[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-Marktechpost--AI-Tutorial-Codes-Included":3,"tool-Marktechpost--AI-Tutorial-Codes-Included":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",150720,2,"2026-04-11T11:33:10",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":82,"stars":91,"forks":92,"last_commit_at":93,"license":78,"difficulty_score":94,"env_os":95,"env_gpu":96,"env_ram":95,"env_deps":97,"category_tags":101,"github_topics":103,"view_count":32,"oss_zip_url":78,"oss_zip_packed_at":78,"status":17,"created_at":111,"updated_at":112,"faqs":113,"releases":114},6610,"Marktechpost\u002FAI-Tutorial-Codes-Included","AI-Tutorial-Codes-Included","Codes\u002FNotebooks for AI Projects","AI-Tutorial-Codes-Included 是一个专为人工智能开发者与研究爱好者打造的实战代码库，汇集了涵盖代理智能（Agentic AI）、大语言模型（LLMs）、检索增强生成（RAG）、计算机视觉及语音 AI 等前沿领域的完整项目笔记与可运行代码。\n\n面对 AI 技术迭代快、理论到落地门槛高的问题，该资源库提供了从基础机器学习到复杂多智能体协作系统的全套解决方案。它不仅包含通用的算法实现，更独特地聚焦于生产级应用开发，例如如何构建具备思维链、工具调用及并行工作流的智能体系统，以及如何整合 Google 搜索、地图等外部服务打造多功能 Agent。此外，库中还涉及 AI 安全、基础设施搭建及 MCP 指南等深层技术内容。\n\n无论是希望快速上手的初学者，还是寻求架构灵感的专业工程师与研究人员，都能在此找到对应的 Jupyter Notebook 教程与源码。通过直接复用和修改这些经过验证的代码示例，用户可以大幅缩短实验周期，深入理解从模型微调到复杂智能体编排的核心逻辑，是探索现代 AI 工程化落地的优质学习伴侣。","🤝 Show your support - give a ⭐️ if you liked the content\n\n## Star History\n\n[![Star History Chart](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMarktechpost_AI-Tutorial-Codes-Included_readme_c30713c16b03.png)](https:\u002F\u002Fwww.star-history.com\u002F#Marktechpost\u002FAI-Tutorial-Codes-Included&type=date&legend=top-left)\n\n\n\n# AI-Tutorials\u002FImplementations and Notebooks\n\n### Index \n\n* [Agentic AI and Agents](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks?tab=readme-ov-file#agentic-ai-and-agents)\n* [ML & Data Science](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#machine-learning--data-science)\n* [MCPs Guides](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks?tab=readme-ov-file#mcps-guides)\n* [LLMs and Other AI Section](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included?tab=readme-ov-file#llmsml-and-other-ai-section)\n* [Voice AI](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#voice-ai)\n* [RAG](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#rag)\n* [Computer Vision](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#computer-vision)\n* [Security](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#security)\n* [AI Infrastructure](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#ai-infrastructure)\n\n---\n  \n\n### Agentic AI and Agents\n\n▶ How to Combine Google Search, Google Maps, and Custom Functions in a Single Gemini API Call With Context Circulation, Parallel Tool IDs, and Multi-Step Agentic Chains [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fgemini3_tool_combination_maps_grounding_context_circulation_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F07\u002Fhow-to-combine-google-search-google-maps-and-custom-functions-in-a-single-gemini-api-call-with-context-circulation-parallel-tool-ids-and-multi-step-agentic-chains\u002F)\n\n▶ How to Build Production-Ready Agentic Systems with Z.AI GLM-5 Using Thinking Mode, Tool Calling, Streaming, and Multi-Turn Workflows [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fglm5_agentic_systems_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F03\u002Fhow-to-build-production-ready-agentic-systems-with-z-ai-glm-5-using-thinking-mode-tool-calling-streaming-and-multi-turn-workflows\u002F)\n\n▶ How to Build Production Ready AgentScope Workflows with ReAct Agents, Custom Tools, Multi-Agent Debate, Structured Output and Concurrent Pipelines [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20Workflows\u002Fagentscope_production_agent_workflows_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F01\u002Fhow-to-build-production-ready-agentscope-workflows-with-react-agents-custom-tools-multi-agent-debate-structured-output-and-concurrent-pipelines\u002F)\n\n▶ How to Build and Evolve a Custom OpenAI Agent with A-Evolve Using Benchmarks, Skills, Memory, and Workspace Mutations [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fa_evolve_openai_agent_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F31\u002Fhow-to-build-and-evolve-a-custom-openai-agent-with-a-evolve-using-benchmarks-skills-memory-and-workspace-mutations\u002F)\n\n▶ How to Build Advanced Cybersecurity AI Agents with CAI Using Tools, Guardrails, Handoffs, and Multi-Agent Workflows [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fcai_cybersecurity_ai_agents_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F29\u002Fhow-to-build-advanced-cybersecurity-ai-agents-with-cai-using-tools-guardrails-handoffs-and-multi-agent-workflows\u002F)\n\n▶ A Coding Guide to Exploring nanobot’s Full Agent Pipeline, from Wiring Up Tools and Memory to Skills, Subagents, and Cron Scheduling [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fnanobot_deep_dive_build_ai_agent_from_inside_out_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F28\u002Fa-coding-guide-to-exploring-nanobots-full-agent-pipeline-from-wiring-up-tools-and-memory-to-skills-subagents-and-cron-scheduling\u002F)\n\n▶ An Implementation of IWE’s Context Bridge as an AI-Powered Knowledge Graph with Agentic RAG, OpenAI Function Calling, and Graph Traversal [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fiwe_knowledge_graph_ai_agents_agentic_rag_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F27\u002Fan-implementation-of-iwes-context-bridge-as-an-ai-powered-knowledge-graph-with-agentic-rag-openai-function-calling-and-graph-traversal\u002F)\n\n▶ How to Build a Vision-Guided Web AI Agent with MolmoWeb-4B Using Multimodal Reasoning and Action Prediction [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fmolmoweb_multimodal_web_agent_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F25\u002Fhow-to-build-a-vision-guided-web-ai-agent-with-molmoweb-4b-using-multimodal-reasoning-and-action-prediction\u002F)\n\n▶ A Coding Implementation to Design Self-Evolving Skill Engine with OpenSpace for Skill Learning, Token Efficiency, and Collective Intelligence [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fopenspace_self_evolving_skill_evolution_engine_token_efficiency_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F24\u002Fa-coding-implementation-to-design-self-evolving-skill-engine-with-openspace-for-skill-learning-token-efficiency-and-collective-intelligence\u002F)\n\n▶ How to Design a Production-Ready AI Agent That Automates Google Colab Workflows Using Colab-MCP, MCP Tools, FastMCP, and Kernel Execution [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMCP%20Codes\u002FWiring_AI_Agents_to_Google_Colab_MCP_Deep_Dive_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F23\u002Fhow-to-design-a-production-ready-ai-agent-that-automates-google-colab-workflows-using-colab-mcp-mcp-tools-fastmcp-and-kernel-execution\u002F)\n\n▶ Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FReinforcement%20learning\u002Frlax_dqn_cartpole_jax_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F22\u002Fimplementing-deep-q-learning-dqn-from-scratch-using-rlax-jax-haiku-and-optax-to-train-a-cartpole-reinforcement-learning-agent\u002F)\n\n▶ A Coding Implementation Showcasing ClawTeam's Multi-Agent Swarm Orchestration with OpenAI Function Calling [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FClawTeam_Agent_Swarm_Intelligence_OpenAI_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F20\u002Fa-coding-implementation-showcasing-clawteams-multi-agent-swarm-orchestration-with-openai-function-calling\u002F)\n\n▶ A Coding Implementation to Design an Enterprise AI Governance System Using OpenClaw Gateway Policy Engines, Approval Workflows and Auditable Agent Execution [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fopenclaw_enterprise_ai_governance_gateway_approval_workflows_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F15\u002Fa-coding-implementation-to-design-an-enterprise-ai-governance-system-using-openclaw-gateway-policy-engines-approval-workflows-and-auditable-agent-execution\u002F)\n\n▶ How to Build an Autonomous Machine Learning Research Loop in Google Colab Using Andrej Karpathy’s AutoResearch Framework for Hyperparameter Discovery and Experiment Tracking [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fautoresearch_autonomous_ml_research_colab_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F12\u002Fhow-to-build-an-autonomous-machine-learning-research-loop-in-google-colab-using-andrej-karpathys-autoresearch-framework-for-hyperparameter-discovery-and-experiment-tracking\u002F)\n\n▶ How to Design a Streaming Decision Agent with Partial Reasoning, Online Replanning, and Reactive Mid-Execution Adaptation in Dynamic Environments [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fstreaming_decision_agent_online_replanning_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F11\u002Fhow-to-design-a-streaming-decision-agent-with-partial-reasoning-online-replanning-and-reactive-mid-execution-adaptation-in-dynamic-environments\u002F)\n\n▶ How to Build a Self-Designing Meta-Agent That Automatically Constructs, Instantiates, and Refines Task-Specific AI Agents [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fmeta_agent_auto_designer_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F10\u002Fhow-to-build-a-self-designing-meta-agent-that-automatically-constructs-instantiates-and-refines-task-specific-ai-agents\u002F)\n\n▶ How to Build a Risk-Aware AI Agent with Internal Critic, Self-Consistency Reasoning, and Uncertainty Estimation for Reliable Decision-Making [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fcritic_augmented_risk_aware_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F09\u002Fhow-to-build-a-risk-aware-ai-agent-with-internal-critic-self-consistency-reasoning-and-uncertainty-estimation-for-reliable-decision-making\u002F)\n\n▶ Building Next-Gen Agentic AI: A Complete Framework for Cognitive Blueprint Driven Runtime Agents with Memory Tools and Validation [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fcognitive_blueprint_runtime_agents_auton_framework_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F07\u002Fbuilding-next-gen-agentic-ai-a-complete-framework-for-cognitive-blueprint-driven-runtime-agents-with-memory-tools-and-validation\u002F)\n\n▶ How to Design an Advanced Tree-of-Thoughts Multi-Branch Reasoning Agent with Beam Search, Heuristic Scoring, and Depth-Limited Pruning [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Ftree_of_thoughts_multi_branch_reasoning_agent_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F05\u002Fhow-to-design-an-advanced-tree-of-thoughts-multi-branch-reasoning-agent-with-beam-search-heuristic-scoring-and-depth-limited-pruning\u002F)\n\n▶ How to Build an EverMem-Style Persistent AI Agent OS with Hierarchical Memory, FAISS Vector Retrieval, SQLite Storage, and Automated Memory Consolidation [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fevermem_persistent_agent_os_faiss_sqlite_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F04\u002Fhow-to-build-an-evermem-style-persistent-ai-agent-os-with-hierarchical-memory-faiss-vector-retrieval-sqlite-storage-and-automated-memory-consolidation\u002F)\n\n▶ How to Design a Production-Grade Multi-Agent Communication System Using LangGraph Structured Message Bus, ACP Logging, and Persistent Shared State Architecture [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgent%20Communication%20Protocol\u002FGetting%20Started\u002Flanggraph_acp_structured_message_bus_multi_agent_system_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F01\u002Fhow-to-design-a-production-grade-multi-agent-communication-system-using-langgraph-structured-message-bus-acp-logging-and-persistent-shared-state-architecture\u002F)\n\n▶ A Coding Implementation to Build a Hierarchical Planner AI Agent Using Open-Source LLMs with Tool Execution and Structured Multi-Agent Reasoning [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fhierarchical_planner_ai_agent_open_source_llm_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F27\u002Fa-coding-implementation-to-build-a-hierarchical-planner-ai-agent-using-open-source-llms-with-tool-execution-and-structured-multi-agent-reasoning\u002F)\n\n▶ How to Build a Production-Grade Customer Support Automation Pipeline with Griptape Using Deterministic Tools and Agentic Reasoning [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20Workflows\u002Fgriptape_customer_support_automation_pipeline_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F23\u002Fhow-to-build-a-production-grade-customer-support-automation-pipeline-with-griptape-using-deterministic-tools-and-agentic-reasoning\u002F)\n\n▶ How to Design a Swiss Army Knife Research Agent with Tool-Using AI, Web Search, PDF Analysis, Vision, and Automated Reporting [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fswiss_army_knife_research_agent_tool_using_ai_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F20\u002Fhow-to-design-a-swiss-army-knife-research-agent-with-tool-using-ai-web-search-pdf-analysis-vision-and-automated-reporting\u002F)\n\n▶ How to Design an Agentic Workflow for Tool-Driven Route Optimization with Deterministic Computation and Structured Outputs [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20Workflows\u002Fagentic_workflow_tool_driven_route_optimization_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F21\u002Fhow-to-design-an-agentic-workflow-for-tool-driven-route-optimization-with-deterministic-computation-and-structured-outputs\u002F)\n\n▶ A Coding Implementation to Build Bulletproof Agentic Workflows with PydanticAI Using Strict Schemas, Tool Injection, and Model-Agnostic Execution [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20Workflows\u002Fpydanticai_bulletproof_agentic_workflows_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F19\u002Fa-coding-implementation-to-build-bulletproof-agentic-workflows-with-pydanticai-using-strict-schemas-tool-injection-and-model-agnostic-execution\u002F)\n\n▶ A Coding Implementation to Design a Stateful Tutor Agent with Long-Term Memory, Semantic Recall, and Adaptive Practice Generation [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fstateful_tutor_long_term_memory_agent_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F15\u002Fa-coding-implementation-to-design-a-stateful-tutor-agent-with-long-term-memory-semantic-recall-and-adaptive-practice-generation\u002F)\n\n▶ How to Build a Self-Organizing Agent Memory System for Long-Term AI Reasoning [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fself_organizing_agent_memory_long_horizon_reasoning_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F14\u002Fhow-to-build-a-self-organizing-agent-memory-system-for-long-term-ai-reasoning\u002F)\n\n▶ How to Build an Atomic-Agents RAG Pipeline with Typed Schemas, Dynamic Context Injection, and Agent Chaining [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fatomic_agents_advanced_rag_pipeline_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F11\u002Fhow-to-build-an-atomic-agents-rag-pipeline-with-typed-schemas-dynamic-context-injection-and-agent-chaining\u002F)\n\n▶ How to Build a Production-Grade Agentic AI System with Hybrid Retrieval, Provenance-First Citations, Repair Loops, and Episodic Memory [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002FUltra_Agentic_AI_Hybrid_Retrieval_Guardrails_Episodic_Memory_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F06\u002Fhow-to-build-a-production-grade-agentic-ai-system-with-hybrid-retrieval-provenance-first-citations-repair-loops-and-episodic-memory\u002F)\n\n▶ How to Build Efficient Agentic Reasoning Systems by Dynamically Pruning Multiple Chain-of-Thought Paths Without Losing Accuracy [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fagentic_chain_of_thought_pruning_dynamic_reasoning_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F04\u002Fhow-to-build-efficient-agentic-reasoning-systems-by-dynamically-pruning-multiple-chain-of-thought-paths-without-losing-accuracy\u002F)\n\n\n▶ A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FReinforcement%20learning\u002Foffline_safety_critical_rl_conservative_q_learning_d3rlpy_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F03\u002Fa-coding-implementation-to-train-safety-critical-reinforcement-learning-agents-offline-using-conservative-q-learning-with-d3rlpy-and-fixed-historical-data\u002F)\n\n▶ How to Build Memory-Driven AI Agents with Short-Term, Long-Term, and Episodic Memory [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fmemory_engineering_short_term_long_term_episodic_agents_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F01\u002Fhow-to-build-memory-driven-ai-agents-with-short-term-long-term-and-episodic-memory\u002F)\n\n▶ How to Design Self-Reflective Dual-Agent Governance Systems with Constitutional AI for Secure and Compliant Financial Operations [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fconstitutional_dual_agent_financial_governance_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F28\u002Fhow-to-design-self-reflective-dual-agent-governance-systems-with-constitutional-ai-for-secure-and-compliant-financial-operations\u002F)\n\n▶ How a Haystack-Powered Multi-Agent System Detects Incidents, Investigates Metrics and Logs, and Produces Production-Grade Incident Reviews End-to-End [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fmulti_agent_incident_response_haystack_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F26\u002Fhow-a-haystack-powered-multi-agent-system-detects-incidents-investigates-metrics-and-logs-and-produces-production-grade-incident-reviews-end-to-end\u002F)\n\n▶ How an AI Agent Chooses What to Do Under Tokens, Latency, and Tool-Call Budget Constraints? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fcost_aware_planning_agent_budget_constrained_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F23\u002Fhow-an-ai-agent-chooses-what-to-do-under-tokens-latency-and-tool-call-budget-constraints\u002F)\n\n▶ A Coding Guide to Anemoi-Style Semi-Centralized Agentic Systems Using Peer-to-Peer Critic Loops in LangGraph [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fanemoi_semi_centralized_peer_critic_loop_langgraph_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F20\u002Fa-coding-guide-to-anemoi-style-semi-centralized-agentic-systems-using-peer-to-peer-critic-loops-in-langgraph\u002F)\n\n▶ How to Build a Self-Evaluating Agentic AI System with LlamaIndex and OpenAI Using Retrieval, Tool Use, and Automated Quality Checks [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fagentic_llamaindex_rag_self_evaluation_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F17\u002Fhow-to-build-a-self-evaluating-agentic-ai-system-with-llamaindex-and-openai-using-retrieval-tool-use-and-automated-quality-checks\u002F)\n\n▶ How to Build a Safe, Autonomous Prior Authorization Agent for Healthcare Revenue Cycle Management with Human-in-the-Loop Controls [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fautonomous_prior_auth_agent_healthcare_rcm_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F15\u002Fhow-to-build-a-safe-autonomous-prior-authorization-agent-for-healthcare-revenue-cycle-management-with-human-in-the-loop-controls\u002F)\n\n▶ How to Design an Agentic AI Architecture with LangGraph and OpenAI Using Adaptive Deliberation, Memory Graphs, and Reflexion Loops [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fagentic_ai_with_langgraph_adaptive_memory_reflexion_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F06\u002Fhow-to-design-an-agentic-ai-architecture-with-langgraph-and-openai-using-adaptive-deliberation-memory-graphs-and-reflexion-loops\u002F)\n\n▶ A Coding Guide to Design and Orchestrate Advanced ReAct-Based Multi-Agent Workflows with AgentScope and OpenAI [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentscope_multi_agent_incident_response_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F04\u002Fa-coding-guide-to-design-and-orchestrate-advanced-react-based-multi-agent-workflows-with-agentscope-and-openai\u002F)\n\n▶ How to Build a Production-Ready Multi-Agent Incident Response System Using OpenAI Swarm and Tool-Augmented Agents [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fopenai_swarm_multi_agent_incident_response_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F03\u002Fhow-to-build-a-production-ready-multi-agent-incident-response-system-using-openai-swarm-and-tool-augmented-agents\u002F)\n\n▶ A Coding Implementation to Build a Self-Testing Agentic AI System Using Strands to Red-Team Tool-Using Agents and Enforce Safety at Runtime [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fstrands_agentic_red_teaming_tool_injection_harness_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F02\u002Fa-coding-implementation-to-build-a-self-testing-agentic-ai-system-using-strands-to-red-team-tool-using-agents-and-enforce-safety-at-runtime\u002F)\n\n▶ How to Design Transactional Agentic AI Systems with LangGraph Using Two-Phase Commit, Human Interrupts, and Safe Rollbacks [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Ftransactional_agentic_ai_langgraph_two_phase_commit_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F31\u002Fhow-to-design-transactional-agentic-ai-systems-with-langgraph-using-two-phase-commit-human-interrupts-and-safe-rollbacks\u002F)\n\n▶ How to Build a Robust Multi-Agent Pipeline Using CAMEL with Planning, Web-Augmented Reasoning, Critique, and Persistent Memory [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fcamel_multi_agent_research_pipeline_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F29\u002Fhow-to-build-a-robust-multi-agent-pipeline-using-camel-with-planning-web-augmented-reasoning-critique-and-persistent-memory\u002F)\n\n▶ How to Build Contract-First Agentic Decision Systems with PydanticAI for Risk-Aware, Policy-Compliant Enterprise AI [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fpydantic_ai_contract_first_agentic_decision_system_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F28\u002Fhow-to-build-contract-first-agentic-decision-systems-with-pydanticai-for-risk-aware-policy-compliant-enterprise-ai\u002F)\n\n▶ How to Build Production-Grade Agentic Workflows with GraphBit Using Deterministic Tools, Validated Execution Graphs, and Optional LLM Orchestration [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20Workflows\u002Fgraphbit_production_agentic_workflows_offline_to_llm_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F27\u002Fhow-to-build-production-grade-agentic-workflows-with-graphbit-using-deterministic-tools-validated-execution-graphs-and-optional-llm-orchestration\u002F)\n\n▶ A Coding Guide to Build