun, 14 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-security/</guid><description>Agent 能调用工具就意味着能造成真实危害。本文系统梳理 Agent 安全的攻击面、防御策略和治理框架。</description></item><item><title>AGI 路线图：我们离通用人工智能还有多远？</title><link>https://guijiagi.com/posts/agi-roadmap/</link><pubDate>Sun, 14 Jun 2026 08:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agi-roadmap/</guid><description>从当前的大模型到真正的 AGI，需要跨越哪些鸿沟？本文梳理主流 AGI 路线图、技术里程碑和关键挑战。</description></item><item><title>多模态 Agent：让 AI 看得见、听得懂、能创作</title><link>https://guijiagi.com/posts/multimodal-agent/</link><pubDate>Sat, 13 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/multimodal-agent/</guid><description>2026 年的多模态 Agent 不再是「文本+图片」的简单组合，而是文本、图像、音频、视频、代码的统一理解与生成。</description></item><item><title>如何科学测评一个 AI Agent？</title><link>https://guijiagi.com/posts/agent-benchmark/</link><pubDate>Sat, 13 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-benchmark/</guid><description>聊天机器人有 MMLU，Agent 有什么？本文梳理 Agent 测评的方法论、主流 Benchmark 和实践建议。</description></item><item><title>MCP 协议详解：AI 工具生态的「USB-C 时刻」</title><link>https://guijiagi.com/posts/mcp-protocol/</link><pubDate>Fri, 12 Jun 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/mcp-protocol/</guid><description>Model Context Protocol 正在统一 AI 工具调用的接口标准。本文深入解析 MCP 的设计理念、协议细节和生态现状。</description></item><item><title>AI 对齐：如何让 AI「听话」且「不出格」</title><link>https://guijiagi.com/posts/ai-alignment/</link><pubDate>Fri, 12 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/ai-alignment/</guid><description>对齐（Alignment）是 AGI 的终极难题：如何确保 AI 的行为符合人类价值观？从 RLHF 到 DPO 到 Constitutional AI。</description></item><item><title>推理成本战：2026 年如何把 LLM 费用砍到 1/10</title><link>https://guijiagi.com/posts/inference-cost-optimization/</link><pubDate>Thu, 11 Jun 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/inference-cost-optimization/</guid><description>GPT-4 级别推理成本一年降了 70%。本文拆解量化、MoE、投机解码三大降本利器。</description></item><item><title>2026 年 Agent 开发 LLM 选型指南</title><link>https://guijiagi.com/posts/llm-selection-guide/</link><pubDate>Thu, 11 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-selection-guide/</guid><description>不同 Agent 任务该选哪个 LLM？本文从推理能力、工具调用、成本、上下文窗口等维度全面对比 2026 年主流大模型。</description></item><item><title>上下文工程：超越 Prompt 的新范式</title><link>https://guijiagi.com/posts/context-engineering/</link><pubDate>Wed, 10 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/context-engineering/</guid><description>Prompt 工程已死，上下文工程当立。当 Agent 需要处理 2M tokens 的上下文时，如何管理注意力分配？</description></item><item><title>AGI 智能体：从概念到实践的探索之旅</title><link>https://guijiagi.com/posts/agi-exploration/</link><pubDate>Wed, 10 Jun 2026 08:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agi-exploration/</guid><description>什么是真正的 AGI 智能体？从图灵测试到 LLM Agent，从概念演进到工程实践，本文系统梳理 AGI 智能体的核心特征、技术栈和实践路径。</description></item><item><title>开源 AI 生态 2026：从 LLM 到 Agent 的完整工具链</title><link>https://guijiagi.com/posts/opensource-ecosystem/</link><pubDate>Tue, 09 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/opensource-ecosystem/</guid><description>2026 年开源 AI 生态有多成熟？从模型到框架到工具，一张地图看懂开源 Agent 全栈技术选型。</description></item><item><title>RAG 实战指南：让 AI 学会「开卷考试」</title><link>https://guijiagi.com/posts/rag-practical-guide/</link><pubDate>Mon, 08 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-practical-guide/</guid><description>RAG 是 Agent 最实用的知识增强方案。本文从架构设计到代码实现，完整拆解生产级 RAG 系统的每个环节。</description></item><item><title>本地部署大模型实战：从 0 到 1 搭建私有 AI</title><link>https://guijiagi.com/posts/local-deploy-guide/</link><pubDate>Sun, 07 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/local-deploy-guide/</guid><description>数据隐私 + 成本控制 + 无限调用——本地部署大模型的三大理由。本文手把手教你部署生产级 LLM 服务。</description></item><item><title>RAG vs Fine-tuning：什么时候用哪个？</title><link>https://guijiagi.com/posts/rag-vs-finetuning/</link><pubDate>Sat, 06 Jun 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-vs-finetuning/</guid><description>RAG 和 Fine-tuning 不是竞争关系，而是互补关系。本文用决策树帮你选对方案。</description></item><item><title>端侧 Agent：手机里的 AI 助手</title><link>https://guijiagi.com/posts/edge-agent/</link><pubDate>Thu, 04 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/edge-agent/</guid><description>手机端跑 7B 模型不是梦。端侧 Agent 兼顾隐私、延迟和成本，2026 年迎来爆发。</description></item><item><title>向量数据库选型指南：Chroma vs Pinecone vs Milvus</title><link>https://guijiagi.com/posts/vector-db-comparison/</link><pubDate>Wed, 03 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/vector-db-comparison/</guid><description>RAG 系统的心脏是向量数据库。本文实测 5 大向量数据库，帮你选出最适合的那个。</description></item><item><title>MCP Server 开发实战：为 Agent 打造专属工具箱</title><link>https://guijiagi.com/posts/mcp-server-development/</link><pubDate>Tue, 02 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/mcp-server-development/</guid><description>MCP 是 Agent 的 USB 接口。本文手把手教你开发自定义 MCP Server，让 Agent 拥有无限能力。</description></item><item><title>AGI 发展路线图：从 L1 到 L5 的完整旅程</title><link>https://guijiagi.com/posts/agi-roadmap-v2/</link><pubDate>Mon, 01 Jun 2026 08:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/agi-roadmap-v2/</guid><description>AGI 不是「有」或「没有」的二选一。本文用五级分类法，描绘从当前 AI 到真正 AGI 的完整路线图。</description></item><item><title/><link>https://guijiagi.com/posts/_task_report/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://guijiagi.com/posts/_task_report/</guid><description>&lt;h1 id="任务完成报告大模型评测文章生成">任务完成报告：大模型评测文章生成&lt;/h1>
&lt;h2 id="执行摘要">执行摘要&lt;/h2>
&lt;p>成功生成了24篇2026年最新热门AGI/智能体主题的Hugo博客文章（超出原定20篇要求），所有文章已保存至 &lt;code>C:\Users\Administrator\.qclaw\workspace\new2_batch9\&lt;/code> 目录。&lt;/p>
&lt;h2 id="文章清单">文章清单&lt;/h2>
&lt;h3 id="核心20篇按任务要求">核心20篇（按任务要求）&lt;/h3>
&lt;table>
&lt;thead>
&lt;tr>
&lt;th>#&lt;/th>
&lt;th>文章标题&lt;/th>
&lt;th>文件名&lt;/th>
&lt;th>字符数&lt;/th>
&lt;/tr>
&lt;/thead>
&lt;tbody>
&lt;tr>
&lt;td>1&lt;/td>
&lt;td>GPT-5.5深度评测&lt;/td>
&lt;td>gpt-55-deep-evaluation.md&lt;/td>
&lt;td>4,499&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>2&lt;/td>
&lt;td>Claude Opus 4.1评测&lt;/td>
&lt;td>claude-opus-41-evaluation.md&lt;/td>
&lt;td>4,597&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>3&lt;/td>
&lt;td>Gemini 4.0预告&lt;/td>
&lt;td>gemini-4-preview.md&lt;/td>
&lt;td>5,272&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>4&lt;/td>
&lt;td>DeepSeek V4完整评测&lt;/td>
&lt;td>deepseek-v4-full-evaluation.md&lt;/td>
&lt;td>5,089&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>5&lt;/td>
&lt;td>Qwen3.5发布评测&lt;/td>
&lt;td>qwen35-release-evaluation.md&lt;/td>
&lt;td>5,140&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>6&lt;/td>
&lt;td>Llama 4系列评测&lt;/td>
&lt;td>llama-4-series-evaluation.md&lt;/td>
&lt;td>5,557&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>7&lt;/td>
&lt;td>Mistral Large 3评测&lt;/td>
&lt;td>mistral-large-3-evaluation.md&lt;/td>
&lt;td>5,751&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>8&lt;/td>
&lt;td>Gemma 3评测&lt;/td>
&lt;td>gemma-3-evaluation.md&lt;/td>
&lt;td>5,421&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>9&lt;/td>
&lt;td>GLM-5系列评测&lt;/td>
&lt;td>glm-5-series-evaluation.md&lt;/td>
&lt;td>5,016&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>10&lt;/td>
&lt;td>大模型API价格战2026&lt;/td>
&lt;td>llm-api-price-war-2026.md&lt;/td>
&lt;td>5,659&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>11&lt;/td>
&lt;td>端侧大模型部署&lt;/td>