an Autonomous Multi-Agent Logistics System with Route Planning, Dynamic Auctions, and Real-Time Visualization Using Graph-Based Simulation [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fagentic_logistics_swarm_simulation_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F24\u002Fa-coding-guide-to-build-an-autonomous-multi-agent-logistics-system-with-route-planning-dynamic-auctions-and-real-time-visualization-using-graph-based-simulation\u002F)\n\n▶ How to Build a Fully Autonomous Local Fleet-Maintenance Analysis Agent Using SmolAgents and Qwen Model [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fsmolagents_fleet_maintenance_autonomous_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F22\u002Fhow-to-build-a-fully-autonomous-local-fleet-maintenance-analysis-agent-using-smolagents-and-qwen-model\u002F)\n\n▶ How to Build a Proactive Pre-Emptive Churn Prevention Agent with Intelligent Observation and Strategy Formation [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fpreemptive_churn_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F23\u002Fhow-to-build-a-proactive-pre-emptive-churn-prevention-agent-with-intelligent-observation-and-strategy-formation\u002F)\n\n▶ A Coding Guide to Design a Complete Agentic Workflow in Gemini for Automated Medical Evidence Gathering and Prior Authorization Submission [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002FGemini_Agentic_Medical_Authorization_Workflow_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F20\u002Fa-coding-guide-to-design-a-complete-agentic-workflow-in-gemini-for-automated-medical-evidence-gathering-and-prior-authorization-submission\u002F)\n\n▶ How to Orchestrate a Fully Autonomous Multi-Agent Research and Writing Pipeline Using CrewAI and Gemini for Real-Time Intelligent Collaboration [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fcrewai_multiagent_gemini_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F17\u002Fhow-to-orchestrate-a-fully-autonomous-multi-agent-research-and-writing-pipeline-using-crewai-and-gemini-for-real-time-intelligent-collaboration\u002F)\n\n▶ A Complete Workflow for Automated Prompt Optimization Using Gemini Flash, Few-Shot Selection, and Evolutionary Instruction Search [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FPrompt%20Optimization\u002Fgemini_prompt_optimization_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F19\u002Fa-complete-workflow-for-automated-prompt-optimization-using-gemini-flash-few-shot-selection-and-evolutionary-instruction-search\u002F)\n\n▶ How to Design a Gemini-Powered Self-Correcting Multi-Agent AI System with Semantic Routing, Symbolic Guardrails, and Reflexive Orchestration [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fgemini_semantic_agent_orchestrator_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F15\u002Fhow-to-design-a-gemini-powered-self-correcting-multi-agent-ai-system-with-semantic-routing-symbolic-guardrails-and-reflexive-orchestration\u002F)\n\n▶ How to Design a Fully Local Agentic Storytelling Pipeline Using Griptape Workflows, Hugging Face Models, and Modular Creative Task Orchestration [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fgriptape_local_agentic_story_pipeline_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F12\u002Fhow-to-design-a-fully-local-agentic-storytelling-pipeline-using-griptape-workflows-hugging-face-models-and-modular-creative-task-orchestration\u002F)\n\n▶ A Coding Guide to Build a Procedural Memory Agent That Learns, Stores, Retrieves, and Reuses Skills as Neural Modules Over Time [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fprocedural_memory_agent_skill_learning_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F09\u002Fa-coding-guide-to-build-a-procedural-memory-agent-that-learns-stores-retrieves-and-reuses-skills-as-neural-modules-over-time\u002F)\n\n▶ How to Build an Adaptive Meta-Reasoning Agent That Dynamically Chooses Between Fast, Deep, and Tool-Based Thinking Strategies [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fadaptive_meta_reasoning_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F06\u002Fhow-to-build-an-adaptive-meta-reasoning-agent-that-dynamically-chooses-between-fast-deep-and-tool-based-thinking-strategies\u002F)\n\n▶ How to Design a Fully Local Multi-Agent Orchestration System Using TinyLlama for Intelligent Task Decomposition and Autonomous Collaboration [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Flocal_multi_agent_manager_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F05\u002Fhow-to-design-a-fully-local-multi-agent-orchestration-system-using-tinyllama-for-intelligent-task-decomposition-and-autonomous-collaboration\u002F)\n\n▶ How to Build a Meta-Cognitive AI Agent That Dynamically Adjusts Its Own Reasoning Depth for Efficient Problem Solving [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fmeta_cognitive_reasoning_controller_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F03\u002Fhow-to-build-a-meta-cognitive-ai-agent-that-dynamically-adjusts-its-own-reasoning-depth-for-efficient-problem-solving\u002F)\n\n▶ A Coding Guide to Design an Agentic AI System Using a Control-Plane Architecture for Safe, Modular, and Scalable Tool-Driven Reasoning Workflows [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fcontrol_plane_agentic_ai_system_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F28\u002Fa-coding-guide-to-design-an-agentic-ai-system-using-a-control-plane-architecture-for-safe-modular-and-scalable-tool-driven-reasoning-workflows\u002F)\n\n▶ A Coding Implementation for an Agentic AI Framework that Performs Literature Analysis, Hypothesis Generation, Experimental Planning, Simulation, and Scientific Reporting [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fagentic_scientific_discovery_pipeline_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F27\u002Fa-coding-implementation-for-an-agentic-ai-framework-that-performs-literature-analysis-hypothesis-generation-experimental-planning-simulation-and-scientific-reporting\u002F)\n\n▶ How to Build a Neuro-Symbolic Hybrid Agent that Combines Logical Planning with Neural Perception for Robust Autonomous Decision-Making [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fneuro_symbolic_hybrid_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F24\u002Fhow-to-build-a-neuro-symbolic-hybrid-agent-that-combines-logical-planning-with-neural-perception-for-robust-autonomous-decision-making\u002F)\n\n▶ How to Design a Mini Reinforcement Learning Environment-Acting Agent with Intelligent Local Feedback, Adaptive Decision-Making, and Multi-Agent Coordination [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentic_multi_agent_rl_gridworld_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F22\u002Fhow-to-design-a-mini-reinforcement-learning-environment-acting-agent-with-intelligent-local-feedback-adaptive-decision-making-and-multi-agent-coordination\u002F)\n\n▶ How to Build a Fully Offline Multi-Tool Reasoning Agent with Dynamic Planning, Error Recovery, and Intelligent Function Routing [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fadvanced_multitool_agentic_ai_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F20\u002Fhow-to-build-a-fully-offline-multi-tool-reasoning-agent-with-dynamic-planning-error-recovery-and-intelligent-function-routing\u002F)\n\n▶ An Implementation of a Comprehensive Empirical Framework for Benchmarking Reasoning Strategies in Modern Agentic AI Systems [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentic_benchmarking_empirical_study_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F19\u002Fan-implementation-of-a-comprehensive-empirical-framework-for-benchmarking-reasoning-strategies-in-modern-agentic-ai-systems\u002F)\n\n▶ How to Build an Agentic Deep Reinforcement Learning System with Curriculum Progression, Adaptive Exploration, and Meta-Level UCB Planning [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentic_deep_rl_curriculum_ucb_meta_control_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F18\u002Fhow-to-build-an-agentic-deep-reinforcement-learning-system-with-curriculum-progression-adaptive-exploration-and-meta-level-ucb-planning\u002F)\n\n▶ How to Build Memory-Powered Agentic AI That Learns Continuously Through Episodic Experiences and Semantic Patterns for Long-Term Autonomy [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fmemory_driven_agentic_ai_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F15\u002Fhow-to-build-memory-powered-agentic-ai-that-learns-continuously-through-episodic-experiences-and-semantic-patterns-for-long-term-autonomy\u002F)\n\n▶ How to Design an Advanced Multi-Agent Reasoning System with spaCy Featuring Planning, Reflection, Memory, and Knowledge Graphs [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fspacy_agentic_ai_system_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F14\u002Fhow-to-design-an-advanced-multi-agent-reasoning-system-with-spacy-featuring-planning-reflection-memory-and-knowledge-graphs\u002F)\n\n▶ How to Build a Fully Self-Verifying Data Operations AI Agent Using Local Hugging Face Models for Automated Planning, Execution, and Testing [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fself_verifying_dataops_agent_local_hf_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F13\u002Fhow-to-build-a-fully-self-verifying-data-operations-ai-agent-using-local-hugging-face-models-for-automated-planning-execution-and-testing\u002F)\n\n▶ A Coding Implementation to Build Neural Memory Agents with Differentiable Memory, Meta-Learning, and Experience Replay for Continual Adaptation in Dynamic Environments [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fneural_memory_agents_continual_learning_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F09\u002Fa-coding-implementation-to-build-neural-memory-agents-with-differentiable-memory-meta-learning-and-experience-replay-for-continual-adaptation-in-dynamic-environments\u002F)\n\n▶ How to Build an Agentic Voice AI Assistant that Understands, Reasons, Plans, and Responds through Autonomous Multi-Step Intelligence [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FVoice%20AI\u002Fagentic_voice_ai_autonomous_assistant_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F08\u002Fhow-to-build-an-agentic-voice-ai-assistant-that-understands-reasons-plans-and-responds-through-autonomous-multi-step-intelligence\u002F)\n\n▶ Build a Multi-Agent System for Integrated Transcriptomic, Proteomic, and Metabolomic Data Interpretation with Pathway Reasoning [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fmulti_agent_omics_integration_pipeline_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F07\u002Fbuild-a-multi-agent-system-for-integrated-transcriptomic-proteomic-and-metabolomic-data-interpretation-with-pathway-reasoning\u002F)\n\n▶ How to Build a Model-Native Agent That Learns Internal Planning, Memory, and Multi-Tool Reasoning Through End-to-End Reinforcement Learning [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002FModel_Native_Agentic_AI_End_to_End_RL_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F05\u002Fhow-to-build-a-model-native-agent-that-learns-internal-planning-memory-and-multi-tool-reasoning-through-end-to-end-reinforcement-learning\u002F)\n\n▶ Build an Autonomous Wet-Lab Protocol Planner and Validator Using Salesforce CodeGen for Agentic Experiment Design and Safety Optimization [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fwetlab_protocol_planner_codegen_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F06\u002Fbuild-an-autonomous-wet-lab-protocol-planner-and-validator-using-salesforce-codegen-for-agentic-experiment-design-and-safety-optimization\u002F)\n\n▶ How to Design a Persistent Memory and Personalized Agentic AI System with Decay and Self-Evaluation? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002FPersistent_Memory_Personalised_Agentic_AI_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F02\u002Fhow-to-design-a-persistent-memory-and-personalized-agentic-ai-system-with-decay-and-self-evaluation\u002F)\n\n▶ How to Design an Autonomous Multi-Agent Data and Infrastructure Strategy System Using Lightweight Qwen Models for Efficient Pipeline Intelligence? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentic_data_infrastructure_strategy_qwen_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F30\u002Fhow-to-design-an-autonomous-multi-agent-data-and-infrastructure-strategy-system-using-lightweight-qwen-models-for-efficient-pipeline-intelligence\u002F)\n\n▶ How to Build a Fully Functional Computer-Use Agent that Thinks, Plans, and Executes Virtual Actions Using Local AI Models [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FComputer_Use_Agent_Local_AI_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F25\u002Fhow-to-build-a-fully-functional-computer-use-agent-that-thinks-plans-and-executes-virtual-actions-using-local-ai-models\u002F)\n\n▶ A Coding Implementation of a Comprehensive Enterprise AI Benchmarking Framework to Evaluate Rule-Based LLM, and Hybrid Agentic AI Systems Across Real-World Tasks [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fenterprise_agentic_benchmarking_framework_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F01\u002Fa-coding-implementation-of-a-comprehensive-enterprise-ai-benchmarking-framework-to-evaluate-rule-based-llm-and-hybrid-agentic-ai-systems-across-real-world-tasks\u002F)\n\n▶ How to Build Ethically Aligned Autonomous Agents through Value-Guided Reasoning and Self-Correcting Decision-Making Using Open-Source Models [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FValue_Alignment_and_Ethics_in_Agentic_Systems_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F29\u002Fhow-to-build-ethically-aligned-autonomous-agents-through-value-guided-reasoning-and-self-correcting-decision-making-using-open-source-models\u002F)\n\n▶ How to Build, Train, and Compare Multiple Reinforcement Learning Agents in a Custom Trading Environment Using Stable-Baselines3 [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fadvanced_stable_baselines3_trading_agent_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F26\u002Fhow-to-build-train-and-compare-multiple-reinforcement-learning-agents-in-a-custom-trading-environment-using-stable-baselines3\u002F)\n\n▶ How I Built an Intelligent Multi-Agent Systems with AutoGen, LangChain, and Hugging Face to Demonstrate Practical Agentic AI Workflows [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FAgentic_AI_LangChain_AutoGen_HuggingFace_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F21\u002Fhow-i-built-an-intelligent-multi-agent-systems-with-autogen-langchain-and-hugging-face-to-demonstrate-practical-agentic-ai-workflows\u002F)\n\n▶ A Coding Guide to Build a Fully Functional Multi-Agent Marketplace Using uAgent [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fuagents_multi_agent_marketplace_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F23\u002Fa-coding-guide-to-build-a-fully-functional-multi-agent-marketplace-using-uagent\u002F)\n\n▶ A Coding Implementation of Secure AI Agent with Self-Auditing Guardrails, PII Redaction, and Safe Tool Access in Python [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fsecure_ai_agent_with_guardrails_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F12\u002Fa-coding-implementation-of-secure-ai-agent-with-self-auditing-guardrails-pii-redaction-and-safe-tool-access-in-python\u002F)\n\n▶ Meet LangChain’s DeepAgents Library and a Practical Example to See How DeepAgents Actually Work in Action [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FLangchain_Deepagents.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F20\u002Fmeet-langchains-deepagents-library-and-a-practical-example-to-see-how-deepagents-actually-work-in-action\u002F)\n\n▶ An Intelligent Conversational Machine Learning Pipeline Integrating LangChain Agents and XGBoost for Automated Data Science Workflows [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FLangChain_XGBoost_Agentic_Pipeline_Tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F07\u002Fan-intelligent-conversational-machine-learning-pipeline-integrating-langchain-agents-and-xgboost-for-automated-data-science-workflows\u002F)\n\n▶ A Coding Guide to Build an AI-Powered Cryptographic Agent System with Hybrid Encryption, Digital Signatures, and Adaptive Security Intelligence [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FAI_Crypto_Agent_Secure_Comms_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F16\u002Fa-coding-guide-to-build-an-ai-powered-cryptographic-agent-system-with-hybrid-encryption-digital-signatures-and-adaptive-security-intelligence\u002F)\n\n▶ How to Build an Advanced Agentic Retrieval-Augmented Generation (RAG) System with Dynamic Strategy and Smart Retrieval? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentic_rag_tutorial_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F30\u002Fhow-to-build-an-advanced-agentic-retrieval-augmented-generation-rag-system-with-dynamic-strategy-and-smart-retrieval\u002F)\n\n▶ A Coding Guide to Build a Hierarchical Supervisor Agent Framework with CrewAI and Google Gemini for Coordinated Multi-Agent Workflows [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fsupervisor_framework_crewai_gemini_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F30\u002Fa-coding-guide-to-build-a-hierarchical-supervisor-agent-framework-with-crewai-and-google-gemini-for-coordinated-multi-agent-workflows\u002F)\n\n▶ How to Build an Intelligent AI Desktop Automation Agent with Natural Language Commands and Interactive Simulation? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fai_desktop_automation_agent_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F26\u002Fhow-to-build-an-intelligent-ai-desktop-automation-agent-with-natural-language-commands-and-interactive-simulation\u002F)\n\n▶ How to Build an Advanced End-to-End Voice AI Agent Using Hugging Face Pipelines? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fhow_to_build_an_advanced_end_to_end_voice_ai_agent_using_hugging_face_pipelines.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F17\u002Fhow-to-build-an-advanced-end-to-end-voice-ai-agent-using-hugging-face-pipelines\u002F)\n\n▶ How to Create Reliable Conversational AI Agents Using Parlant? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fparlant.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F22\u002Fhow-to-create-reliable-conversational-ai-agents-using-parlant\u002F)\n\n▶ How to Build a Multilingual OCR AI Agent in Python with EasyOCR and OpenCV [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fadvanced_ocr_ai_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F12\u002Fhow-to-build-a-multilingual-ocr-ai-agent-in-python-with-easyocr-and-opencv\u002F)\n\n▶ How to Build a Robust Advanced Neural AI Agent with Stable Training, Adaptive Learning, and Intelligent Decision-Making? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fadvanced_neural_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F13\u002Fhow-to-build-a-robust-advanced-neural-ai-agent-with-stable-training-adaptive-learning-and-intelligent-decision-making\u002F)\n\n▶ Building Advanced MCP (Model Context Protocol) Agents with Multi-Agent Coordination, Context Awareness, and Gemini Integration [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FBuilding%20Advanced%20MCP%20Agents%20with%20Multi-Agent%20Coordination.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F10\u002Fbuilding-advanced-mcp-model-context-protocol-agents-with-multi-agent-coordination-context-awareness-and-gemini-integration\u002F)\n\n▶ How to Build a Complete Multi-Domain AI Web Agent Using Notte and Gemini [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FBuild%20a%20Complete%20Multi-Domain%20AI%20Web%20Agent%20Using%20Notte%20and%20Gemini) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F08\u002Fhow-to-build-a-complete-multi-domain-ai-web-agent-using-notte-and-gemini\u002F)\n\n▶ How to Create a Bioinformatics AI Agent Using Biopython for DNA and Protein Analysis [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FBioinformatics%20AI%20Agent%20with%20Biopython) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F07\u002Fhow-to-create-a-bioinformatics-ai-agent-using-biopython-for-dna-and-protein-analysis\u002F)\n\n▶ Step-by-Step Guide to AI Agent Development Using Microsoft Agent-Lightning [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagent_lightning_prompt_optimization_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F31\u002Fstep-by-step-guide-to-ai-agent-development-using-microsoft-agent-lightning\u002F)\n\n▶ How to Build an Advanced AI Agent with Summarized Short-Term and Vector-Based Long-Term Memory [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FAdvanced%20AI%20Agent%20with%20Summarized%20Short%20Term%20and%20Vector-Based%20LongTerm%20Memory) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F02\u002Fhow-to-build-an-advanced-ai-agent-with-summarized-short-term-and-vector-based-long-term-memory\u002F)\n\n▶ How to Build a Conversational Research AI Agent with LangGraph: Step Replay and Time-Travel Checkpoints [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Flanggraph_time_travel_research_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F31\u002Fhow-to-build-a-conversational-research-ai-agent-with-langgraph-step-replay-and-time-travel-checkpoints\u002F)\n\n▶ How to Build a Multi-Round Deep Research Agent with Gemini, DuckDuckGo API, and Automated Reporting? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fdeep_research_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F28\u002Fhow-to-build-a-multi-round-deep-research-agent-with-gemini-duckduckgo-api-and-automated-reporting\u002F)\n\n▶ A Coding Guide to Building a Brain-Inspired Hierarchical Reasoning AI Agent with Hugging Face Models [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fhrm_braininspired_ai_agent_huggingface_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F30\u002Fa-coding-guide-to-building-a-brain-inspired-hierarchical-reasoning-ai-agent-with-hugging-face-models\u002F)\n\n▶ A Full Code Implementation to Design a Graph-Structured AI Agent with Gemini for Task Planning, Retrieval, Computation, and Self-Critique [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fgraphagent_gemini_advanced_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F23\u002Fa-full-code-implementation-to-design-a-graph-structured-ai-agent-with-gemini-for-task-planning-retrieval-computation-and-self-critique\u002F)\n\n▶ Building a Reliable End-to-End Machine Learning Pipeline Using MLE-Agent and Ollama Locally [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fmle_agent_ollama_local_pipeline_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F25\u002Fbuilding-a-reliable-end-to-end-machine-learning-pipeline-using-mle-agent-and-ollama-locally\u002F)\n\n▶ An Implementation Guide to Build a Modular Conversational AI Agent with Pipecat and HuggingFace [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fpipecat_huggingface_implementation_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F13\u002Fan-implementation-guide-to-build-a-modular-conversational-ai-agent-with-pipecat-and-huggingface\u002F)\n\n▶ Building a Secure and Memory-Enabled Cipher Workflow for AI Agents with Dynamic LLM Selection and API Integration [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fcipher_memory_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F11\u002Fbuilding-a-secure-and-memory-enabled-cipher-workflow-for-ai-agents-with-dynamic-llm-selection-and-api-integration\u002F)\n\n▶ A Developer’s Guide to OpenAI’s GPT-5 Model Capabilities [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FGPT-5\u002FGPT_5.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F08\u002Fa-developers-guide-to-openais-gpt-5-model-capabilities\u002F)\n\n▶ Building an Advanced PaperQA2 Research Agent with Google Gemini for Scientific Literature Analysis [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fpaperqa2_gemini_research_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F09\u002Fbuilding-an-advanced-paperqa2-research-agent-with-google-gemini-for-scientific-literature-analysis\u002F)\n\n▶ A Code Implementation to Build a Multi-Agent Research System with OpenAI Agents, Function Tools, Handoffs, and Session Memory [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fopenai_agents_multiagent_research_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F08\u002Fa-code-implementation-to-build-a-multi-agent-research-system-with-openai-agents-function-tools-handoffs-and-session-memory\u002F)\n\n▶ A Coding Implementation to Build a Self-Adaptive Goal-Oriented AI Agent Using Google Gemini and the SAGE Framework [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fsage_ai_agent_gemini_implementation_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F06\u002Fa-coding-implementation-to-build-a-self-adaptive-goal-oriented-ai-agent-using-google-gemini-and-the-sage-framework\u002F)\n\n▶ Building a Multi-Agent Conversational AI Framework with Microsoft AutoGen and Gemini API [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fgemini_autogen_multiagent_framework_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F04\u002Fbuilding-a-multi-agent-conversational-ai-framework-with-microsoft-autogen-and-gemini-api\u002F)\n\n▶ A Coding Guide to Build an Intelligent Conversational AI Agent with Agent Memory Using Cognee and Free Hugging Face Models [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FCognee_Agent_Tutorial_with_HuggingFace_Integration_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F31\u002Fa-coding-guide-to-build-an-intelligent-conversational-ai-agent-with-agent-memory-using-cognee-and-free-hugging-face-models\u002F)\n\n▶ A Coding Guide to Build a Scalable Multi-Agent System with Google ADK [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fadvanced_google_adk_multi_agent_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F30\u002Fa-coding-guide-to-build-a-scalable-multi-agent-system-with-google-adk\u002F)\n\n▶ A Coding Guide to Build a Tool-Calling ReAct Agent Fusing Prolog Logic with Gemini and LangGraph [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fprolog_gemini_langgraph_react_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F24\u002Fa-coding-guide-to-build-a-tool-calling-react-agent-fusing-prolog-logic-with-gemini-and-langgraph\u002F)\n\n▶ A Coding Guide to Build an AI Code-Analysis Agent with Griffe [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fgriffe_ai_code_analyzer_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F16\u002Fa-coding-guide-to-build-an-ai-code-analysis-agent-with-griffe\u002F)\n\n▶ A Code Implementation for Designing Intelligent Multi-Agent Workflows with the BeeAI Framework [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fbeeai_multi_agent_workflow_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F07\u002Fa-code-implementation-for-designing-intelligent-multi-agent-workflows-with-the-beeai-framework\u002F)\n\n▶ Implementing a Tool-Enabled Multi-Agent Workflow with Python, OpenAI API, and PrimisAI Nexus [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fprimisai_nexus_multi_agent_workflow_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F07\u002Fimplementing-a-tool-enabled-multi-agent-workflow-with-python-openai-api-and-primisai-nexus\u002F)\n\n▶ Getting Started with Agent Communication Protocol (ACP): Build a Weather Agent with Python [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Ftree\u002Fmain\u002FAgent%20Communication%20Protocol\u002FGetting%20Started) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F06\u002Fgetting-started-with-agent-communication-protocol-acp-build-a-weather-agent-with-python\u002F)\n\n▶ Build a Powerful Multi-Tool AI Agent Using Nebius with Llama 3 and Real-Time Reasoning Tools [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fnebius_llama3_multitool_agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F27\u002Fbuild-a-powerful-multi-tool-ai-agent-using-nebius-with-llama-3-and-real-time-reasoning-tools\u002F)\n\n▶ Building Production-Ready Custom AI Agents for Enterprise Workflows with Monitoring, Orchestration, and Scalability [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fproduction_ready_custom_ai_agents_workflows_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F22\u002Fbuilding-production-ready-custom-ai-agents-for-enterprise-workflows-with-monitoring-orchestration-and-scalability\u002F)\n\n▶ Building an A2A-Compliant Random Number Agent: A Step-by-Step Guide to Implementing the Low-Level Executor Pattern with Python [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Ftree\u002Fmain\u002FA2A_Simple_Agent) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F21\u002Fbuilding-an-a2a-compliant-random-number-agent-a-step-by-step-guide-to-implementing-the-low-level-executor-pattern-with-python\u002F)\n\n▶ Build a Low-Footprint AI Coding Assistant with Mistral Devstral [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fmistral_devstral_compact_loading_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F25\u002Fbuild-a-low-footprint-ai-coding-assistant-with-mistral-devstral\u002F)\n\n▶ How to Build an Advanced BrightData Web Scraper with Google Gemini for AI-Powered Data Extraction [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FEnhanced_BrightData_Gemini_Scraper_Tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F18\u002Fhow-to-build-an-advanced-brightdata-web-scraper-with-google-gemini-for-ai-powered-data-extraction\u002F)\n\n▶ Build an Intelligent Multi-Tool AI Agent Interface Using Streamlit for Seamless Real-Time Interaction [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fstreamlit_ai_agent_multitool_interface_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F20\u002Fbuild-an-intelligent-multi-tool-ai-agent-interface-using-streamlit-for-seamless-real-time-interaction\u002F)\n\n▶ How to Use python-A2A to Create and Connect Financial Agents with Google’s Agent-to-Agent (A2A) Protocol [Notebook-inflation_agent.py](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Finflation_agent.py) [Notebook-network.ipynb](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fnetwork.ipynb) [Notebook-emi_agent.py](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Femi_agent.py)  [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F16\u002Fhow-to-use-python-a2a-to-create-and-connect-financial-agents-with-googles-agent-to-agent-a2a-protocol\u002F)\n\n▶ Develop a Multi-Tool AI Agent with Secure Python Execution using Riza and Gemini [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002FAgents\u002FAgentic-AI\u002FRiza_Gemini_Agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F11\u002Fdevelop-a-multi-tool-ai-agent-with-secure-python-execution-using-riza-and-gemini\u002F)\n\n▶ Build a Gemini-Powered DataFrame Agent for Natural Language Data Analysis with Pandas and LangChain [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FGemini_Pandas_Agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F10\u002Fbuild-a-gemini-powered-dataframe-agent-for-natural-language-data-analysis-with-pandas-and-langchain\u002F)\n\n▶ How to Build an Asynchronous AI Agent Network Using Gemini for Research, Analysis, and Validation Tasks [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fgemini_agent_network_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F08\u002Fhow-to-build-an-asynchronous-ai-agent-network-using-gemini-for-research-analysis-and-validation-tasks\u002F)\n\n▶ How to Create Smart Multi-Agent Workflows Using the Mistral Agents API’s Handoffs Feature [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fagent_orchestration_with_mistral_agents_api.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F09\u002Fhow-to-create-smart-multi-agent-workflows-using-the-mistral-agents-apis-handoffs-feature\u002F)\n\n▶ How to Enable Function Calling in Mistral Agents Using the Standard JSON Schema Format [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fhow%20to%20enable%20function%20calling%20in%20Mistral%20Agents.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F08\u002Fhow-to-enable-function-calling-in-mistral-agents-using-the-standard-json-schema-format\u002F)\n\n▶ A Step-by-Step Coding Guide to Building an Iterative AI Workflow Agent Using LangGraph and Gemini [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FGraphAIAgent_LangGraph_Gemini_Workflow_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F05\u002Fa-step-by-step-coding-guide-to-building-an-iterative-ai-workflow-agent-using-langgraph-and-gemini\u002F)\n\n▶ A Coding Implementation to Build an Advanced Web Intelligence Agent with Tavily and Gemini AI [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fsmartwebagent_tavily_gemini_webintelligence_marktechpost2.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F03\u002Fa-coding-implementation-to-build-an-advanced-web-intelligence-agent-with-tavily-and-gemini-ai\u002F)\n\n▶ Hands-On Guide: Getting started with Mistral Agents API [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FGetting_Started_with_Mistral_Agents_API.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F03\u002Fhands-on-guide-getting-started-with-mistral-agents-api\u002F)\n\n▶ A Coding Guide to Building a Scalable Multi-Agent Communication Systems Using Agent Communication Protocol (ACP)  [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FA_Coding_Guide_to_ACP_Systems_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F31\u002Fa-coding-guide-to-building-a-scalable-multi-agent-communication-systems-using-agent-communication-protocol-acp\u002F)\n\n▶ A Coding Guide for Building a Self-Improving AI Agent Using Google’s Gemini API with Intelligent Adaptation Features [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FSelf_Improving_AI_Agent_with_Gemini_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F29\u002Fa-coding-guide-for-building-a-self-improving-ai-agent-using-googles-gemini-api-with-intelligent-adaptation-features\u002F)\n\n▶ A Step-by-Step Coding Implementation of an Agent2Agent Framework for Collaborative and Critique-Driven AI Problem Solving with Consensus-Building [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fagent2agent_collaboration_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F27\u002Fa-step-by-step-coding-implementation-of-an-agent2agent-framework-for-collaborative-and-critique-driven-ai-problem-solving-with-consensus-building\u002F)\n\n▶ A Coding Guide to Building a Customizable Multi-Tool AI Agent with LangGraph and Claude for Dynamic Agent Creation [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAINotebooks\u002Fblob\u002Fmain\u002FCustomizable_MultiTool_AI_Agent_with_Claude_Marktechpost%20(1).ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F24\u002Fstep-by-step-guide-to-build-a-customizable-multi-tool-ai-agent-with-langgraph-and-claude-for-dynamic-agent-creation\u002F)\n\n▶ A Coding Implementation to Build an AI Agent with Live Python Execution and Automated Validation [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FLive_Python_Execution_and_Validation_Agent_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F25\u002Fa-coding-implementation-to-build-an-ai-agent-with-live-python-execution-and-automated-validation\u002F)\n\n▶ A Comprehensive Coding Guide to Crafting Advanced Round-Robin Multi-Agent Workflows with Microsoft AutoGen  [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FSynthetic_Data_Creation.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F23\u002Fa-comprehensive-coding-guide-to-crafting-advanced-round-robin-multi-agent-workflows-with-microsoft-autogen\u002F)\n\n▶ A Coding Implementation of an Intelligent AI Assistant with Jina Search, LangChain, and Gemini for Real-Time Information Retrieval [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FJina_LangChain_Gemini_AI_Assistant_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F01\u002Fa-coding-implementation-of-an-intelligent-ai-assistant-with-jina-search-langchain-and-gemini-for-real-time-information-retrieval\u002F)\n\n---\n\n### Machine Learning & Data Science\n\n▶ An End-to-End Coding Guide to NVIDIA KVPress for Long-Context LLM Inference, KV Cache Compression, and Memory-Efficient Generation [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fnvidia_kvpress_long_context_kv_cache_compression_tutorial_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F09\u002Fan-end-to-end-coding-guide-to-nvidia-kvpress-for-long-context-llm-inference-kv-cache-compression-and-memory-efficient-generation\u002F)\n\n▶ A Comprehensive Implementation Guide to ModelScope for Model Search, Inference, Fine-Tuning, Evaluation, and Export [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDeep%20Learning\u002Fmodelscope_end_to_end_model_hub_dataset_nlp_cv_finetuning_evaluation_export_marktechpost(1).py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F08\u002Fa-comprehensive-implementation-guide-to-modelscope-for-model-search-inference-fine-tuning-evaluation-and-export\u002F)\n\n▶ Step by Step Guide to Build an End-to-End Model Optimization Pipeline with NVIDIA Model Optimizer Using FastNAS Pruning and Fine-Tuning [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDeep%20Learning\u002Fnvidia_model_optimizer_fastnas_pipeline_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F03\u002Fstep-by-step-guide-to-build-an-end-to-end-model-optimization-pipeline-with-nvidia-model-optimizer-using-fastnas-pruning-and-fine-tuning\u002F)\n\n▶ How to Build a Production-Ready Gemma 3 1B Instruct Generation AI Pipeline with Hugging Face Transformers, Chat Templates, and Colab Inference [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fgemma_3_1b_instruct_tutorial_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F01\u002Fhow-to-build-a-production-ready-gemma-3-1b-instruct-generation-ai-pipeline-with-hugging-face-transformers-chat-templates-and-colab-inference\u002F)\n\n▶ A Coding Guide to Implement Advanced Differential Equation Solvers, Stochastic Simulations, and Neural Ordinary Differential Equations Using Diffrax and JAX [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDeep%20Learning\u002Fdiffrax_differential_equations_neural_ode_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F19\u002Fa-coding-guide-to-implement-advanced-differential-equation-solvers-stochastic-simulations-and-neural-ordinary-differential-equations-using-diffrax-and-jax\u002F)\n\n▶ Beyond Accuracy: Quantifying the Production Fragility Caused by Excessive, Redundant, and Low-Signal Features in Regression [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002FFeature_Bloat.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F08\u002Fbeyond-accuracy-quantifying-the-production-fragility-caused-by-excessive-redundant-and-low-signal-features-in-regression\u002F)\n\n▶ How to Build Progress Monitoring Using Advanced tqdm for Async, Parallel, Pandas, Logging, and High-Performance Workflows [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Ftqdm_production_progress_monitoring_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F07\u002Fhow-to-build-progress-monitoring-using-advanced-tqdm-for-async-parallel-pandas-logging-and-high-performance-workflows\u002F)\n\n▶ A Coding Guide to Build a Scalable End-to-End Machine Learning Data Pipeline Using Daft for High-Performance Structured and Image Data Processing [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fdaft_end_to_end_ml_pipeline_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F05\u002Fa-coding-guide-to-build-a-scalable-end-to-end-machine-learning-data-pipeline-using-daft-for-high-performance-structured-and-image-data-processing\u002F)\n\n▶ A Coding Guide to Build a Scalable End-to-End Analytics and Machine Learning Pipeline on Millions of Rows Using Vaex [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fvaex_large_scale_analytics_and_ml_pipeline_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F02\u002Fa-coding-guide-to-build-a-scalable-end-to-end-analytics-and-machine-learning-pipeline-on-millions-of-rows-using-vaex\u002F)\n\n▶ How to Build an Explainable AI Analysis Pipeline Using SHAP-IQ to Understand Feature Importance, Interaction Effects, and Model Decision Breakdown [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FSHAP-IQ\u002Fshapiq_explainable_ai_feature_and_interaction_analysis_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F01\u002Fhow-to-build-an-explainable-ai-analysis-pipeline-using-shap-iq-to-understand-feature-importance-interaction-effects-and-model-decision-breakdown\u002F)\n\n▶ A Complete End-to-End Coding Guide to MLflow Experiment Tracking, Hyperparameter Optimization, Model Evaluation, and Live Model Deployment [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMLFlow%20for%20LLM%20Evaluation\u002Fmlflow_experiment_tracking_and_model_serving_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F01\u002Fa-complete-end-to-end-coding-guide-to-mlflow-experiment-tracking-hyperparameter-optimization-model-evaluation-and-live-model-deployment\u002F)\n\n▶ How to Build Interactive Geospatial Dashboards Using Folium with Heatmaps, Choropleths, Time Animation, Marker Clustering, and Advanced Interactive Plugins [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Ffolium_interactive_geospatial_visualization_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F27\u002Fhow-to-build-interactive-geospatial-dashboards-using-folium-with-heatmaps-choropleths-time-animation-marker-clustering-and-advanced-interactive-plugins\u002F)\n\n▶ A Coding Implementation to Simulate Practical Byzantine Fault Tolerance with Asyncio, Malicious Nodes, and Latency Analysis [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Fpbft_asyncio_byzantine_latency_simulator_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F24\u002Fa-coding-implementation-to-simulate-practical-byzantine-fault-tolerance-with-asyncio-malicious-nodes-and-latency-analysis\u002F)\n\n▶ How to Build an Advanced, Interactive Exploratory Data Analysis Workflow Using PyGWalker and Feature-Engineered Data [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fadvanced_pygwalker_interactive_eda_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F17\u002Fhow-to-build-an-advanced-interactive-exploratory-data-analysis-workflow-using-pygwalker-and-feature-engineered-data\u002F)\n\n▶ [In-Depth Guide] The Complete CTGAN + SDV Pipeline for High-Fidelity Synthetic Data [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fctgan_sdv_synthetic_data_pipeline_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F13\u002Fin-depth-guide-the-complete-ctgan-sdv-pipeline-for-high-fidelity-synthetic-data\u002F)\n\n▶ How to Build a Privacy-Preserving Federated Pipeline to Fine-Tune Large Language Models with LoRA Using Flower and PEFT [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FFederated%20Learning\u002Ffederated_lora_llm_finetuning_flower_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F09\u002Fhow-to-build-a-privacy-preserving-federated-pipeline-to-fine-tune-large-language-models-with-lora-using-flower-and-peft\u002F)\n\n▶ How to Design Production-Grade Mock Data Pipelines Using Polyfactory with Dataclasses, Pydantic, Attrs, and Nested Models [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fpolyfactory_production_grade_mock_data_generation_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F08\u002Fhow-to-design-production-grade-mock-data-pipelines-using-polyfactory-with-dataclasses-pydantic-attrs-and-nested-models\u002F)\n\n▶ A Coding, Data-Driven Guide to Measuring, Visualizing, and Enforcing Cognitive Complexity in Python Projects Using complexipy [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fcognitive_complexity_auditing_with_complexipy_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F06\u002Fa-coding-data-driven-guide-to-measuring-visualizing-and-enforcing-cognitive-complexity-in-python-projects-using-complexipy\u002F)\n\n▶ How to Build Advanced Quantum Algorithms Using Qrisp with Grover Search, Quantum Phase Estimation, and QAOA [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FQuantum%20Computing\u002FQrisp_Quantum_Algorithms_Grover_QPE_QAOA_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F03\u002Fhow-to-build-advanced-quantum-algorithms-using-qrisp-with-grover-search-quantum-phase-estimation-and-qaoa\u002F)\n\n▶ A Coding and Experimental Analysis of Decentralized Federated Learning with Gossip Protocols and Differential Privacy [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Fdecentralized_gossip_federated_learning_with_differential_privacy_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F01\u002Fa-coding-and-experimental-analysis-of-decentralized-federated-learning-with-gossip-protocols-and-differential-privacy\u002F)\n\n▶ A Coding Implementation to Training, Optimizing, Evaluating, and Interpreting Knowledge Graph Embeddings with PyKEENs [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fadvanced_pykeen_knowledge_graph_embeddings_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F30\u002Fa-coding-implementation-to-training-optimizing-evaluating-and-interpreting-knowledge-graph-embeddings-with-pykeen\u002F)\n\n▶ A Coding Deep Dive into Differentiable Computer Vision with Kornia Using Geometry Optimization, LoFTR Matching, and GPU Augmentations [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FComputer%20Vision\u002Fkornia_differentiable_vision_loftr_ransac_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F29\u002Fa-coding-deep-dive-into-differentiable-computer-vision-with-kornia-using-geometry-optimization-loftr-matching-and-gpu-augmentations\u002F)\n\n▶ How Machine Learning and Semantic Embeddings Reorder CVE Vulnerabilities Beyond Raw CVSS Scores [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FSecurity\u002Fai_assisted_vulnerability_prioritization_ml_nlp_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F23\u002Fhow-machine-learning-and-semantic-embeddings-reorder-cve-vulnerabilities-beyond-raw-cvss-scores\u002F)\n\n▶ How AutoGluon Enables Modern AutoML Pipelines for Production-Grade Tabular Models with Ensembling and Distillation [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fautogluon_end_to_end_tabular_modeling_and_deployment_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F21\u002Fhow-autogluon-enables-modern-automl-pipelines-for-production-grade-tabular-models-with-ensembling-and-distillation\u002F)\n\n▶ A Coding Guide to Understanding How Retries Trigger Failure Cascades in RPC and Event-Driven Architectures [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Frpc_vs_event_driven_failure_dynamics_distributed_systems_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F18\u002Fa-coding-guide-to-understanding-how-retries-trigger-failure-cascades-in-rpc-and-event-driven-architectures\u002F)\n\n▶ How to Build Portable, In-Database Feature Engineering Pipelines with Ibis Using Lazy Python APIs and DuckDB Execution [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fibis_portable_in_database_feature_engineering_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F09\u002Fhow-to-build-portable-in-database-feature-engineering-pipelines-with-ibis-using-lazy-python-apis-and-duckdb-execution\u002F)\n\n▶ A Coding Implementation to Build a Unified Apache Beam Pipeline Demonstrating Batch and Stream Processing with Event-Time Windowing Using DirectRunner [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Fapache_beam_batch_and_stream_windowing_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F07\u002Fa-coding-implementation-to-build-a-unified-apache-beam-pipeline-demonstrating-batch-and-stream-processing-with-event-time-windowing-using-directrunner\u002F)\n\n▶ Implementing Softmax From Scratch: Avoiding the Numerical Stability Trap [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002FSoftmax.