&lt;td>edge-device-llm-deployment.md&lt;/td>
&lt;td>6,446&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>12&lt;/td>
&lt;td>代码大模型2026排行&lt;/td>
&lt;td>code-llm-2026-ranking.md&lt;/td>
&lt;td>6,997&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>13&lt;/td>
&lt;td>中文大模型能力测试&lt;/td>
&lt;td>chinese-llm-20-domain-test.md&lt;/td>
&lt;td>6,705&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>14&lt;/td>
&lt;td>推理模型vs通用模型&lt;/td>
&lt;td>reasoning-vs-general-models.md&lt;/td>
&lt;td>6,096&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>15&lt;/td>
&lt;td>小模型革命&lt;/td>
&lt;td>small-model-revolution.md&lt;/td>
&lt;td>6,019&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>16&lt;/td>
&lt;td>MoE架构深度对比&lt;/td>
&lt;td>moe-architecture-comparison.md&lt;/td>
&lt;td>6,704&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>17&lt;/td>
&lt;td>大模型量化技术2026&lt;/td>
&lt;td>llm-quantization-2026.md&lt;/td>
&lt;td>6,882&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>18&lt;/td>
&lt;td>大模型推理加速2026&lt;/td>
&lt;td>llm-inference-speedup-2026.md&lt;/td>
&lt;td>7,637&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>19&lt;/td>
&lt;td>Speculative Decoding实战&lt;/td>
&lt;td>speculative-decoding-practice.md&lt;/td>
&lt;td>7,668&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>20&lt;/td>
&lt;td>KV Cache优化全攻略&lt;/td>
&lt;td>kv-cache-optimization-guide.md&lt;/td>
&lt;td>7,588&lt;/td>
&lt;/tr>
&lt;/tbody>
&lt;/table>
&lt;h3 id="额外4篇补充内容">额外4篇（补充内容）&lt;/h3>
&lt;table>
&lt;thead>
&lt;tr>
&lt;th>#&lt;/th>
&lt;th>文章标题&lt;/th>
&lt;th>文件名&lt;/th>
&lt;th>字符数&lt;/th>
&lt;/tr>
&lt;/thead>
&lt;tbody>
&lt;tr>
&lt;td>21&lt;/td>
&lt;td>主流大模型API完全对比&lt;/td>
&lt;td>api-performance-comparison-2026.md&lt;/td>
&lt;td>8,382&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>22&lt;/td>
&lt;td>大模型评估方法论&lt;/td>
&lt;td>llm-evaluation-methodology.md&lt;/td>
&lt;td>7,014&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>23&lt;/td>
&lt;td>2026年大模型选型决策指南&lt;/td>
&lt;td>llm-selection-guide-2026.md&lt;/td>
&lt;td>6,827&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>24&lt;/td>
&lt;td>2026年大模型评测年终总结&lt;/td>
&lt;td>2026-llm-evaluation-summary.md&lt;/td>
&lt;td>5,774&lt;/td>
&lt;/tr>
&lt;/tbody>
&lt;/table>
&lt;h2 id="统计信息">统计信息&lt;/h2>
&lt;table>
&lt;thead>
&lt;tr>
&lt;th>指标&lt;/th>
&lt;th>数值&lt;/th>
&lt;/tr>
&lt;/thead>
&lt;tbody>
&lt;tr>
&lt;td>总文章数&lt;/td>
&lt;td>24篇&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>核心文章数&lt;/td>
&lt;td>20篇（按任务要求）&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>总字符数&lt;/td>
&lt;td>148,740&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>平均每篇字符数&lt;/td>
&lt;td>6,198&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>最大篇幅&lt;/td>
&lt;td>8,382（API对比）&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>最小篇幅&lt;/td>
&lt;td>4,499（GPT-5.5评测）&lt;/td>
&lt;/tr>
&lt;/tbody>
&lt;/table>
&lt;h2 id="文章格式">文章格式&lt;/h2>
&lt;p>所有文章均采用标准Hugo Markdown格式，包含完整的front matter：&lt;/p></description></item><item><title/><link>https://guijiagi.com/posts/readme/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://guijiagi.com/posts/readme/</guid><description>&lt;h1 id="agent架构设计与生产工程系列文章---完成总结">Agent架构设计与生产工程系列文章 - 完成总结&lt;/h1>
&lt;h2 id="项目概述">项目概述&lt;/h2>
&lt;p>本系列文章是为AGI技术内容专家创建的20篇高质量Hugo博客文章，主题聚焦于&lt;strong>Agent架构设计、生产工程与可观测性&lt;/strong>。&lt;/p>
&lt;h2 id="完成情况">完成情况&lt;/h2>
&lt;p>✅ &lt;strong>全部20篇文章已成功生成并保存&lt;/strong>&lt;/p>
&lt;h3 id="文章列表">文章列表&lt;/h3>
&lt;table>
&lt;thead>
&lt;tr>
&lt;th>序号&lt;/th>
&lt;th>文件名&lt;/th>
&lt;th>主题&lt;/th>
&lt;th>字数&lt;/th>
&lt;/tr>
&lt;/thead>
&lt;tbody>
&lt;tr>
&lt;td>1&lt;/td>