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F06\u002Fimplementing-softmax-from-scratch-avoiding-the-numerical-stability-trap\u002F)\n\n▶ A Coding Implementation of an OpenAI-Assisted Privacy-Preserving Federated Fraud Detection System from Scratch Using Lightweight PyTorch Simulations [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FFederated%20Learning\u002Fopenai_federated_fraud_detection_from_scratch_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F30\u002Fa-coding-implementation-of-an-openai-assisted-privacy-preserving-federated-fraud-detection-system-from-scratch-using-lightweight-pytorch-simulations\u002F)\n\n▶ A Coding Implementation on Building Self-Organizing Zettelkasten Knowledge Graphs and Sleep-Consolidation Mechanisms [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002FAgentic_Zettelkasten_Memory_Martechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F25\u002Fa-coding-implementation-on-building-self-organizing-zettelkasten-knowledge-graphs-and-sleep-consolidation-mechanisms\u002F)\n\n▶ How to Build a High-Performance Distributed Task Routing System Using Kombu with Topic Exchanges and Concurrent Workers [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Fkombu_task_routing_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F19\u002Fhow-to-build-a-high-performance-distributed-task-routing-system-using-kombu-with-topic-exchanges-and-concurrent-workers\u002F)\n\n▶ A Coding Implementation of a Complete Hierarchical Bayesian Regression Workflow in NumPyro Using JAX-Powered Inference and Posterior Predictive Analysis [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fnumpyro_hierarchical_regression_advanced_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F07\u002Fa-coding-implementation-of-a-complete-hierarchical-bayesian-regression-workflow-in-numpyro-using-jax-powered-inference-and-posterior-predictive-analysis\u002F)\n\n▶ How to Design an Advanced Multi-Page Interactive Analytics Dashboard with Dynamic Filtering, Live KPIs, and Rich Visual Exploration Using Panel [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fadvanced_panel_interactive_dashboard_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F30\u002Fhow-to-design-an-advanced-multi-page-interactive-analytics-dashboard-with-dynamic-filtering-live-kpis-and-rich-visual-exploration-using-panel\u002F)\n\n▶ How We Learn Step-Level Rewards from Preferences to Solve Sparse-Reward Environments Using Online Process Reward Learning [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FReinforcement%20learning\u002Foprl_preference_shaped_rl_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F02\u002Fhow-we-learn-step-level-rewards-from-preferences-to-solve-sparse-reward-environments-using-online-process-reward-learning\u002F)\n\n▶ How to Build an End-to-End Interactive Analytics Dashboard Using PyGWalker Features for Insightful Data Exploration [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fadvanced_pygwalker_visual_analysis_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F11\u002Fhow-to-build-an-end-to-end-interactive-analytics-dashboard-using-pygwalker-features-for-insightful-data-exploration\u002F)\n\n▶ How to Design a Fully Interactive, Reactive, and Dynamic Terminal-Based Data Dashboard Using Textual? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fadvanced_textual_data_dashboard_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F15\u002Fhow-to-design-a-fully-interactive-reactive-and-dynamic-terminal-based-data-dashboard-using-textual\u002F)\n\n▶ A Coding Implementation to Build and Train Advanced Architectures with Residual Connections, Self-Attention, and Adaptive Optimization Using JAX, Flax, and Optax [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fadvanced_jax_flax_optax_training_pipeline_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F10\u002Fa-coding-implementation-to-build-and-train-advanced-architectures-with-residual-connections-self-attention-and-adaptive-optimization-using-jax-flax-and-optax\u002F)\n\n▶ How Can We Build Scalable and Reproducible Machine Learning Experiment Pipelines Using Meta Research Hydra? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fmeta_hydra_advanced_implementation_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F04\u002Fhow-can-we-build-scalable-and-reproducible-machine-learning-experiment-pipelines-using-meta-research-hydra\u002F)\n\n▶ How to Build an Advanced Multi-Page Reflex Web Application with Real-Time Database, Dynamic State Management, and Reactive UI [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fadvanced_reflex_reactive_webapp_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F08\u002Fhow-to-build-an-advanced-multi-page-reflex-web-application-with-real-time-database-dynamic-state-management-and-reactive-ui\u002F)\n\n▶ How to Build an End-to-End Data Engineering and Machine Learning Pipeline with Apache Spark and PySpark [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002FAdvanced_PySpark_End_to_End_Tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F01\u002Fhow-to-build-an-end-to-end-data-engineering-and-machine-learning-pipeline-with-apache-spark-and-pyspark\u002F)\n\n▶ How to Build Supervised AI Models When You Don’t Have Annotated Data [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002FActive_Learning.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F03\u002Fhow-to-build-supervised-ai-models-when-you-dont-have-annotated-data\u002F)\n\n---\n\n\n### MCPs Guides \n\n▶ How to Design a Production-Ready AI Agent That Automates Google Colab Workflows Using Colab-MCP, MCP Tools, FastMCP, and Kernel Execution [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMCP%20Codes\u002FWiring_AI_Agents_to_Google_Colab_MCP_Deep_Dive_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F23\u002Fhow-to-design-a-production-ready-ai-agent-that-automates-google-colab-workflows-using-colab-mcp-mcp-tools-fastmcp-and-kernel-execution\u002F)\n\n▶ How to Build a Stateless, Secure, and Asynchronous MCP-Style Protocol for Scalable Agent Workflows [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMCP%20Codes\u002Fstateless_async_mcp_protocol_demo_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F14\u002Fhow-to-build-a-stateless-secure-and-asynchronous-mcp-style-protocol-for-scalable-agent-workflows\u002F)\n\n▶ An Implementation to Build Dynamic AI Systems with the Model Context Protocol (MCP) for Real-Time Resource and Tool Integration [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMCP%20Codes\u002FModel_Context_Protocol_Integration_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F19\u002Fan-implementation-to-build-dynamic-ai-systems-with-the-model-context-protocol-mcp-for-real-time-resource-and-tool-integration\u002F)\n\n▶ Implementing OAuth 2.1 for MCP Servers with Scalekit: A Step-by-Step Coding Tutorial [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Ftree\u002Fmain\u002FOAuth%202.1%20for%20MCP%20Servers) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F01\u002Fimplementing-oauth-2-1-for-mcp-servers-with-scalekit-a-step-by-step-coding-tutorial\u002F)\n\n▶ Building an MCP-Powered AI Agent with Gemini and mcp-agent Framework: A Step-by-Step Implementation Guide [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fmcp_gemini_agent_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F17\u002Fbuilding-an-mcp-powered-ai-agent-with-gemini-and-mcp-agent-framework-a-step-by-step-implementation-guide\u002F)\n\n▶ Creating Dashboards Using Vizro MCP: Vizro is an Open-Source Python Toolkit by McKinsey [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F18\u002Fcreating-dashboards-using-vizro-mcp-vizro-is-an-open-source-python-toolkit-by-mckinsey\u002F)\n\n▶ A Step-by-Step Coding Guide to Defining Custom Model Context Protocol (MCP) Server and Client Tools with FastMCP and Integrating Them into Google Gemini 2.0’s Function‑Calling Workflow [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fcustom_mcp_tools_integration_with_fastmcp_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F04\u002F21\u002Fa-step-by-step-coding-guide-to-defining-custom-model-context-protocol-mcp-server-and-client-tools-with-fastmcp-and-integrating-them-into-google-gemini-2-0s-function%e2%80%91calling-workflow\u002F)\n\n▶ A Code Implementation to Building a Context-Aware AI Assistant in Google Colab Using LangChain, LangGraph, Gemini Pro, and Model Context Protocol (MCP) Principles with Tool Integration Support [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FContext_Aware_Assistant_MCP_Gemini_LangChain_LangGraph_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F04\u002F04\u002Fa-code-implementation-to-building-a-context-aware-ai-assistant-in-google-colab-using-langchain-langgraph-gemini-pro-and-model-context-protocol-mcp-principles-with-tool-integration-support\u002F)\n\n▶ Guide to Using the Desktop Commander MCP Server [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F01\u002Fguide-to-using-the-desktop-commander-mcp-server\u002F)\n\n---\n\n### LLMs,ML and Other AI Section\n\n▶ A Coding Guide to Build Advanced Document Intelligence Pipelines with Google LangExtract, OpenAI Models, Structured Extraction, and Interactive Visualization [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Flangextract_document_intelligence_pipeline_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F08\u002Fa-coding-guide-to-build-advanced-document-intelligence-pipelines-with-google-langextract-openai-models-structured-extraction-and-interactive-visualization\u002F)\n\n▶ How to Deploy Open WebUI with Secure OpenAI API Integration, Public Tunneling, and Browser-Based Chat Access [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fopen_webui_google_colab_secure_openai_tunnel_setup_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F07\u002Fhow-to-deploy-open-webui-with-secure-openai-api-integration-public-tunneling-and-browser-based-chat-access\u002F)\n\n▶ An Implementation Guide to Running NVIDIA Transformer Engine with Mixed Precision, FP8 Checks, Benchmarking, and Fallback Execution [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fnvidia_transformer_engine_colab_mixed_precision_fp8_benchmarking_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F06\u002Fan-implementation-guide-to-running-nvidia-transformer-engine-with-mixed-precision-fp8-checks-benchmarking-and-fallback-execution\u002F)\n\n▶ A Coding Implementation to Run Qwen3.5 Reasoning Models Distilled with Claude-Style Thinking Using GGUF and 4-Bit Quantization [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fqwen3_5_reasoning_colab_dual_path_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F26\u002Fa-coding-implementation-to-run-qwen3-5-reasoning-models-distilled-with-claude-style-thinking-using-gguf-and-4-bit-quantization\u002F)\n\n▶ A Coding Implementation to Build an Uncertainty-Aware LLM System with Confidence Estimation, Self-Evaluation, and Automatic Web Research [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Funcertainty_aware_llm_confidence_self_evaluation_auto_research_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F21\u002Fa-coding-implementation-to-build-an-uncertainty-aware-llm-system-with-confidence-estimation-self-evaluation-and-automatic-web-research\u002F)\n\n▶ How to Build a Stable and Efficient QLoRA Fine-Tuning Pipeline Using Unsloth for Large Language Models [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Funsloth_qlora_stable_sft_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F03\u002Fhow-to-build-a-stable-and-efficient-qlora-fine-tuning-pipeline-using-unsloth-for-large-language-models\u002F)\n\n▶ A Coding Guide to Instrumenting, Tracing, and Evaluating LLM Applications Using TruLens and OpenAI Models [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Evaluation\u002Ftrulens_llm_instrumentation_feedback_evaluation_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F22\u002Fa-coding-guide-to-instrumenting-tracing-and-evaluating-llm-applications-using-trulens-and-openai-models\u002F)\n\n▶ How to Align Large Language Models with Human Preferences Using Direct Preference Optimization, QLoRA, and Ultra-Feedback [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fdpo_alignment_qlora_ultrafeedback_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F12\u002Fhow-to-align-large-language-models-with-human-preferences-using-direct-preference-optimization-qlora-and-ultra-feedback\u002F)\n\n▶ How to Build a Matryoshka-Optimized Sentence Embedding Model for Ultra-Fast Retrieval with 64-Dimension Truncation [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fmatryoshka_representation_learning_sentencetransformers_msmarco_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F11\u002Fhow-to-build-a-matryoshka-optimized-sentence-embedding-model-for-ultra-fast-retrieval-with-64-dimension-truncation\u002F)\n\n▶ A Coding Implementation to Establish Rigorous Prompt Versioning and Regression Testing Workflows for Large Language Models using MLflow [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMLFlow%20for%20LLM%20Evaluation\u002FPrompt_Versioning_and_Regression_Testing_for_LLMs_with_MLflow_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F08\u002Fa-coding-implementation-to-establish-rigorous-prompt-versioning-and-regression-testing-workflows-for-large-language-models-using-mlflow\u002F)\n\n▶ A Coding Implementation to Automating LLM Quality Assurance with DeepEval, Custom Retrievers, and LLM-as-a-Judge Metrics [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Evaluation\u002Frag_deepeval_quality_benchmarking_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F25\u002Fa-coding-implementation-to-automating-llm-quality-assurance-with-deepeval-custom-retrievers-and-llm-as-a-judge-metrics\u002F)\n\n▶ How to Implement Functional Components of Transformer and Mini-GPT Model from Scratch Using Tinygrad to Understand Deep Learning Internals [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Ftinygrad_transformer_minigpt_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F25\u002Fhow-to-implement-functional-components-of-transformer-and-mini-gpt-model-from-scratch-using-tinygrad-to-understand-deep-learning-internals\u002F)\n\n▶ An Implementation of Fully Traced and Evaluated Local LLM Pipeline Using Opik for Transparent, Measurable, and Reproducible AI Workflows [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fopik_local_llm_tracing_and_evaluation_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F21\u002Fan-implementation-of-fully-traced-and-evaluated-local-llm-pipeline-using-opik-for-transparent-measurable-and-reproducible-ai-workflows\u002F)\n\n▶ A Coding Implementation to Build a Transformer-Based Regression Language Model to Predict Continuous Values from Text [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fregression_language_model_transformer_rlm_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F04\u002Fa-coding-implementation-to-build-a-transformer-based-regression-language-model-to-predict-continuous-values-from-text\u002F)\n\n▶ An Implementation on Building Advanced Multi-Endpoint Machine Learning APIs with LitServe: Batching, Streaming, Caching, and Local Inference [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fadvanced_litserve_multi_endpoint_api_tutorial_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F24\u002Fan-implementation-on-building-advanced-multi-endpoint-machine-learning-apis-with-litserve-batching-streaming-caching-and-local-inference\u002F)\n\n▶ Ivy Framework Agnostic Machine Learning Build, Transpile, and Benchmark Across All Major Backends [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002FAdvanced_Ivy_Framework_Agnostic_ML_Tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F13\u002Fivy-framework-agnostic-machine-learning-build-transpile-and-benchmark-across-all-major-backends\u002F)\n\n▶ A Coding Guide to Master Self-Supervised Learning with Lightly AI for Efficient Data Curation and Active Learning [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Flightly_ai_self_supervised_active_learning_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F11\u002Fa-coding-guide-to-master-self-supervised-learning-with-lightly-ai-for-efficient-data-curation-and-active-learning\u002F)\n\n▶ Building and Optimizing Intelligent Machine Learning Pipelines with TPOT for Complete Automation and Performance Enhancement [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Ftpot_advanced_pipeline_optimization_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F29\u002Fbuilding-and-optimizing-intelligent-machine-learning-pipelines-with-tpot-for-complete-automation-and-performance-enhancement\u002F)\n\n▶ A Coding Implementation to Build a Complete Self-Hosted LLM Workflow with Ollama, REST API, and Gradio Chat Interface [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fself_hosted_llm_ollama_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F19\u002Fa-coding-implementation-to-build-a-complete-self-hosted-llm-workflow-with-ollama-rest-api-and-gradio-chat-interface\u002F)\n\n▶ How to Test an OpenAI Model Against Single-Turn Adversarial Attacks Using deepteam [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAdversarial%20Attacks\u002FSingle-Turn%20Attacks.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F17\u002Fhow-to-test-an-openai-model-against-single-turn-adversarial-attacks-using-deepteam\u002F)\n\n▶ Using RouteLLM to Optimize LLM Usage [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FGPT-5\u002FRouteLLM.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F10\u002Fusing-routellm-to-optimize-llm-usage\u002F)\n\n▶ Tutorial: Exploring SHAP-IQ Visualizations[Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FSHAP-IQ\u002FSHAP_IQ_Visuals.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F03\u002Ftutorial-exploring-shap-iq-visualizations\u002F)\n\n▶ Building an End-to-End Object Tracking and Analytics System with Roboflow Supervision [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Froboflow_supervision_advanced_tracking_analytics_pipeline_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F03\u002Fbuilding-an-end-to-end-object-tracking-and-analytics-system-with-roboflow-supervision\u002F)\n\n▶ Getting Started with Microsoft’s Presidio: A Step-by-Step Guide to Detecting and Anonymizing Personally Identifiable Information PII in Text [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FPresidio.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F24\u002Fgetting-started-with-microsofts-presidio-a-step-by-step-guide-to-detecting-and-anonymizing-personally-identifiable-information-pii-in-text\u002F)\n\n▶ Build a Groundedness Verification Tool Using Upstage API and LangChain [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FUpstage_Groundedness_Check_Tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F24\u002Fbuild-a-groundedness-verification-tool-using-upstage-api-and-langchain\u002F)\n\n▶ A Coding Guide to Build a Production-Ready Asynchronous Python SDK with Rate Limiting, In-Memory Caching, and Authentication [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fadvanced_async_python_sdk_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F23\u002Fa-coding-guide-to-build-a-production-ready-asynchronous-python-sdk-with-rate-limiting-in-memory-caching-and-authentication\u002F)\n\n▶ Building High-Performance Financial Analytics Pipelines with Polars: Lazy Evaluation, Advanced Expressions, and SQL Integration [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fpolars_sql_analytics_pipeline_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F17\u002Fbuilding-high-performance-financial-analytics-pipelines-with-polars-lazy-evaluation-advanced-expressions-and-sql-integration\u002F)\n\n▶ Building AI-Powered Applications Using the Plan → Files → Code Workflow in TinyDev [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Ftinydev_gemini_implementation_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F14\u002Fbuilding-ai-powered-applications-using-the-plan-%e2%86%92-files-%e2%86%92-code-workflow-in-tinydev\u002F)\n\n▶ A Comprehensive Coding Tutorial for Advanced SerpAPI Integration with Google Gemini-1.5-Flash for Advanced Analytics [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fadvanced_serpapi_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F06\u002Fa-comprehensive-coding-tutorial-for-advanced-serpapi-integration-with-google-gemini-1-5-flash-for-advanced-analytics\u002F)\n\n▶ Build a Secure AI Code Execution Workflow Using Daytona SDK [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fdaytona_secure_ai_code_execution_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F12\u002Fbuild-a-secure-ai-code-execution-workflow-using-daytona-sdk\u002F)\n\n▶ A Coding Guide Implementing ScrapeGraph and Gemini AI for an Automated, Scalable, Insight-Driven Competitive Intelligence and Market Analysis Workflow  [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FCompetitive_Analysis_with_ScrapeGraph_Gemini_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F02\u002Fa-coding-guide-implementing-scrapegraph-and-gemini-ai-for-an-automated-scalable-insight-driven-competitive-intelligence-and-market-analysis-workflow\u002F)\n\n▶ A Coding Implementation to Build an Interactive Transcript and PDF Analysis with Lyzr Chatbot Framework [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FLyzr_Chatbot_Framework_Implementation_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F27\u002Fa-coding-implementation-to-build-an-interactive-transcript-and-pdf-analysis-with-lyzr-chatbot-framework\u002F)\n\n▶ Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)  [Notebook](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FAutoGen_TeamTool_RoundRobin_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F25\u002Fstep-by-step-guide-to-creating-synthetic-data-using-the-synthetic-data-vault-sdv\u002F)\n\n▶ Creating a Knowledge Graph Using an LLM  [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Ftree\u002Fmain\u002FMirascope) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F28\u002Fcreating-a-knowledge-graph-using-an-llm\u002F)\n\n---\n\n### Voice AI\n\n▶ How to Design a Fully Streaming Voice Agent with End-to-End Latency Budgets, Incremental ASR, LLM Streaming, and Real-Time TTS [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FVoice%20AI\u002Fstreaming_voice_agent_latency_budgets_end_to_end_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F19\u002Fhow-to-design-a-fully-streaming-voice-agent-with-end-to-end-latency-budgets-incremental-asr-llm-streaming-and-real-time-tts\u002F)\n\n▶ How to Build an Agentic Voice AI Assistant that Understands, Reasons, Plans, and Responds through Autonomous Multi-Step Intelligence [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FVoice%20AI\u002Fagentic_voice_ai_autonomous_assistant_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F08\u002Fhow-to-build-an-agentic-voice-ai-assistant-that-understands-reasons-plans-and-responds-through-autonomous-multi-step-intelligence\u002F)\n\n▶ How to Build an Advanced Voice AI Pipeline with WhisperX for Transcription, Alignment, Analysis, and Export? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FVoice%20AI\u002Fvoice_ai_whisperx_advanced_tutorial_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F02\u002Fhow-to-build-an-advanced-voice-ai-pipeline-with-whisperx-for-transcription-alignment-analysis-and-export\u002F)\n\n▶ Building a Speech Enhancement and Automatic Speech Recognition (ASR) Pipeline in Python Using SpeechBrain  [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fguide_to_building_an_end_to_end_speech_enhancement_and_recognition_pipeline_with_speechbrain.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F09\u002Fbuilding-a-speech-enhancement-and-automatic-speech-recognition-asr-pipeline-in-python-using-speechbrain\u002F)\n\n▶ How to Build an Advanced End-to-End Voice AI Agent Using Hugging Face Pipelines? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fhow_to_build_an_advanced_end_to_end_voice_ai_agent_using_hugging_face_pipelines.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F17\u002Fhow-to-build-an-advanced-end-to-end-voice-ai-agent-using-hugging-face-pipelines\u002F)\n\n---\n\n### RAG\n\n▶ How Tree-KG Enables Hierarchical Knowledge Graphs for Contextual Navigation and Explainable Multi-Hop Reasoning Beyond Traditional RAG [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FRAG\u002Ftree_kg_hierarchical_knowledge_graph_multi_hop_reasoning_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F27\u002Fhow-tree-kg-enables-hierarchical-knowledge-graphs-for-contextual-navigation-and-explainable-multi-hop-reasoning-beyond-traditional-rag\u002F)\n\n▶ How to Reduce Cost and Latency of Your RAG Application Using Semantic LLM Caching [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FRAG\u002FSemantic_Caching.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F11\u002Fhow-to-reduce-cost-and-latency-of-your-rag-application-using-semantic-llm-caching\u002F)\n\n▶ How to Build an Agentic Decision-Tree RAG System with Intelligent Query Routing, Self-Checking, and Iterative Refinement? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FRAG\u002Fagentic_rag_with_routing_and_self_check_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F27\u002Fhow-to-build-an-agentic-decision-tree-rag-system-with-intelligent-query-routing-self-checking-and-iterative-refinement\u002F)\n\n▶ How to Design a Fully Functional Enterprise AI Assistant with Retrieval Augmentation and Policy Guardrails Using Open Source AI Models [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FRAG\u002Fenterprise_ai_rag_guardrails_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F22\u002Fhow-to-design-a-fully-functional-enterprise-ai-assistant-with-retrieval-augmentation-and-policy-guardrails-using-open-source-ai-models\u002F)\n\n▶ How to Evaluate Your RAG Pipeline with Synthetic Data? [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FRAG\u002Frag_evaluation.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F13\u002Fhow-to-evaluate-your-rag-pipeline-with-synthetic-data\u002F)\n\n\n---\n\n### Computer Vision\n\n▶ [Tutorial] A Coding Guide to Markerless 3D Human Kinematics with Pose2Sim, RTMPose, and OpenSim [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FComputer%20Vision\u002Fpose2sim_markerless_3d_kinematics_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F10\u002Fa-coding-guide-to-markerless-3d-human-kinematics-with-pose2sim-rtmpose-and-opensim\u002F)\n\n▶ [Tutorial] How to Build a Netflix VOID Video Object Removal and Inpainting Pipeline with CogVideoX, Custom Prompting, and End-to-End Sample Inference [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FComputer%20Vision\u002Fnetflix_void_video_object_removal_inpainting_pipeline_with_cogvideox_and_sample_inference.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F05\u002Fhow-to-build-a-netflix-void-video-object-removal-and-inpainting-pipeline-with-cogvideox-custom-prompting-and-end-to-end-sample-inference\u002F)\n\n▶ [Tutorial] Building a Visual Document Retrieval Pipeline with ColPali and Late Interaction Scoring [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FComputer%20Vision\u002Fcolpali_visual_retrieval_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F18\u002Ftutorial-building-a-visual-document-retrieval-pipeline-with-colpali-and-late-interaction-scoring\u002F)\n\n▶ A Coding Guide to Implement Advanced Hyperparameter Optimization with Optuna using Pruning Multi-Objective Search, Early Stopping, and Deep Visual Analysis [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FComputer%20Vision\u002Foptuna_advanced_hpo_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F17\u002Fa-coding-guide-to-implement-advanced-hyperparameter-optimization-with-optuna-using-pruning-multi-objective-search-early-stopping-and-deep-visual-analysis\u002F)\n\n\n### Security\n\n▶ How to Build Multi-Layered LLM Safety Filters to Defend Against Adaptive, Paraphrased, and Adversarial Prompt Attacks [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAdversarial%20Attacks\u002Frobust_llm_safety_filters_adaptive_attack_defense_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F02\u002Fhow-to-build-multi-layered-llm-safety-filters-to-defend-against-adaptive-paraphrased-and-adversarial-prompt-attacks\u002F)\n\n▶ A Coding Guide to Demonstrate Targeted Data Poisoning Attacks in Deep Learning by Label Flipping on CIFAR-10 with PyTorch [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FSecurity\u002Ftargeted_data_poisoning_label_flipping_cifar10_pytorch_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F11\u002Fa-coding-guide-to-demonstrate-targeted-data-poisoning-attacks-in-deep-learning-by-label-flipping-on-cifar-10-with-pytorch\u002F)\n\n▶ How to Build a Multi-Turn Crescendo Red-Teaming Pipeline to Evaluate and Stress-Test LLM Safety Using Garak [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAdversarial%20Attacks\u002Fmultiturn_crescendo_llm_safety_evaluation_with_garak_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F13\u002Fhow-to-build-a-multi-turn-crescendo-red-teaming-pipeline-to-evaluate-and-stress-test-llm-safety-using-garak\u002F)\n\n\n### AI Infrastructure\n\n▶ How to Build High-Performance GPU-Accelerated Simulations and Differentiable Physics Workflows Using NVIDIA Warp Kernels [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FScientific%20Computing\u002Fnvidia_warp_gpu_simulation_and_differentiable_physics_Marktechpost.ipynb) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F16\u002Fhow-to-build-high-performance-gpu-accelerated-simulations-and-differentiable-physics-workflows-using-nvidia-warp-kernels\u002F)\n\n▶ How to Build an Elastic Vector Database with Consistent Hashing, Sharding, and Live Ring Visualization for RAG Systems [Codes](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Felastic_vector_db_consistent_hashing_rag_marktechpost.py) [Tutorial](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F25\u002Fhow-to-build-an-elastic-vector-database-with-consistent-hashing-sharding-and-live-ring-visualization-for-rag-systems\u002F)\n\n","🤝 表达你的支持——如果你喜欢这些内容，就给个⭐️吧！\n\n## 星标历史\n\n[![星标历史图表](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMarktechpost_AI-Tutorial-Codes-Included_readme_c30713c16b03.png)](https:\u002F\u002Fwww.star-history.com\u002F#Marktechpost\u002FAI-Tutorial-Codes-Included&type=date&legend=top-left)\n\n\n\n# AI教程\u002F实现与笔记本\n\n### 索引 \n\n* [自主智能与智能体](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks?tab=readme-ov-file#agentic-ai-and-agents)\n* [机器学习与数据科学](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#machine-learning--data-science)\n* [MCP指南](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks?tab=readme-ov-file#mcps-guides)\n* [大语言模型及其他AI板块](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included?tab=readme-ov-file#llmsml-and-other-ai-section)\n* [语音AI](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#voice-ai)\n* [RAG](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#rag)\n* [计算机视觉](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#computer-vision)\n* [安全](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#security)\n* [AI基础设施](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FREADME.md#ai-infrastructure)\n\n---\n  \n\n### 自主智能与智能体\n\n▶ 如何在单次Gemini API调用中结合谷歌搜索、谷歌地图和自定义函数，并实现上下文循环、并行工具ID及多步自主链？[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fgemini3_tool_combination_maps_grounding_context_circulation_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F07\u002Fhow-to-combine-google-search-google-maps-and-custom-functions-in-a-single-gemini-api-call-with-context-circulation-parallel-tool-ids-and-multi-step-agentic-chains\u002F)\n\n▶ 如何使用Z.AI GLM-5构建生产级自主系统，利用思考模式、工具调用、流式传输及多轮工作流？[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fglm5_agentic_systems_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F03\u002Fhow-to-build-production-ready-agentic-systems-with-z-ai-glm-5-using-thinking-mode-tool-calling-streaming-and-multi-turn-workflows\u002F)\n\n▶ 如何使用ReAct智能体、自定义工具、多智能体辩论、结构化输出以及并发管道构建生产就绪的AgentScope工作流？[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20Workflows\u002Fagentscope_production_agent_workflows_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F01\u002Fhow-to-build-production-ready-agentscope-workflows-with-react-agents-custom-tools-multi-agent-debate-structured-output-and-concurrent-pipelines\u002F)\n\n▶ 如何使用A-Evolve基于基准测试、技能、记忆和工作空间突变构建并进化一个自定义的OpenAI智能体？[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fa_evolve_openai_agent_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F31\u002Fhow-to-build-and-evolve-a-custom-openai-agent-with-a-evolve-using-benchmarks-skills-memory-and-workspace-mutations\u002F)\n\n▶ 如何使用CAI构建先进的网络安全AI智能体，利用工具、护栏、交接机制及多智能体工作流？[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fcai_cybersecurity_ai_agents_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F29\u002Fhow-to-build-advanced-cybersecurity-ai-agents-with-cai-using-tools-guardrails-handoffs-and-multi-agent-workflows\u002F)\n\n▶ 一份深入探索nanobot完整智能体流水线的编码指南，从工具和内存的连接到技能、子智能体以及Cron调度。[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fnanobot_deep_dive_build_ai_agent_from_inside_out_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F28\u002Fa-coding-guide-to-exploring-nanobots-full-agent-pipeline-from-wiring-up-tools-and-memory-to-skills-subagents-and-cron-scheduling\u002F)\n\n▶ 将IWE的上下文桥以AI驱动的知识图谱形式实现，结合自主RAG、OpenAI函数调用和图遍历。[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fiwe_knowledge_graph_ai_agents_agentic_rag_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F27\u002Fan-implementation-of-iwes-context-bridge-as-an-ai-powered-knowledge-graph-with-agentic-rag-openai-function-calling-and-graph-traversal\u002F)\n\n▶ 如何使用MolmoWeb-4B构建一个基于视觉引导的网络AI智能体，利用多模态推理和动作预测？[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fmolmoweb_multimodal_web_agent_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F25\u002Fhow-to-build-a-vision-guided-web-ai-agent-with-molmoweb-4b-using-multimodal-reasoning-and-action-prediction\u002F)\n\n▶ 一种使用OpenSpace设计自我进化技能引擎的编码实现，用于技能学习、令牌效率及集体智慧。[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fopenspace_self_evolving_skill_evolution_engine_token_efficiency_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F24\u002Fa-coding-implementation-to-design-self-evolving-skill-engine-with-openspace-for-skill-learning-token-efficiency-and-collective-intelligence\u002F)\n\n▶ 如何设计一个能够自动化Google Colab工作流的生产就绪AI智能体，利用Colab-MCP、MCP工具、FastMCP及内核执行？[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMCP%20Codes\u002FWiring_AI_Agents_to_Google_Colab_MCP_Deep_Dive_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F23\u002Fhow-to-design-a-production-ready-ai-agent-that-automates-google-colab-workflows-using-colab-mcp-mcp-tools-fastmcp-and-kernel-execution\u002F)\n\n▶ 使用RLax JAX Haiku和Optax从零开始实现深度Q学习（DQN），训练CartPole强化学习智能体。[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FReinforcement%20learning\u002Frlax_dqn_cartpole_jax_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F22\u002Fimplementing-deep-q-learning-dqn-from-scratch-using-rlax-jax-haiku-and-optax-to-train-a-cartpole-reinforcement-learning-agent\u002F)\n\n▶ 一段代码实现，展示 ClawTeam 如何利用 OpenAI 函数调用进行多智能体 swarm 协同编排 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FClawTeam_Agent_Swarm_Intelligence_OpenAI_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F20\u002Fa-coding-implementation-showcasing-clawteams-multi-agent-swarm-orchestration-with-openai-function-calling\u002F)\n\n▶ 一段代码实现，使用 OpenClaw 网关策略引擎、审批工作流和可审计的智能体执行来设计企业级 AI 治理系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fopenclaw_enterprise_ai_governance_gateway_approval_workflows_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F15\u002Fa-coding-implementation-to-design-an-enterprise-ai-governance-system-using-openclaw-gateway-policy-engines-approval-workflows-and-auditable-agent-execution\u002F)\n\n▶ 如何在 Google Colab 中利用 Andrej Karpathy 的 AutoResearch 框架构建自主机器学习研究闭环，用于超参数搜索与实验跟踪 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fautoresearch_autonomous_ml_research_colab_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F12\u002Fhow-to-build-an-autonomous-machine-learning-research-loop-in-google-colab-using-andrej-karpathys-autoresearch-framework-for-hyperparameter-discovery-and-experiment-tracking\u002F)\n\n▶ 如何设计一个具备部分推理、在线重规划及动态环境中运行时自适应能力的流式决策智能体 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fstreaming_decision_agent_online_replanning_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F11\u002Fhow-to-design-a-streaming-decision-agent-with-partial-reasoning-online-replanning-and-reactive-mid-execution-adaptation-in-dynamic-environments\u002F)\n\n▶ 如何构建一个能够自动构建、实例化并优化特定任务 AI 智能体的自设计元智能体 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fmeta_agent_auto_designer_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F10\u002Fhow-to-build-a-self-designing-meta-agent-that-automatically-constructs-instantiates-and-refines-task-specific-ai-agents\u002F)\n\n▶ 如何构建一个具有内部批评机制、自我一致性推理和不确定性估计的风险感知 AI 智能体，以实现可靠的决策 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fcritic_augmented_risk_aware_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F09\u002Fhow-to-build-a-risk-aware-ai-agent-with-internal-critic-self-consistency-reasoning-and-uncertainty-estimation-for-reliable-decision-making\u002F)\n\n▶ 构建下一代代理型 AI：一套完整的框架，用于基于认知蓝图的运行时智能体，配备记忆工具与验证机制 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fcognitive_blueprint_runtime_agents_auton_framework_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F07\u002Fbuilding-next-gen-agentic-ai-a-complete-framework-for-cognitive-blueprint-driven-runtime-agents-with-memory-tools-and-validation\u002F)\n\n▶ 如何设计一个高级的“思维之树”多分支推理智能体，结合束搜索、启发式评分和深度限制剪枝 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Ftree_of_thoughts_multi_branch_reasoning_agent_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F05\u002Fhow-to-design-an-advanced-tree-of-thoughts-multi-branch-reasoning-agent-with-beam-search-heuristic-scoring-and-depth-limited-pruning\u002F)\n\n▶ 如何构建一个类似 EverMem 的持久化 AI 智能体操作系统，具备层次化记忆、FAISS 向量检索、SQLite 存储以及自动化记忆整合功能 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fevermem_persistent_agent_os_faiss_sqlite_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F04\u002Fhow-to-build-an-evermem-style-persistent-ai-agent-os-with-hierarchical-memory-faiss-vector-retrieval-sqlite-storage-and-automated-memory-consolidation\u002F)\n\n▶ 如何使用 LangGraph 结构化消息总线、ACP 日志记录和持久共享状态架构设计生产级多智能体通信系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgent%20Communication%20Protocol\u002FGetting%20Started\u002Flanggraph_acp_structured_message_bus_multi_agent_system_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F01\u002Fhow-to-design-a-production-grade-multi-agent-communication-system-using-langgraph-structured-message-bus-acp-logging-and-persistent-shared-state-architecture\u002F)\n\n▶ 一段代码实现，利用开源大语言模型构建具备工具执行与结构化多智能体推理能力的分层规划型 AI 智能体 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fhierarchical_planner_ai_agent_open_source_llm_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F27\u002Fa-coding-implementation-to-build-a-hierarchical-planner-ai-agent-using-open-source-llms-with-tool-execution-and-structured-multi-agent-reasoning\u002F)\n\n▶ 如何使用 Griptape 构建生产级客户支持自动化流水线，结合确定性工具与代理型推理 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20Workflows\u002Fgriptape_customer_support_automation_pipeline_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F23\u002Fhow-to-build-a-production-grade-customer-support-automation-pipeline-with-griptape-using-deterministic-tools-and-agentic-reasoning\u002F)\n\n▶ 如何设计一个瑞士军刀式的研究智能体，具备工具使用型 AI、网络搜索、PDF 分析、视觉处理及自动化报告功能 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fswiss_army_knife_research_agent_tool_using_ai_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F20\u002Fhow-to-design-a-swiss-army-knife-research-agent-with-tool-using-ai-web-search-pdf-analysis-vision-and-automated-reporting\u002F)\n\n▶ 如何设计一个基于工具驱动的路线优化代理型工作流，实现确定性计算与结构化输出 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20Workflows\u002Fagentic_workflow_tool_driven_route_optimization_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F21\u002Fhow-to-design-an-agentic-workflow-for-tool-driven-route-optimization-with-deterministic-computation-and-structured-outputs\u002F)\n\n▶ 使用 PydanticAI 构建防弹型智能体工作流的代码实现：采用严格模式、工具注入和模型无关的执行 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20Workflows\u002Fpydanticai_bulletproof_agentic_workflows_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F19\u002Fa-coding-implementation-to-build-bulletproof-agentic-workflows-with-pydanticai-using-strict-schemas-tool-injection-and-model-agnostic-execution\u002F)\n\n▶ 设计具有长期记忆、语义回忆和自适应练习生成的状态感知辅导智能体的代码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fstateful_tutor_long_term_memory_agent_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F15\u002Fa-coding-implementation-to-design-a-stateful-tutor-agent-with-long-term-memory-semantic-recall-and-adaptive-practice-generation\u002F)\n\n▶ 如何构建用于长期 AI 推理的自组织智能体记忆系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fself_organizing_agent_memory_long_horizon_reasoning_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F14\u002Fhow-to-build-a-self-organizing-agent-memory-system-for-long-term-ai-reasoning\u002F)\n\n▶ 如何构建带有类型化模式、动态上下文注入和智能体链式的原子级 RAG 流水线 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fatomic_agents_advanced_rag_pipeline_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F11\u002Fhow-to-build-an-atomic-agents-rag-pipeline-with-typed-schemas-dynamic-context-injection-and-agent-chaining\u002F)\n\n▶ 如何构建具备混合检索、以出处为先的引用、修复循环和情景记忆的生产级智能体 AI 系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002FUltra_Agentic_AI_Hybrid_Retrieval_Guardrails_Episodic_Memory_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F06\u002Fhow-to-build-a-production-grade-agentic-ai-system-with-hybrid-retrieval-provenance-first-citations-repair-loops-and-episodic-memory\u002F)\n\n▶ 如何在不损失准确性的情况下，通过动态修剪多条思维链路径来构建高效的智能体推理系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fagentic_chain_of_thought_pruning_dynamic_reasoning_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F04\u002Fhow-to-build-efficient-agentic-reasoning-systems-by-dynamically-pruning-multiple-chain-of-thought-paths-without-losing-accuracy\u002F)\n\n\n▶ 使用 d3rlpy 和固定历史数据，通过保守 Q 学习离线训练安全关键强化学习智能体的代码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FReinforcement%20learning\u002Foffline_safety_critical_rl_conservative_q_learning_d3rlpy_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F03\u002Fa-coding-implementation-to-train-safety-critical-reinforcement-learning-agents-offline-using-conservative-q-learning-with-d3rlpy-and-fixed-historical-data\u002F)\n\n▶ 如何构建具备短期、长期和情景记忆的记忆驱动型 AI 智能体 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fmemory_engineering_short_term_long_term_episodic_agents_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F01\u002Fhow-to-build-memory-driven-ai-agents-with-short-term-long-term-and-episodic-memory\u002F)\n\n▶ 如何设计具有宪法式 AI 的自我反思双智能体治理系统，以实现安全合规的金融运营 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fconstitutional_dual_agent_financial_governance_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F28\u002Fhow-to-design-self-reflective-dual-agent-governance-systems-with-constitutional-ai-for-secure-and-compliant-financial-operations\u002F)\n\n▶ 基于 Haystack 的多智能体系统如何从端到端检测事件、分析指标与日志，并生成符合生产标准的事件报告 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fmulti_agent_incident_response_haystack_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F26\u002Fhow-a-haystack-powered-multi-agent-system-detects-incidents-investigates-metrics-and-logs-and-produces-production-grade-incident-reviews-end-to-end\u002F)\n\n▶ 在令牌、延迟和工具调用预算受限的情况下，AI 智能体如何做出决策？