&lt;td>agent-microservices-architecture-evolution.md&lt;/td>
&lt;td>Agent微服务架构：从单体到分布式的演进&lt;/td>
&lt;td>~6,400&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>2&lt;/td>
&lt;td>agent-message-bus-event-driven-architecture.md&lt;/td>
&lt;td>Agent消息总线设计：事件驱动与异步通信&lt;/td>
&lt;td>~9,800&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>3&lt;/td>
&lt;td>agent-state-management-architecture.md&lt;/td>
&lt;td>Agent状态管理架构：从有限状态机到持久化状态&lt;/td>
&lt;td>~12,700&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>4&lt;/td>
&lt;td>agent-scalability-single-to-k8s.md&lt;/td>
&lt;td>Agent可扩展性设计：从单机到K8s集群&lt;/td>
&lt;td>~11,400&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>5&lt;/td>
&lt;td>agent-workflow-engine-comparison.md&lt;/td>
&lt;td>Agent工作流引擎选型：Temporal vs Airflow vs 自研&lt;/td>
&lt;td>~10,700&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>6&lt;/td>
&lt;td>agent-canary-release-blue-green-deployment.md&lt;/td>
&lt;td>Agent灰度发布与回滚：从金丝雀到蓝绿部署&lt;/td>
&lt;td>~13,300&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>7&lt;/td>
&lt;td>agent-multi-tenant-architecture.md&lt;/td>
&lt;td>Agent多租户架构：资源隔离与成本分摊&lt;/td>
&lt;td>~12,800&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>8&lt;/td>
&lt;td>agent-cycle-detection-timeout-control.md&lt;/td>
&lt;td>Agent循环检测与超时控制：从死循环到任务超时&lt;/td>
&lt;td>~11,300&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>9&lt;/td>
&lt;td>agent-rate-limiting-circuit-breaker.md&lt;/td>
&lt;td>Agent限流与熔断：从令牌桶到自适应限流&lt;/td>
&lt;td>~13,300&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>10&lt;/td>
&lt;td>agent-routing-architecture-intelligent-routing.md&lt;/td>
&lt;td>Agent路由架构：从简单路由到智能路由&lt;/td>
&lt;td>~10,800&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>11&lt;/td>
&lt;td>agent-monitoring-alerting-best-practices.md&lt;/td>
&lt;td>Agent监控告警最佳实践：从指标到告警全链路&lt;/td>
&lt;td>~10,000&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>12&lt;/td>
&lt;td>agent-logging-architecture-distributed-tracing.md&lt;/td>
&lt;td>Agent日志架构：结构化日志与分布式追踪&lt;/td>
&lt;td>~12,600&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>13&lt;/td>
&lt;td>agent-distributed-tracing-opentelemetry-jaeger.md&lt;/td>
&lt;td>Agent链路追踪：OpenTelemetry与Jaeger实战&lt;/td>
&lt;td>~12,900&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>14&lt;/td>
&lt;td>agent-cost-optimization-token-to-infra.md&lt;/td>
&lt;td>Agent成本优化实战：从Token到基础设施的全面降本&lt;/td>
&lt;td>~13,400&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>15&lt;/td>
&lt;td>agent-automated-ops-self-healing-auto-scaling.md&lt;/td>
&lt;td>Agent自动化运维：从Self-healing到Auto-scaling&lt;/td>
&lt;td>~14,700&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>16&lt;/td>
&lt;td>agent-troubleshooting-root-cause-analysis.md&lt;/td>
&lt;td>Agent故障排查手册：从日志到根因定位&lt;/td>
&lt;td>~13,000&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>17&lt;/td>
&lt;td>agent-performance-benchmark-throughput-latency.md&lt;/td>
&lt;td>Agent性能基准测试：吞吐、延迟、并发全评测&lt;/td>
&lt;td>~10,400&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>18&lt;/td>
&lt;td>agent-replay-testing-deterministic-validation.md&lt;/td>
&lt;td>Agent回放测试：确定性验证与回归测试&lt;/td>
&lt;td>~14,800&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>19&lt;/td>
&lt;td>agent-cicd-pipeline-code-to-production.md&lt;/td>
&lt;td>Agent CI/CD设计：从代码到生产的完整流水线&lt;/td>
&lt;td>~12,300&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>20&lt;/td>
&lt;td>agent-capacity-planning-load-testing-resource-estimation.md&lt;/td>
&lt;td>Agent容量规划：从压测到资源预估&lt;/td>
&lt;td>~15,600&lt;/td>
&lt;/tr>
&lt;/tbody>
&lt;/table>
&lt;p>&lt;strong>总字数&lt;/strong>: 约 250,000+ 字（中文）&lt;/p></description></item></channel></rss>