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fcost_aware_planning_agent_budget_constrained_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F23\u002Fhow-an-ai-agent-chooses-what-to-do-under-tokens-latency-and-tool-call-budget-constraints\u002F)\n\n▶ 使用 LangGraph 中的点对点批评循环构建安摩伊风格半中心化智能体系统的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fanemoi_semi_centralized_peer_critic_loop_langgraph_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F20\u002Fa-coding-guide-to-anemoi-style-semi-centralized-agentic-systems-using-peer-to-peer-critic-loops-in-langgraph\u002F)\n\n▶ 如何使用 LlamaIndex 和 OpenAI，结合检索、工具使用以及自动化质量检查，构建可自我评估的智能体 AI 系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fagentic_llamaindex_rag_self_evaluation_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F17\u002Fhow-to-build-a-self-evaluating-agentic-ai-system-with-llamaindex-and-openai-using-retrieval-tool-use-and-automated-quality-checks\u002F)\n\n▶ 如何构建具备人工介入控制的安全自主医疗收入周期管理事前授权智能体 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fautonomous_prior_auth_agent_healthcare_rcm_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F15\u002Fhow-to-build-a-safe-autonomous-prior-authorization-agent-for-healthcare-revenue-cycle-management-with-human-in-the-loop-controls\u002F)\n\n▶ 如何使用 LangGraph 和 OpenAI，结合自适应深思熟虑、记忆图谱和反思循环，设计智能体 AI 架构 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fagentic_ai_with_langgraph_adaptive_memory_reflexion_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F06\u002Fhow-to-design-an-agentic-ai-architecture-with-langgraph-and-openai-using-adaptive-deliberation-memory-graphs-and-reflexion-loops\u002F)\n\n▶ 使用 AgentScope 和 OpenAI 设计并编排基于 ReAct 的高级多智能体工作流的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentscope_multi_agent_incident_response_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F04\u002Fa-coding-guide-to-design-and-orchestrate-advanced-react-based-multi-agent-workflows-with-agentscope-and-openai\u002F)\n\n▶ 如何使用 OpenAI Swarm 和工具增强型智能体构建生产就绪的多智能体事件响应系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fopenai_swarm_multi_agent_incident_response_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F03\u002Fhow-to-build-a-production-ready-multi-agent-incident-response-system-using-openai-swarm-and-tool-augmented-agents\u002F)\n\n▶ 使用 Strands 对使用工具的智能体进行红队测试，并在运行时强制执行安全性的自测型智能体 AI 系统的编码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fstrands_agentic_red_teaming_tool_injection_harness_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F02\u002Fa-coding-implementation-to-build-a-self-testing-agentic-ai-system-using-strands-to-red-team-tool-using-agents-and-enforce-safety-at-runtime\u002F)\n\n▶ 如何使用 LangGraph 设计具有两阶段提交、人工干预和安全回滚功能的事务型智能体 AI 系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Ftransactional_agentic_ai_langgraph_two_phase_commit_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F31\u002Fhow-to-design-transactional-agentic-ai-systems-with-langgraph-using-two-phase-commit-human-interrupts-and-safe-rollbacks\u002F)\n\n▶ 如何使用 CAMEL 构建强大的多智能体流水线，包含规划、网络增强推理、批评与反馈以及持久化存储 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fcamel_multi_agent_research_pipeline_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F29\u002Fhow-to-build-a-robust-multi-agent-pipeline-using-camel-with-planning-web-augmented-reasoning-critique-and-persistent-memory\u002F)\n\n▶ 如何使用 PydanticAI 构建以合同为先的智能体决策系统，用于风险敏感且符合政策的企业级 AI 应用 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fpydantic_ai_contract_first_agentic_decision_system_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F28\u002Fhow-to-build-contract-first-agentic-decision-systems-with-pydanticai-for-risk-aware-policy-compliant-enterprise-ai\u002F)\n\n▶ 如何使用 GraphBit 构建生产级别的智能体工作流，采用确定性工具、经过验证的执行图以及可选的 LLM 编排 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20Workflows\u002Fgraphbit_production_agentic_workflows_offline_to_llm_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F27\u002Fhow-to-build-production-grade-agentic-workflows-with-graphbit-using-deterministic-tools-validated-execution-graphs-and-optional-llm-orchestration\u002F)\n\n▶ 使用基于图的仿真技术构建具备路径规划、动态拍卖和实时可视化功能的自主多智能体物流系统的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fagentic_logistics_swarm_simulation_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F24\u002Fa-coding-guide-to-build-an-autonomous-multi-agent-logistics-system-with-route-planning-dynamic-auctions-and-real-time-visualization-using-graph-based-simulation\u002F)\n\n▶ 如何使用 SmolAgents 和通义千问模型构建完全自主的本地车队维护分析智能体 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fsmolagents_fleet_maintenance_autonomous_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F22\u002Fhow-to-build-a-fully-autonomous-local-fleet-maintenance-analysis-agent-using-smolagents-and-qwen-model\u002F)\n\n▶ 如何构建具备智能观察与策略制定功能的主动式事前流失预防智能体 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fpreemptive_churn_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F23\u002Fhow-to-build-a-proactive-pre-emptive-churn-prevention-agent-with-intelligent-observation-and-strategy-formation\u002F)\n\n▶ 在 Gemini 中设计完整智能体工作流以实现自动化医疗证据收集和事前授权提交的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002FGemini_Agentic_Medical_Authorization_Workflow_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F20\u002Fa-coding-guide-to-design-a-complete-agentic-workflow-in-gemini-for-automated-medical-evidence-gathering-and-prior-authorization-submission\u002F)\n\n▶ 如何使用 CrewAI 和 Gemini 编排完全自主的多智能体研究与写作流水线，实现实时智能协作 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fcrewai_multiagent_gemini_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F17\u002Fhow-to-orchestrate-a-fully-autonomous-multi-agent-research-and-writing-pipeline-using-crewai-and-gemini-for-real-time-intelligent-collaboration\u002F)\n\n▶ 使用 Gemini Flash、少样本选择和进化式指令搜索实现自动提示优化的完整工作流 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FPrompt%20Optimization\u002Fgemini_prompt_optimization_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F19\u002Fa-complete-workflow-for-automated-prompt-optimization-using-gemini-flash-few-shot-selection-and-evolutionary-instruction-search\u002F)\n\n▶ 如何设计基于 Gemini 的自我纠正型多智能体 AI 系统，配备语义路由、符号护栏和反射式编排 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fgemini_semantic_agent_orchestrator_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F15\u002Fhow-to-design-a-gemini-powered-self-correcting-multi-agent-ai-system-with-semantic-routing-symbolic-guardrails-and-reflexive-orchestration\u002F)\n\n▶ 如何使用 Griptape 工作流、Hugging Face 模型和模块化创意任务编排，设计一个完全本地化的智能体叙事流水线 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fgriptape_local_agentic_story_pipeline_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F12\u002Fhow-to-design-a-fully-local-agentic-storytelling-pipeline-using-griptape-workflows-hugging-face-models-and-modular-creative-task-orchestration\u002F)\n\n▶ 构建程序性记忆智能体的编码指南：该智能体能够随时间学习、存储、检索并以神经模块形式重用技能 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fprocedural_memory_agent_skill_learning_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F09\u002Fa-coding-guide-to-build-a-procedural-memory-agent-that-learns-stores-retrieves-and-reuses-skills-as-neural-modules-over-time\u002F)\n\n▶ 如何构建一个自适应元推理智能体，使其能够在快速、深度和工具驱动的思维策略之间动态选择 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fadaptive_meta_reasoning_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F06\u002Fhow-to-build-an-adaptive-meta-reasoning-agent-that-dynamically-chooses-between-fast-deep-and-tool-based-thinking-strategies\u002F)\n\n▶ 如何使用 TinyLlama 设计一个完全本地化的多智能体编排系统，实现智能任务分解与自主协作 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Flocal_multi_agent_manager_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F05\u002Fhow-to-design-a-fully-local-multi-agent-orchestration-system-using-tinyllama-for-intelligent-task-decomposition-and-autonomous-collaboration\u002F)\n\n▶ 如何构建一个元认知 AI 智能体，使其能够动态调整自身的推理深度以高效解决问题 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fmeta_cognitive_reasoning_controller_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F03\u002Fhow-to-build-a-meta-cognitive-ai-agent-that-dynamically-adjusts-its-own-reasoning-depth-for-efficient-problem-solving\u002F)\n\n▶ 使用控制平面架构设计安全、模块化且可扩展的工具驱动推理工作流的智能体 AI 系统编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fcontrol_plane_agentic_ai_system_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F28\u002Fa-coding-guide-to-design-an-agentic-ai-system-using-a-control-plane-architecture-for-safe-modular-and-scalable-tool-driven-reasoning-workflows\u002F)\n\n▶ 一种用于文学分析、假设生成、实验规划、仿真及科学报告的智能体 AI 框架的编码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fagentic_scientific_discovery_pipeline_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F27\u002Fa-coding-implementation-for-an-agentic-ai-framework-that-performs-literature-analysis-hypothesis-generation-experimental-planning-simulation-and-scientific-reporting\u002F)\n\n▶ 如何构建一个神经符号混合智能体，将逻辑规划与神经感知相结合，以实现稳健的自主决策 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fneuro_symbolic_hybrid_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F24\u002Fhow-to-build-a-neuro-symbolic-hybrid-agent-that-combines-logical-planning-with-neural-perception-for-robust-autonomous-decision-making\u002F)\n\n▶ 如何设计一个具备智能本地反馈、自适应决策和多智能体协调功能的微型强化学习环境交互智能体 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentic_multi_agent_rl_gridworld_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F22\u002Fhow-to-design-a-mini-reinforcement-learning-environment-acting-agent-with-intelligent-local-feedback-adaptive-decision-making-and-multi-agent-coordination\u002F)\n\n▶ 如何构建一个完全离线的多工具推理智能体，具备动态规划、错误恢复和智能函数路由功能 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Codes\u002Fadvanced_multitool_agentic_ai_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F20\u002Fhow-to-build-a-fully-offline-multi-tool-reasoning-agent-with-dynamic-planning-error-recovery-and-intelligent-function-routing\u002F)\n\n▶ 现代智能体 AI 系统中推理策略基准测试的综合实证框架实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentic_benchmarking_empirical_study_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F19\u002Fan-implementation-of-a-comprehensive-empirical-framework-for-benchmarking-reasoning-strategies-in-modern-agentic-ai-systems\u002F)\n\n▶ 如何构建一个具有课程式进展、自适应探索和元层级 UCB 规划的智能体深度强化学习系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentic_deep_rl_curriculum_ucb_meta_control_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F18\u002Fhow-to-build-an-agentic-deep-reinforcement-learning-system-with-curriculum-progression-adaptive-exploration-and-meta-level-ucb-planning\u002F)\n\n▶ 如何构建一种基于记忆的智能体 AI，通过情景式经验和语义模式持续学习，以实现长期自主运行 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fmemory_driven_agentic_ai_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F15\u002Fhow-to-build-memory-powered-agentic-ai-that-learns-continuously-through-episodic-experiences-and-semantic-patterns-for-long-term-autonomy\u002F)\n\n▶ 如何设计一个基于 spaCy 的高级多智能体推理系统，具备规划、反思、记忆和知识图谱功能 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fspacy_agentic_ai_system_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F14\u002Fhow-to-design-an-advanced-multi-agent-reasoning-system-with-spacy-featuring-planning-reflection-memory-and-knowledge-graphs\u002F)\n\n▶ 如何使用本地 Hugging Face 模型构建一个完全自验证的数据运维 AI 代理，实现自动化规划、执行和测试 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fself_verifying_dataops_agent_local_hf_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F13\u002Fhow-to-build-a-fully-self-verifying-data-operations-ai-agent-using-local-hugging-face-models-for-automated-planning-execution-and-testing\u002F)\n\n▶ 一种编码实现：构建具有可微分记忆、元学习和经验回放功能的神经记忆代理，以在动态环境中持续适应 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fneural_memory_agents_continual_learning_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F09\u002Fa-coding-implementation-to-build-neural-memory-agents-with-differentiable-memory-meta-learning-and-experience-replay-for-continual-adaptation-in-dynamic-environments\u002F)\n\n▶ 如何构建一个能够理解、推理、规划并响应的代理式语音 AI 助手，通过自主多步智能实现交互 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FVoice%20AI\u002Fagentic_voice_ai_autonomous_assistant_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F08\u002Fhow-to-build-an-agentic-voice-ai-assistant-that-understands-reasons-plans-and-responds-through-autonomous-multi-step-intelligence\u002F)\n\n▶ 构建一个多智能体系统，用于整合转录组、蛋白质组和代谢组数据的解读，并结合通路推理 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fmulti_agent_omics_integration_pipeline_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F07\u002Fbuild-a-multi-agent-system-for-integrated-transcriptomic-proteomic-and-metabolomic-data-interpretation-with-pathway-reasoning\u002F)\n\n▶ 如何构建一个模型原生代理，通过端到端强化学习学会内部规划、记忆和多工具推理 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002FModel_Native_Agentic_AI_End_to_End_RL_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F05\u002Fhow-to-build-a-model-native-agent-that-learns-internal-planning-memory-and-multi-tool-reasoning-through-end-to-end-reinforcement-learning\u002F)\n\n▶ 使用 Salesforce CodeGen 构建一个自主湿实验室实验方案规划与验证系统，用于代理式实验设计和安全优化 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fwetlab_protocol_planner_codegen_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F06\u002Fbuild-an-autonomous-wet-lab-protocol-planner-and-validator-using-salesforce-codegen-for-agentic-experiment-design-and-safety-optimization\u002F)\n\n▶ 如何设计一个具有衰减机制和自我评估功能的持久化内存及个性化代理式 AI 系统？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002FPersistent_Memory_Personalised_Agentic_AI_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F02\u002Fhow-to-design-a-persistent-memory-and-personalized-agentic-ai-system-with-decay-and-self-evaluation\u002F)\n\n▶ 如何利用轻量级 Qwen 模型设计一个自主多智能体数据与基础设施战略系统，实现高效的管道智能化？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentic_data_infrastructure_strategy_qwen_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F30\u002Fhow-to-design-an-autonomous-multi-agent-data-and-infrastructure-strategy-system-using-lightweight-qwen-models-for-efficient-pipeline-intelligence\u002F)\n\n▶ 如何构建一个功能齐全的计算机使用代理，利用本地 AI 模型进行思考、规划和执行虚拟操作 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FComputer_Use_Agent_Local_AI_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F25\u002Fhow-to-build-a-fully-functional-computer-use-agent-that-thinks-plans-and-executes-virtual-actions-using-local-ai-models\u002F)\n\n▶ 一种综合企业 AI 基准测试框架的编码实现，用于评估基于规则的 LLM 以及跨真实世界任务的混合代理式 AI 系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fenterprise_agentic_benchmarking_framework_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F01\u002Fa-coding-implementation-of-a-comprehensive-enterprise-ai-benchmarking-framework-to-evaluate-rule-based-llm-and-hybrid-agentic-ai-systems-across-real-world-tasks\u002F)\n\n▶ 如何通过开源模型，借助价值导向的推理和自我修正决策机制，构建符合伦理规范的自主代理？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FValue_Alignment_and_Ethics_in_Agentic_Systems_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F29\u002Fhow-to-build-ethically-aligned-autonomous-agents-through-value-guided-reasoning-and-self-correcting-decision-making-using-open-source-models\u002F)\n\n▶ 如何在自定义交易环境中使用 Stable-Baselines3 构建、训练并比较多个强化学习代理？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fadvanced_stable_baselines3_trading_agent_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F26\u002Fhow-to-build-train-and-compare-multiple-reinforcement-learning-agents-in-a-custom-trading-environment-using-stable-baselines3\u002F)\n\n▶ 我如何利用 AutoGen、LangChain 和 Hugging Face 构建智能多智能体系统，以展示实际的代理式 AI 工作流程？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FAgentic_AI_LangChain_AutoGen_HuggingFace_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F21\u002Fhow-i-built-an-intelligent-multi-agent-systems-with-autogen-langchain-and-hugging-face-to-demonstrate-practical-agentic-ai-workflows\u002F)\n\n▶ 使用 uAgent 构建一个功能齐全的多智能体市场的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fuagents_multi_agent_marketplace_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F23\u002Fa-coding-guide-to-build-a-fully-functional-multi-agent-marketplace-using-uagent\u002F)\n\n▶ 使用 Python 实现具有自我审计护栏、PII 去标识化和安全工具访问功能的 secure AI 代理 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fsecure_ai_agent_with_guardrails_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F12\u002Fa-coding-implementation-of-secure-ai-agent-with-self-auditing-guardrails-pii-redaction-and-safe-tool-access-in-python\u002F)\n\n▶ 认识 LangChain 的 DeepAgents 库，并通过一个实用示例了解 DeepAgents 的实际运行方式 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FLangchain_Deepagents.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F20\u002Fmeet-langchains-deepagents-library-and-a-practical-example-to-see-how-deepagents-actually-work-in-action\u002F)\n\n▶ 集成 LangChain 代理与 XGBoost 的智能对话式机器学习流水线，用于自动化数据科学工作流 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FLangChain_XGBoost_Agentic_Pipeline_Tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F07\u002Fan-intelligent-conversational-machine-learning-pipeline-integrating-langchain-agents-and-xgboost-for-automated-data-science-workflows\u002F)\n\n▶ 构建基于 AI 的密码学代理系统的编码指南：结合混合加密、数字签名和自适应安全情报 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FAI_Crypto_Agent_Secure_Comms_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F16\u002Fa-coding-guide-to-build-an-ai-powered-cryptographic-agent-system-with-hybrid-encryption-digital-signatures-and-adaptive-security-intelligence\u002F)\n\n▶ 如何构建具有动态策略和智能检索功能的高级代理式检索增强生成（RAG）系统？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagentic_rag_tutorial_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F30\u002Fhow-to-build-an-advanced-agentic-retrieval-augmented-generation-rag-system-with-dynamic-strategy-and-smart-retrieval\u002F)\n\n▶ 使用 CrewAI 和 Google Gemini 构建分层监督代理框架的编码指南，用于协调多代理工作流 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fsupervisor_framework_crewai_gemini_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F30\u002Fa-coding-guide-to-build-a-hierarchical-supervisor-agent-framework-with-crewai-and-google-gemini-for-coordinated-multi-agent-workflows\u002F)\n\n▶ 如何构建具备自然语言指令和交互式模拟功能的智能桌面自动化 AI 代理？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fai_desktop_automation_agent_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F26\u002Fhow-to-build-an-intelligent-ai-desktop-automation-agent-with-natural-language-commands-and-interactive-simulation\u002F)\n\n▶ 如何使用 Hugging Face 流水线构建先进的端到端语音 AI 代理？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fhow_to_build_an_advanced_end_to_end_voice_ai_agent_using_hugging_face_pipelines.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F17\u002Fhow-to-build-an-advanced-end-to-end-voice-ai-agent-using-hugging-face-pipelines\u002F)\n\n▶ 如何使用 Parlant 创建可靠的对话式 AI 代理？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fparlant.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F22\u002Fhow-to-create-reliable-conversational-ai-agents-using-parlant\u002F)\n\n▶ 如何使用 EasyOCR 和 OpenCV 在 Python 中构建多语言 OCR AI 代理？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fadvanced_ocr_ai_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F12\u002Fhow-to-build-a-multilingual-ocr-ai-agent-in-python-with-easyocr-and-opencv\u002F)\n\n▶ 如何构建具备稳定训练、自适应学习和智能决策能力的稳健型高级神经网络 AI 代理？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fadvanced_neural_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F13\u002Fhow-to-build-a-robust-advanced-neural-ai-agent-with-stable-training-adaptive-learning-and-intelligent-decision-making\u002F)\n\n▶ 构建具有多代理协作、上下文感知和 Gemini 集成的高级 MCP（模型上下文协议）代理 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FBuilding%20Advanced%20MCP%20Agents%20with%20Multi-Agent%20Coordination.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F10\u002Fbuilding-advanced-mcp-model-context-protocol-agents-with-multi-agent-coordination-context-awareness-and-gemini-integration\u002F)\n\n▶ 如何使用 Notte 和 Gemini 构建完整的多领域 AI 网页代理？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FBuild%20a%20Complete%20Multi-Domain%20AI%20Web%20Agent%20Using%20Notte%20and%20Gemini) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F08\u002Fhow-to-build-a-complete-multi-domain-ai-web-agent-using-notte-and-gemini\u002F)\n\n▶ 如何使用 Biopython 构建用于 DNA 和蛋白质分析的生物信息学 AI 代理？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FBioinformatics%20AI%20Agent%20with%20Biopython) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F07\u002Fhow-to-create-a-bioinformatics-ai-agent-using-biopython-for-dna-and-protein-analysis\u002F)\n\n▶ 使用 Microsoft Agent-Lightning 进行 AI 代理开发的分步指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fagent_lightning_prompt_optimization_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F31\u002Fstep-by-step-guide-to-ai-agent-development-using-microsoft-agent-lightning\u002F)\n\n▶ 如何构建具备摘要式短期记忆和基于向量的长期记忆的高级 AI 代理？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002FAdvanced%20AI%20Agent%20with%20Summarized%20Short%20Term%20and%20Vector-Based%20LongTerm%20Memory) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F02\u002Fhow-to-build-an-advanced-ai-agent-with-summarized-short-term-and-vector-based-long-term-memory\u002F)\n\n▶ 如何使用 LangGraph 构建对话式研究 AI 助手：步骤重放与时间旅行检查点 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Flanggraph_time_travel_research_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F31\u002Fhow-to-build-a-conversational-research-ai-agent-with-langgraph-step-replay-and-time-travel-checkpoints\u002F)\n\n▶ 如何利用 Gemini、DuckDuckGo API 和自动化报告功能构建多轮深度研究助手？[代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fdeep_research_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F28\u002Fhow-to-build-a-multi-round-deep-research-agent-with-gemini-duckduckgo-api-and-automated-reporting\u002F)\n\n▶ 使用 Hugging Face 模型构建受大脑启发的层次化推理 AI 助手的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fhrm_braininspired_ai_agent_huggingface_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F30\u002Fa-coding-guide-to-building-a-brain-inspired-hierarchical-reasoning-ai-agent-with-hugging-face-models\u002F)\n\n▶ 使用 Gemini 进行任务规划、检索、计算和自我反思的图结构 AI 助手完整代码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fgraphagent_gemini_advanced_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F23\u002Fa-full-code-implementation-to-design-a-graph-structured-ai-agent-with-gemini-for-task-planning-retrieval-computation-and-self-critique\u002F)\n\n▶ 在本地使用 MLE-Agent 和 Ollama 构建可靠的端到端机器学习流水线 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fmle_agent_ollama_local_pipeline_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F25\u002Fbuilding-a-reliable-end-to-end-machine-learning-pipeline-using-mle-agent-and-ollama-locally\u002F)\n\n▶ 使用 Pipecat 和 HuggingFace 构建模块化对话式 AI 助手的实施指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fpipecat_huggingface_implementation_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F13\u002Fan-implementation-guide-to-build-a-modular-conversational-ai-agent-with-pipecat-and-huggingface\u002F)\n\n▶ 利用动态 LLM 选择和 API 集成，为 AI 助手构建安全且具备记忆功能的密码工作流 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fcipher_memory_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F11\u002Fbuilding-a-secure-and-memory-enabled-cipher-workflow-for-ai-agents-with-dynamic-llm-selection-and-api-integration\u002F)\n\n▶ 开放AI GPT-5模型能力开发者指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FGPT-5\u002FGPT_5.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F08\u002Fa-developers-guide-to-openais-gpt-5-model-capabilities\u002F)\n\n▶ 使用 Google Gemini 构建先进的 PaperQA2 研究助手，用于科学文献分析 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fpaperqa2_gemini_research_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F09\u002Fbuilding-an-advanced-paperqa2-research-agent-with-google-gemini-for-scientific-literature-analysis\u002F)\n\n▶ 使用 OpenAI Agents、函数工具、交接机制和会话记忆构建多智能体研究系统的代码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fopenai_agents_multiagent_research_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F08\u002Fa-code-implementation-to-build-a-multi-agent-research-system-with-openai-agents-function-tools-handoffs-and-session-memory\u002F)\n\n▶ 使用 Google Gemini 和 SAGE 框架构建自适应目标导向 AI 助手的编码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fsage_ai_agent_gemini_implementation_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F06\u002Fa-coding-implementation-to-build-a-self-adaptive-goal-oriented-ai-agent-using-google-gemini-and-the-sage-framework\u002F)\n\n▶ 使用 Microsoft AutoGen 和 Gemini API 构建多智能体对话式 AI 框架 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fgemini_autogen_multiagent_framework_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F04\u002Fbuilding-a-multi-agent-conversational-ai-framework-with-microsoft-autogen-and-gemini-api\u002F)\n\n▶ 使用 Cognee 和免费 Hugging Face 模型构建具备代理记忆的智能对话式 AI 助手的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FCognee_Agent_Tutorial_with_HuggingFace_Integration_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F31\u002Fa-coding-guide-to-build-an-intelligent-conversational-ai-agent-with-agent-memory-using-cognee-and-free-hugging-face-models\u002F)\n\n▶ 使用 Google ADK 构建可扩展多智能体系统的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fadvanced_google_adk_multi_agent_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F30\u002Fa-coding-guide-to-build-a-scalable-multi-agent-system-with-google-adk\u002F)\n\n▶ 将 Prolog 逻辑与 Gemini 和 LangGraph 融合，构建工具调用 ReAct 代理的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fprolog_gemini_langgraph_react_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F24\u002Fa-coding-guide-to-build-a-tool-calling-react-agent-fusing-prolog-logic-with-gemini-and-langgraph\u002F)\n\n▶ 使用 Griffe 构建 AI 代码分析代理的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fgriffe_ai_code_analyzer_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F16\u002Fa-coding-guide-to-build-an-ai-code-analysis-agent-with-griffe\u002F)\n\n▶ 使用 BeeAI 框架设计智能多智能体工作流的代码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fbeeai_multi_agent_workflow_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F07\u002Fa-code-implementation-for-designing-intelligent-multi-agent-workflows-with-the-beeai-framework\u002F)\n\n▶ 使用 Python、OpenAI API 和 PrimisAI Nexus 实现支持工具的多智能体工作流 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fprimisai_nexus_multi_agent_workflow_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F07\u002Fimplementing-a-tool-enabled-multi-agent-workflow-with-python-openai-api-and-primisai-nexus\u002F)\n\n▶ 代理通信协议（ACP）入门：用Python构建天气代理 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Ftree\u002Fmain\u002FAgent%20Communication%20Protocol\u002FGetting%20Started) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F06\u002Fgetting-started-with-agent-communication-protocol-acp-build-a-weather-agent-with-python\u002F)\n\n▶ 使用Nebius、Llama 3及实时推理工具构建功能强大的多工具AI代理 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fnebius_llama3_multitool_agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F27\u002Fbuild-a-powerful-multi-tool-ai-agent-using-nebius-with-llama-3-and-real-time-reasoning-tools\u002F)\n\n▶ 构建适用于企业工作流的生产级自定义AI代理，支持监控、编排与可扩展性 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fproduction_ready_custom_ai_agents_workflows_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F22\u002Fbuilding-production-ready-custom-ai-agents-for-enterprise-workflows-with-monitoring-orchestration-and-scalability\u002F)\n\n▶ 构建符合A2A标准的随机数代理：使用Python实现低层级执行器模式的分步指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Ftree\u002Fmain\u002FA2A_Simple_Agent) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F21\u002Fbuilding-an-a2a-compliant-random-number-agent-a-step-by-step-guide-to-implementing-the-low-level-executor-pattern-with-python\u002F)\n\n▶ 使用Mistral Devstral构建轻量级AI编码助手 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fmistral_devstral_compact_loading_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F25\u002Fbuild-a-low-footprint-ai-coding-assistant-with-mistral-devstral\u002F)\n\n▶ 如何利用Google Gemini构建先进的BrightData网页爬虫，实现AI驱动的数据提取 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FEnhanced_BrightData_Gemini_Scraper_Tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F18\u002Fhow-to-build-an-advanced-brightdata-web-scraper-with-google-gemini-for-ai-powered-data-extraction\u002F)\n\n▶ 使用Streamlit构建智能多工具AI代理界面，实现无缝实时交互 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fstreamlit_ai_agent_multitool_interface_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F20\u002Fbuild-an-intelligent-multi-tool-ai-agent-interface-using-streamlit-for-seamless-real-time-interaction\u002F)\n\n▶ 如何使用python-A2A创建并连接金融代理，基于Google的代理间通信（A2A）协议 [笔记本-inflation_agent.py](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Finflation_agent.py) [笔记本-network.ipynb](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fnetwork.ipynb) [笔记本-emi_agent.py](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Femi_agent.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F16\u002Fhow-to-use-python-a2a-to-create-and-connect-financial-agents-with-googles-agent-to-agent-a2a-protocol\u002F)\n\n▶ 利用Riza和Gemini开发具有安全Python执行能力的多工具AI代理 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002FAgents\u002FAgentic-AI\u002FRiza_Gemini_Agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F11\u002Fdevelop-a-multi-tool-ai-agent-with-secure-python-execution-using-riza-and-gemini\u002F)\n\n▶ 基于Gemini构建DataFrame代理，实现自然语言数据解析，结合Pandas与LangChain [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FGemini_Pandas_Agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F10\u002Fbuild-a-gemini-powered-dataframe-agent-for-natural-language-data-analysis-with-pandas-and-langchain\u002F)\n\n▶ 如何利用Gemini构建异步AI代理网络，用于研究、分析和验证任务 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fgemini_agent_network_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F08\u002Fhow-to-build-an-asynchronous-ai-agent-network-using-gemini-for-research-analysis-and-validation-tasks\u002F)\n\n▶ 如何利用Mistral Agents API的交接功能创建智能多代理工作流 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fagent_orchestration_with_mistral_agents_api.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F09\u002Fhow-to-create-smart-multi-agent-workflows-using-the-mistral-agents-apis-handoffs-feature\u002F)\n\n▶ 如何使用标准JSON Schema格式在Mistral Agents中启用函数调用 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fhow%20to%20enable%20function%20calling%20in%20Mistral%20Agents.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F08\u002Fhow-to-enable-function-calling-in-mistral-agents-using-the-standard-json-schema-format\u002F)\n\n▶ 使用LangGraph和Gemini构建迭代式AI工作流代理的分步编码指南 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FGraphAIAgent_LangGraph_Gemini_Workflow_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F05\u002Fa-step-by-step-coding-guide-to-building-an-iterative-ai-workflow-agent-using-langgraph-and-gemini\u002F)\n\n▶ 使用Tavily和Gemini AI构建高级网络情报代理的编码实现 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fsmartwebagent_tavily_gemini_webintelligence_marktechpost2.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F03\u002Fa-coding-implementation-to-build-an-advanced-web-intelligence-agent-with-tavily-and-gemini-ai\u002F)\n\n▶ 实战指南：Mistral Agents API入门 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FGetting_Started_with_Mistral_Agents_API.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F03\u002Fhands-on-guide-getting-started-with-mistral-agents-api\u002F)\n\n▶ 使用代理通信协议（ACP）构建可扩展多代理通信系统的编码指南 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FA_Coding_Guide_to_ACP_Systems_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F31\u002Fa-coding-guide-to-building-a-scalable-multi-agent-communication-systems-using-agent-communication-protocol-acp\u002F)\n\n▶ 使用Google Gemini API构建具备智能适应功能的自我改进型AI代理的编码指南 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FSelf_Improving_AI_Agent_with_Gemini_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F29\u002Fa-coding-guide-for-building-a-self-improving-ai-agent-using-googles-gemini-api-with-intelligent-adaptation-features\u002F)\n\n▶ 一种用于协作式、批评驱动型人工智能问题解决及共识构建的Agent2Agent框架的分步编码实现 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fagent2agent_collaboration_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F27\u002Fa-step-by-step-coding-implementation-of-an-agent2agent-framework-for-collaborative-and-critique-driven-ai-problem-solving-with-consensus-building\u002F)\n\n▶ 使用LangGraph和Claude构建可定制多工具AI智能体以实现动态智能体创建的编码指南 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAINotebooks\u002Fblob\u002Fmain\u002FCustomizable_MultiTool_AI_Agent_with_Claude_Marktechpost%20(1).ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F24\u002Fstep-by-step-guide-to-build-a-customizable-multi-tool-ai-agent-with-langgraph-and-claude-for-dynamic-agent-creation\u002F)\n\n▶ 实现具备实时Python执行与自动化验证功能的AI智能体的编码实践 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FLive_Python_Execution_and_Validation_Agent_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F25\u002Fa-coding-implementation-to-build-an-ai-agent-with-live-python-execution-and-automated-validation\u002F)\n\n▶ 使用Microsoft AutoGen打造高级轮询式多智能体工作流的全面编码指南 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FSynthetic_Data_Creation.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F23\u002Fa-comprehensive-coding-guide-to-crafting-advanced-round-robin-multi-agent-workflows-with-microsoft-autogen\u002F)\n\n▶ 结合Jina Search、LangChain和Gemini构建具备实时信息检索功能的智能AI助手的编码实现 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FJina_LangChain_Gemini_AI_Assistant_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F01\u002Fa-coding-implementation-of-an-intelligent-ai-assistant-with-jina-search-langchain-and-gemini-for-real-time-information-retrieval\u002F)\n\n---\n\n\n\n### 机器学习与数据科学\n\n▶ 针对长上下文LLM推理、KV缓存压缩及内存高效生成的NVIDIA KVPress端到端编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fnvidia_kvpress_long_context_kv_cache_compression_tutorial_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F09\u002Fan-end-to-end-coding-guide-to-nvidia-kvpress-for-long-context-llm-inference-kv-cache-compression-and-memory-efficient-generation\u002F)\n\n▶ ModelScope模型搜索、推理、微调、评估与导出的全面实施指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDeep%20Learning\u002Fmodelscope_end_to_end_model_hub_dataset_nlp_cv_finetuning_evaluation_export_marktechpost(1).py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F08\u002Fa-comprehensive-implementation-guide-to-modelscope-for-model-search-inference-fine-tuning-evaluation-and-export\u002F)\n\n▶ 使用FastNAS剪枝与微调技术，借助NVIDIA模型优化器构建端到端模型优化流水线的分步指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDeep%20Learning\u002Fnvidia_model_optimizer_fastnas_pipeline_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F03\u002Fstep-by-step-guide-to-build-an-end-to-end-model-optimization-pipeline-with-nvidia-model-optimizer-using-fastnas-pruning-and-fine-tuning\u002F)\n\n▶ 如何利用Hugging Face Transformers、聊天模板及Colab推理构建生产就绪的Gemma 3 1B指令生成AI流水线 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fgemma_3_1b_instruct_tutorial_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F01\u002Fhow-to-build-a-production-ready-gemma-3-1b-instruct-generation-ai-pipeline-with-hugging-face-transformers-chat-templates-and-colab-inference\u002F)\n\n▶ 使用Diffrax和JAX实现高级微分方程求解器、随机模拟及神经常微分方程的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDeep%20Learning\u002Fdiffrax_differential_equations_neural_ode_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F19\u002Fa-coding-guide-to-implement-advanced-differential-equation-solvers-stochastic-simulations-and-neural-ordinary-differential-equations-using-diffrax-and-jax\u002F)\n\n▶ 超越准确率：量化回归中过度、冗余及低信号特征导致的生产脆弱性 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002FFeature_Bloat.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F08\u002Fbeyond-accuracy-quantifying-the-production-fragility-caused-by-excessive-redundant-and-low-signal-features-in-regression\u002F)\n\n▶ 如何使用高级tqdm为异步、并行、Pandas操作、日志记录及高性能工作流构建进度监控 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Ftqdm_production_progress_monitoring_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F07\u002Fhow-to-build-progress-monitoring-using-advanced-tqdm-for-async-parallel-pandas-logging-and-high-performance-workflows\u002F)\n\n▶ 使用Daft构建可扩展端到端机器学习数据流水线以实现高性能结构化与图像数据处理的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fdaft_end_to_end_ml_pipeline_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F05\u002Fa-coding-guide-to-build-a-scalable-end-to-end-machine-learning-data-pipeline-using-daft-for-high-performance-structured-and-image-data-processing\u002F)\n\n▶ 使用Vaex构建可扩展端到端分析与机器学习流水线以处理数百万行数据的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fvaex_large_scale_analytics_and_ml_pipeline_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F02\u002Fa-coding-guide-to-build-a-scalable-end-to-end-analytics-and-machine-learning-pipeline-on-millions-of-rows-using-vaex\u002F)\n\n▶ 如何利用SHAP-IQ构建可解释AI分析流水线，以理解特征重要性、交互效应及模型决策分解 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FSHAP-IQ\u002Fshapiq_explainable_ai_feature_and_interaction_analysis_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F01\u002Fhow-to-build-an-explainable-ai-analysis-pipeline-using-shap-iq-to-understand-feature-importance-interaction-effects-and-model-decision-breakdown\u002F)\n\n▶ 一份完整的端到端编码指南，涵盖 MLflow 实验跟踪、超参数优化、模型评估及实时模型部署 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMLFlow%20for%20LLM%20Evaluation\u002Fmlflow_experiment_tracking_and_model_serving_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F01\u002Fa-complete-end-to-end-coding-guide-to-mlflow-experiment-tracking-hyperparameter-optimization-model-evaluation-and-live-model-deployment\u002F)\n\n▶ 如何使用 Folium 构建交互式地理空间仪表板，包含热力图、等值区域图、时间动画、标记聚类以及高级交互插件 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Ffolium_interactive_geospatial_visualization_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F27\u002Fhow-to-build-interactive-geospatial-dashboards-using-folium-with-heatmaps-choropleths-time-animation-marker-clustering-and-advanced-interactive-plugins\u002F)\n\n▶ 使用 Asyncio、恶意节点和延迟分析模拟实用拜占庭容错的编码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Fpbft_asyncio_byzantine_latency_simulator_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F24\u002Fa-coding-implementation-to-simulate-practical-byzantine-fault-tolerance-with-asyncio-malicious-nodes-and-latency-analysis\u002F)\n\n▶ 如何利用 PyGWalker 和特征工程数据构建先进、交互式的探索性数据分析工作流 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fadvanced_pygwalker_interactive_eda_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F17\u002Fhow-to-build-an-advanced-interactive-exploratory-data-analysis-workflow-using-pygwalker-and-feature-engineered-data\u002F)\n\n▶ 【深度指南】高保真合成数据的完整 CTGAN + SDV 流程 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fctgan_sdv_synthetic_data_pipeline_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F13\u002Fin-depth-guide-the-complete-ctgan-sdv-pipeline-for-high-fidelity-synthetic-data\u002F)\n\n▶ 如何使用 Flower 和 PEFT 构建保护隐私的联邦流水线，以 LoRA 微调大型语言模型 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FFederated%20Learning\u002Ffederated_lora_llm_finetuning_flower_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F09\u002Fhow-to-build-a-privacy-preserving-federated-pipeline-to-fine-tune-large-language-models-with-lora-using-flower-and-peft\u002F)\n\n▶ 如何使用 Polyfactory 结合数据类、Pydantic、Attrs 及嵌套模型设计生产级模拟数据管道 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fpolyfactory_production_grade_mock_data_generation_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F08\u002Fhow-to-design-production-grade-mock-data-pipelines-using-polyfactory-with-dataclasses-pydantic-attrs-and-nested-models\u002F)\n\n▶ 一份基于编码和数据驱动的指南，介绍如何使用 complexipy 测量、可视化并强制执行 Python 项目的认知复杂度 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fcognitive_complexity_auditing_with_complexipy_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F06\u002Fa-coding-data-driven-guide-to-measuring-visualizing-and-enforcing-cognitive-complexity-in-python-projects-using-complexipy\u002F)\n\n▶ 如何使用 Qrisp 构建高级量子算法，包括 Grover 搜索、量子相位估计和 QAOA [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FQuantum%20Computing\u002FQrisp_Quantum_Algorithms_Grover_QPE_QAOA_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F03\u002Fhow-to-build-advanced-quantum-algorithms-using-qrisp-with-grover-search-quantum-phase-estimation-and-qaoa\u002F)\n\n▶ 基于编码与实验分析的去中心化联邦学习研究，结合 Gossip 协议与差分隐私 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Fdecentralized_gossip_federated_learning_with_differential_privacy_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F01\u002Fa-coding-and-experimental-analysis-of-decentralized-federated-learning-with-gossip-protocols-and-differential-privacy\u002F)\n\n▶ 使用 PyKEENs 训练、优化、评估和解释知识图谱嵌入的编码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fadvanced_pykeen_knowledge_graph_embeddings_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F30\u002Fa-coding-implementation-to-training-optimizing-evaluating-and-interpreting-knowledge-graph-embeddings-with-pykeen\u002F)\n\n▶ 使用 Kornia 进行可微分计算机视觉的深度解析，涵盖几何优化、LoFTR 匹配和 GPU 增强技术 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FComputer%20Vision\u002Fkornia_differentiable_vision_loftr_ransac_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F29\u002Fa-coding-deep-dive-into-differentiable-computer-vision-with-kornia-using-geometry-optimization-loftr-matching-and-gpu-augmentations\u002F)\n\n▶ 机器学习与语义嵌入如何重新排序 CVE 漏洞，超越原始 CVSS 分数 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FSecurity\u002Fai_assisted_vulnerability_prioritization_ml_nlp_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F23\u002Fhow-machine-learning-and-semantic-embeddings-reorder-cve-vulnerabilities-beyond-raw-cvss-scores\u002F)\n\n▶ AutoGluon 如何通过集成与蒸馏技术，为生产级表格数据模型提供现代化的 AutoML 流水线 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fautogluon_end_to_end_tabular_modeling_and_deployment_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F21\u002Fhow-autogluon-enables-modern-automl-pipelines-for-production-grade-tabular-models-with-ensembling-and-distillation\u002F)\n\n▶ 一份编码指南，帮助理解重试机制如何在 RPC 和事件驱动架构中引发故障级联 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Frpc_vs_event_driven_failure_dynamics_distributed_systems_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F18\u002Fa-coding-guide-to-understanding-how-retries-trigger-failure-cascades-in-rpc-and-event-driven-architectures\u002F)\n\n▶ 如何使用 Ibis、惰性 Python API 和 DuckDB 执行引擎构建可移植的数据库内特征工程流水线 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fibis_portable_in_database_feature_engineering_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F09\u002Fhow-to-build-portable-in-database-feature-engineering-pipelines-with-ibis-using-lazy-python-apis-and-duckdb-execution\u002F)\n\n▶ 一种编码实现：使用 DirectRunner 构建统一的 Apache Beam 流水线，演示基于事件时间窗口的批处理与流处理 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Fapache_beam_batch_and_stream_windowing_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F07\u002Fa-coding-implementation-to-build-a-unified-apache-beam-pipeline-demonstrating-batch-and-stream-processing-with-event-time-windowing-using-directrunner\u002F)\n\n▶ 从零开始实现 Softmax：避免数值稳定性陷阱 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002FSoftmax.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F06\u002Fimplementing-softmax-from-scratch-avoiding-the-numerical-stability-trap\u002F)\n\n▶ 一种编码实现：利用轻量级 PyTorch 模拟，从零构建由 OpenAI 辅助的隐私保护联邦欺诈检测系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FFederated%20Learning\u002Fopenai_federated_fraud_detection_from_scratch_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F30\u002Fa-coding-implementation-of-an-openai-assisted-privacy-preserving-federated-fraud-detection-system-from-scratch-using-lightweight-pytorch-simulations\u002F)\n\n▶ 一种编码实现：构建自组织 Zettelkasten 知识图谱及睡眠整合机制 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002FAgentic_Zettelkasten_Memory_Martechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F25\u002Fa-coding-implementation-on-building-self-organizing-zettelkasten-knowledge-graphs-and-sleep-consolidation-mechanisms\u002F)\n\n▶ 如何使用 Kombu、主题交换和并发工作进程构建高性能分布式任务路由系统 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Fkombu_task_routing_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F19\u002Fhow-to-build-a-high-performance-distributed-task-routing-system-using-kombu-with-topic-exchanges-and-concurrent-workers\u002F)\n\n▶ 在 NumPyro 中使用 JAX 驱动的推理和后验预测分析，实现完整的层次贝叶斯回归工作流程 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fnumpyro_hierarchical_regression_advanced_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F07\u002Fa-coding-implementation-of-a-complete-hierarchical-bayesian-regression-workflow-in-numpyro-using-jax-powered-inference-and-posterior-predictive-analysis\u002F)\n\n▶ 如何使用 Panel 设计具有动态筛选、实时 KPI 和丰富可视化探索功能的高级多页交互式分析仪表板 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fadvanced_panel_interactive_dashboard_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F30\u002Fhow-to-design-an-advanced-multi-page-interactive-analytics-dashboard-with-dynamic-filtering-live-kpis-and-rich-visual-exploration-using-panel\u002F)\n\n▶ 我们如何通过偏好学习逐步奖励，以解决稀疏奖励环境？——基于在线过程奖励学习 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FReinforcement%20learning\u002Foprl_preference_shaped_rl_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F12\u002F02\u002Fhow-we-learn-step-level-rewards-from-preferences-to-solve-sparse-reward-environments-using-online-process-reward-learning\u002F)\n\n▶ 如何利用 PyGWalker 的功能构建端到端交互式分析仪表板，进行洞察力十足的数据探索 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fadvanced_pygwalker_visual_analysis_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F11\u002Fhow-to-build-an-end-to-end-interactive-analytics-dashboard-using-pygwalker-features-for-insightful-data-exploration\u002F)\n\n▶ 如何使用 Textual 构建完全交互式、响应式且动态的终端数据仪表板？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002Fadvanced_textual_data_dashboard_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F15\u002Fhow-to-design-a-fully-interactive-reactive-and-dynamic-terminal-based-data-dashboard-using-textual\u002F)\n\n▶ 一种编码实现：使用 JAX、Flax 和 Optax 构建并训练具有残差连接、自注意力机制和自适应优化的先进架构 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fadvanced_jax_flax_optax_training_pipeline_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F10\u002Fa-coding-implementation-to-build-and-train-advanced-architectures-with-residual-connections-self-attention-and-adaptive-optimization-using-jax-flax-and-optax\u002F)\n\n▶ 我们如何使用 Meta Research Hydra 构建可扩展且可复现的机器学习实验流水线？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fmeta_hydra_advanced_implementation_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F04\u002Fhow-can-we-build-scalable-and-reproducible-machine-learning-experiment-pipelines-using-meta-research-hydra\u002F)\n\n▶ 如何构建一个具备实时数据库、动态状态管理和响应式 UI 的高级多页 Reflex Web 应用程序？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fadvanced_reflex_reactive_webapp_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F08\u002Fhow-to-build-an-advanced-multi-page-reflex-web-application-with-real-time-database-dynamic-state-management-and-reactive-ui\u002F)\n\n▶ 如何使用 Apache Spark 和 PySpark 构建端到端的数据工程与机器学习流水线？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002FAdvanced_PySpark_End_to_End_Tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F01\u002Fhow-to-build-an-end-to-end-data-engineering-and-machine-learning-pipeline-with-apache-spark-and-pyspark\u002F)\n\n▶ 当你没有标注数据时，如何构建监督学习人工智能模型 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FData%20Science\u002FActive_Learning.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F03\u002Fhow-to-build-supervised-ai-models-when-you-dont-have-annotated-data\u002F)\n\n---\n\n\n\n\n### MCP 指南\n\n▶ 如何设计一个生产就绪的 AI 代理，利用 Colab-MCP、MCP 工具、FastMCP 和内核执行来自动化 Google Colab 工作流 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMCP%20Codes\u002FWiring_AI_Agents_to_Google_Colab_MCP_Deep_Dive_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F23\u002Fhow-to-design-a-production-ready-ai-agent-that-automates-google-colab-workflows-using-colab-mcp-mcp-tools-fastmcp-and-kernel-execution\u002F)\n\n▶ 如何构建一种无状态、安全且异步的 MCP 风格协议，用于可扩展的代理工作流 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMCP%20Codes\u002Fstateless_async_mcp_protocol_demo_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F14\u002Fhow-to-build-a-stateless-secure-and-asynchronous-mcp-style-protocol-for-scalable-agent-workflows\u002F)\n\n▶ 使用模型上下文协议 (MCP) 构建动态 AI 系统的实现，以实现实时资源和工具集成 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMCP%20Codes\u002FModel_Context_Protocol_Integration_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F19\u002Fan-implementation-to-build-dynamic-ai-systems-with-the-model-context-protocol-mcp-for-real-time-resource-and-tool-integration\u002F)\n\n▶ 使用 Scalekit 为 MCP 服务器实现 OAuth 2.1：分步编码教程 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Ftree\u002Fmain\u002FOAuth%202.1%20for%20MCP%20Servers) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F01\u002Fimplementing-oauth-2-1-for-mcp-servers-with-scalekit-a-step-by-step-coding-tutorial\u002F)\n\n▶ 使用 Gemini 和 mcp-agent 框架构建基于 MCP 的 AI 代理：分步实施指南 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fmcp_gemini_agent_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F17\u002Fbuilding-an-mcp-powered-ai-agent-with-gemini-and-mcp-agent-framework-a-step-by-step-implementation-guide\u002F)\n\n▶ 使用 Vizro MCP 创建仪表板：Vizro 是麦肯锡推出的一款开源 Python 工具包 [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F18\u002Fcreating-dashboards-using-vizro-mcp-vizro-is-an-open-source-python-toolkit-by-mckinsey\u002F)\n\n▶ 分步编码指南：使用 FastMCP 定义自定义模型上下文协议 (MCP) 服务器和客户端工具，并将其集成到 Google Gemini 2.0 的函数调用工作流中 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fcustom_mcp_tools_integration_with_fastmcp_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F04\u002F21\u002Fa-step-by-step-coding-guide-to-defining-custom-model-context-protocol-mcp-server-and-client-tools-with-fastmcp-and-integrating-them-into-google-gemini-2-0s-function%e2%80%91calling-workflow\u002F)\n\n▶ 在 Google Colab 中使用 LangChain、LangGraph、Gemini Pro 和模型上下文协议 (MCP) 原则构建具有工具集成支持的上下文感知 AI 助手的代码实现 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FContext_Aware_Assistant_MCP_Gemini_LangChain_LangGraph_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F04\u002F04\u002Fa-code-implementation-to-building-a-context-aware-ai-assistant-in-google-colab-using-langchain-langgraph-gemini-pro-and-model-context-protocol-mcp-principles-with-tool-integration-support\u002F)\n\n▶ 使用 Desktop Commander MCP 服务器的指南 [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F01\u002Fguide-to-using-the-desktop-commander-mcp-server\u002F)\n\n---\n\n### LLM、ML 及其他 AI 栏目\n\n▶ 使用 Google LangExtract、OpenAI 模型、结构化提取和交互式可视化构建高级文档智能流水线的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Flangextract_document_intelligence_pipeline_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F08\u002Fa-coding-guide-to-build-advanced-document-intelligence-pipelines-with-google-langextract-openai-models-structured-extraction-and-interactive-visualization\u002F)\n\n▶ 如何部署 Open WebUI，实现安全的 OpenAI API 集成、公共隧道服务以及基于浏览器的聊天访问 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fopen_webui_google_colab_secure_openai_tunnel_setup_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F07\u002Fhow-to-deploy-open-webui-with-secure-openai-api-integration-public-tunneling-and-browser-based-chat-access\u002F)\n\n▶ 运行 NVIDIA Transformer Engine 的实施指南：混合精度、FP8 检查、基准测试及回退执行 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fnvidia_transformer_engine_colab_mixed_precision_fp8_benchmarking_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F06\u002Fan-implementation-guide-to-running-nvidia-transformer-engine-with-mixed-precision-fp8-checks-benchmarking-and-fallback-execution\u002F)\n\n▶ 使用 GGUF 和 4 位量化运行 Qwen3.5 推理模型的编码实现，这些模型经过蒸馏并具备 Claude 式思维 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fqwen3_5_reasoning_colab_dual_path_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F26\u002Fa-coding-implementation-to-run-qwen3-5-reasoning-models-distilled-with-claude-style-thinking-using-gguf-and-4-bit-quantization\u002F)\n\n▶ 构建具有不确定性感知能力的 LLM 系统的编码实现：包含置信度估计、自我评估和自动网络搜索功能 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Funcertainty_aware_llm_confidence_self_evaluation_auto_research_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F21\u002Fa-coding-implementation-to-build-an-uncertainty-aware-llm-system-with-confidence-estimation-self-evaluation-and-automatic-web-research\u002F)\n\n▶ 如何使用 Unsloth 为大型语言模型构建稳定高效的 QLoRA 微调流水线 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Funsloth_qlora_stable_sft_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F03\u002Fhow-to-build-a-stable-and-efficient-qlora-fine-tuning-pipeline-using-unsloth-for-large-language-models\u002F)\n\n▶ 使用 TruLens 和 OpenAI 模型对 LLM 应用进行插桩、追踪与评估的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Evaluation\u002Ftrulens_llm_instrumentation_feedback_evaluation_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F22\u002Fa-coding-guide-to-instrumenting-tracing-and-evaluating-llm-applications-using-trulens-and-openai-models\u002F)\n\n▶ 如何使用直接偏好优化、QLoRA 和 Ultra-Feedback 将大型语言模型与人类偏好对齐 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fdpo_alignment_qlora_ultrafeedback_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F12\u002Fhow-to-align-large-language-models-with-human-preferences-using-direct-preference-optimization-qlora-and-ultra-feedback\u002F)\n\n▶ 如何构建采用 64 维截断的套娃优化句子嵌入模型，实现超快速检索 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fmatryoshka_representation_learning_sentencetransformers_msmarco_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F11\u002Fhow-to-build-a-matryoshka-optimized-sentence-embedding-model-for-ultra-fast-retrieval-with-64-dimension-truncation\u002F)\n\n▶ 使用 MLflow 为大型语言模型建立严格的提示版本控制和回归测试工作流的编码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FMLFlow%20for%20LLM%20Evaluation\u002FPrompt_Versioning_and_Regression_Testing_for_LLMs_with_MLflow_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F08\u002Fa-coding-implementation-to-establish-rigorous-prompt-versioning-and-regression-testing-workflows-for-large-language-models-using-mlflow\u002F)\n\n▶ 使用 DeepEval、自定义检索器和“LLM 作为裁判”指标自动化 LLM 质量保证的编码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Evaluation\u002Frag_deepeval_quality_benchmarking_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F25\u002Fa-coding-implementation-to-automating-llm-quality-assurance-with-deepeval-custom-retrievers-and-llm-as-a-judge-metrics\u002F)\n\n▶ 如何使用 Tinygrad 从头实现 Transformer 和 Mini-GPT 模型的功能组件，以深入理解深度学习的内部机制 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Ftinygrad_transformer_minigpt_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F25\u002Fhow-to-implement-functional-components-of-transformer-and-mini-gpt-model-from-scratch-using-tinygrad-to-understand-deep-learning-internals\u002F)\n\n▶ 使用 Opik 实现完全可追踪且经过评估的本地 LLM 流程，打造透明、可度量且可重复的 AI 工作流 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Fopik_local_llm_tracing_and_evaluation_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F21\u002Fan-implementation-of-fully-traced-and-evaluated-local-llm-pipeline-using-opik-for-transparent-measurable-and-reproducible-ai-workflows\u002F)\n\n▶ 构建基于 Transformer 的回归语言模型，用于从文本中预测连续值的编码实现 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fregression_language_model_transformer_rlm_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F04\u002Fa-coding-implementation-to-build-a-transformer-based-regression-language-model-to-predict-continuous-values-from-text\u002F)\n\n▶ 使用 LitServe 构建高级多端点机器学习 API 的实现：批处理、流式传输、缓存及本地推理 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Fadvanced_litserve_multi_endpoint_api_tutorial_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F24\u002Fan-implementation-on-building-advanced-multi-endpoint-machine-learning-apis-with-litserve-batching-streaming-caching-and-local-inference\u002F)\n\n▶ Ivy 框架无关的机器学习构建、转译与跨所有主流后端的基准测试 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002FAdvanced_Ivy_Framework_Agnostic_ML_Tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F13\u002Fivy-framework-agnostic-machine-learning-build-transpile-and-benchmark-across-all-major-backends\u002F)\n\n▶ 使用 Lightly AI 掌握自监督学习的编码指南，以实现高效的数据整理与主动学习 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Flightly_ai_self_supervised_active_learning_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F11\u002Fa-coding-guide-to-master-self-supervised-learning-with-lightly-ai-for-efficient-data-curation-and-active-learning\u002F)\n\n▶ 使用 TPOT 构建并优化智能机器学习流水线，实现全面自动化与性能提升 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FML%20Project%20Codes\u002Ftpot_advanced_pipeline_optimization_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F29\u002Fbuilding-and-optimizing-intelligent-machine-learning-pipelines-with-tpot-for-complete-automation-and-performance-enhancement\u002F)\n\n▶ 使用 Ollama、REST API 和 Gradio 聊天界面构建完整自托管 LLM 工作流的编码实现 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fself_hosted_llm_ollama_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F19\u002Fa-coding-implementation-to-build-a-complete-self-hosted-llm-workflow-with-ollama-rest-api-and-gradio-chat-interface\u002F)\n\n▶ 如何使用 deepteam 对 OpenAI 模型进行单轮对抗性攻击测试 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAdversarial%20Attacks\u002FSingle-Turn%20Attacks.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F17\u002Fhow-to-test-an-openai-model-against-single-turn-adversarial-attacks-using-deepteam\u002F)\n\n▶ 使用 RouteLLM 优化 LLM 的使用 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FGPT-5\u002FRouteLLM.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F10\u002Fusing-routellm-to-optimize-llm-usage\u002F)\n\n▶ 教程：探索 SHAP-IQ 可视化 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FSHAP-IQ\u002FSHAP_IQ_Visuals.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F03\u002Ftutorial-exploring-shap-iq-visualizations\u002F)\n\n▶ 使用 Roboflow Supervision 构建端到端目标跟踪与分析系统 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Froboflow_supervision_advanced_tracking_analytics_pipeline_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F08\u002F03\u002Fbuilding-an-end-to-end-object-tracking-and-analytics-system-with-roboflow-supervision\u002F)\n\n▶ 开始使用 Microsoft 的 Presidio：检测并匿名化文本中个人身份信息 PII 的分步指南 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FPresidio.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F24\u002Fgetting-started-with-microsofts-presidio-a-step-by-step-guide-to-detecting-and-anonymizing-personally-identifiable-information-pii-in-text\u002F)\n\n▶ 使用 Upstage API 和 LangChain 构建 groundedness 验证工具 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FUpstage_Groundedness_Check_Tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F24\u002Fbuild-a-groundedness-verification-tool-using-upstage-api-and-langchain\u002F)\n\n▶ 通过限速、内存缓存和认证构建生产就绪的异步 Python SDK 编码指南 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fadvanced_async_python_sdk_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F23\u002Fa-coding-guide-to-build-a-production-ready-asynchronous-python-sdk-with-rate-limiting-in-memory-caching-and-authentication\u002F)\n\n▶ 使用 Polars 构建高性能金融分析管道：惰性计算、高级表达式与 SQL 集成 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fpolars_sql_analytics_pipeline_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F17\u002Fbuilding-high-performance-financial-analytics-pipelines-with-polars-lazy-evaluation-advanced-expressions-and-sql-integration\u002F)\n\n▶ 在 TinyDev 中使用“计划 → 文件 → 代码”工作流构建 AI 驱动的应用程序 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Ftinydev_gemini_implementation_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F14\u002Fbuilding-ai-powered-applications-using-the-plan-%e2%86%92-files-%e2%86%92-code-workflow-in-tinydev\u002F)\n\n▶ 针对高级分析，使用 Google Gemini-1.5-Flash 进行 SerpAPI 高级集成的全面编码教程 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fadvanced_serpapi_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F06\u002Fa-comprehensive-coding-tutorial-for-advanced-serpapi-integration-with-google-gemini-1-5-flash-for-advanced-analytics\u002F)\n\n▶ 使用 Daytona SDK 构建安全的 AI 代码执行工作流 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002Fdaytona_secure_ai_code_execution_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F12\u002Fbuild-a-secure-ai-code-execution-workflow-using-daytona-sdk\u002F)\n\n▶ 实现 ScrapeGraph 和 Gemini AI，打造自动化、可扩展、基于洞察的竞争情报与市场分析工作流的编码指南 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FCompetitive_Analysis_with_ScrapeGraph_Gemini_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F06\u002F02\u002Fa-coding-guide-implementing-scrapegraph-and-gemini-ai-for-an-automated-scalable-insight-driven-competitive-intelligence-and-market-analysis-workflow\u002F)\n\n▶ 使用 Lyzr 聊天机器人框架构建交互式转录与 PDF 分析的编码实现 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FLyzr_Chatbot_Framework_Implementation_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F27\u002Fa-coding-implementation-to-build-an-interactive-transcript-and-pdf-analysis-with-lyzr-chatbot-framework\u002F)\n\n▶ 使用 Synthetic Data Vault (SDV) 创建合成数据的分步指南 [笔记本](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Notebooks\u002Fblob\u002Fmain\u002FAutoGen_TeamTool_RoundRobin_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F05\u002F25\u002Fstep-by-step-guide-to-creating-synthetic-data-using-the-synthetic-data-vault-sdv\u002F)\n\n▶ 使用 LLM 创建知识图谱 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Ftree\u002Fmain\u002FMirascope) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F07\u002F28\u002Fcreating-a-knowledge-graph-using-an-llm\u002F)\n\n---\n\n\n\n### 语音 AI\n\n▶ 如何设计一个完全流式的语音代理，具备端到端延迟预算、增量 ASR、LLM 流式处理和实时 TTS？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FVoice%20AI\u002Fstreaming_voice_agent_latency_budgets_end_to_end_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F19\u002Fhow-to-design-a-fully-streaming-voice-agent-with-end-to-end-latency-budgets-incremental-asr-llm-streaming-and-real-time-tts\u002F)\n\n▶ 如何构建一个能够理解、推理、规划并以自主多步智能响应的代理型语音 AI 助手？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FVoice%20AI\u002Fagentic_voice_ai_autonomous_assistant_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F08\u002Fhow-to-build-an-agentic-voice-ai-assistant-that-understands-reasons-plans-and-responds-through-autonomous-multi-step-intelligence\u002F)\n\n▶ 如何利用 WhisperX 构建包含转录、对齐、分析和导出功能的高级语音 AI 流程？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FVoice%20AI\u002Fvoice_ai_whisperx_advanced_tutorial_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F02\u002Fhow-to-build-an-advanced-voice-ai-pipeline-with-whisperx-for-transcription-alignment-analysis-and-export\u002F)\n\n▶ 使用 SpeechBrain 在 Python 中构建语音增强与自动语音识别 (ASR) 流程 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002Fguide_to_building_an_end_to_end_speech_enhancement_and_recognition_pipeline_with_speechbrain.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F09\u002Fbuilding-a-speech-enhancement-and-automatic-speech-recognition-asr-pipeline-in-python-using-speechbrain\u002F)\n\n▶ 如何使用 Hugging Face Pipelines 构建先进的端到端语音 AI 代理？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fhow_to_build_an_advanced_end_to_end_voice_ai_agent_using_hugging_face_pipelines.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F09\u002F17\u002Fhow-to-build-an-advanced-end-to-end-voice-ai-agent-using-hugging-face-pipelines\u002F) \n\n---\n\n### RAG\n\n▶ Tree-KG 如何实现层次化知识图谱，支持情境导航与可解释的多跳推理，超越传统 RAG [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FRAG\u002Ftree_kg_hierarchical_knowledge_graph_multi_hop_reasoning_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F27\u002Fhow-tree-kg-enables-hierarchical-knowledge-graphs-for-contextual-navigation-and-explainable-multi-hop-reasoning-beyond-traditional-rag\u002F)\n\n▶ 如何利用语义 LLM 缓存降低 RAG 应用的成本与延迟 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FRAG\u002FSemantic_Caching.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F11\u002Fhow-to-reduce-cost-and-latency-of-your-rag-application-using-semantic-llm-caching\u002F)\n\n▶ 如何构建具有智能查询路由、自我检查及迭代优化功能的代理式决策树 RAG 系统？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FRAG\u002Fagentic_rag_with_routing_and_self_check_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F27\u002Fhow-to-build-an-agentic-decision-tree-rag-system-with-intelligent-query-routing-self-checking-and-iterative-refinement\u002F)\n\n▶ 如何使用开源 AI 模型设计一个具备检索增强与政策约束的全功能企业级 AI 助手？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FRAG\u002Fenterprise_ai_rag_guardrails_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F22\u002Fhow-to-design-a-fully-functional-enterprise-ai-assistant-with-retrieval-augmentation-and-policy-guardrails-using-open-source-ai-models\u002F)\n\n▶ 如何利用合成数据评估你的 RAG 流程？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FRAG\u002Frag_evaluation.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F10\u002F13\u002Fhow-to-evaluate-your-rag-pipeline-with-synthetic-data\u002F)\n\n\n---\n\n### 计算机视觉\n\n▶ [教程] 使用 Pose2Sim、RTMPose 和 OpenSim 进行无标记 3D 人体运动学的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FComputer%20Vision\u002Fpose2sim_markerless_3d_kinematics_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F10\u002Fa-coding-guide-to-markerless-3d-human-kinematics-with-pose2sim-rtmpose-and-opensim\u002F)\n\n▶ [教程] 如何构建基于 CogVideoX、自定义提示及端到端样本推理的 Netflix VOID 视频对象移除与修复流水线 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FComputer%20Vision\u002Fnetflix_void_video_object_removal_inpainting_pipeline_with_cogvideox_and_sample_inference.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F05\u002Fhow-to-build-a-netflix-void-video-object-removal-and-inpainting-pipeline-with-cogvideox-custom-prompting-and-end-to-end-sample-inference\u002F)\n\n▶ [教程] 使用 ColPali 和后期交互评分构建视觉文档检索流水线 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FComputer%20Vision\u002Fcolpali_visual_retrieval_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F18\u002Ftutorial-building-a-visual-document-retrieval-pipeline-with-colpali-and-late-interaction-scoring\u002F)\n\n▶ 使用 Optuna 结合剪枝多目标搜索、早停机制和深度视觉分析实现高级超参数优化的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FComputer%20Vision\u002Foptuna_advanced_hpo_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2025\u002F11\u002F17\u002Fa-coding-guide-to-implement-advanced-hyperparameter-optimization-with-optuna-using-pruning-multi-objective-search-early-stopping-and-deep-visual-analysis\u002F)\n\n\n### 安全\n\n▶ 如何构建多层 LLM 安全过滤器，以防御自适应、改写及对抗性提示攻击 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAdversarial%20Attacks\u002Frobust_llm_safety_filters_adaptive_attack_defense_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F02\u002Fhow-to-build-multi-layered-llm-safety-filters-to-defend-against-adaptive-paraphrased-and-adversarial-prompt-attacks\u002F)\n\n▶ 使用 PyTorch 在 CIFAR-10 数据集上通过标签翻转演示深度学习中的定向数据投毒攻击的编码指南 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FSecurity\u002Ftargeted_data_poisoning_label_flipping_cifar10_pytorch_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F11\u002Fa-coding-guide-to-demonstrate-targeted-data-poisoning-attacks-in-deep-learning-by-label-flipping-on-cifar-10-with-pytorch\u002F)\n\n▶ 如何构建一个多轮渐强式红队测试流水线，利用 Garak 评估并压力测试 LLM 的安全性 [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FAdversarial%20Attacks\u002Fmultiturn_crescendo_llm_safety_evaluation_with_garak_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F01\u002F13\u002Fhow-to-build-a-multi-turn-crescendo-red-teaming-pipeline-to-evaluate-and-stress-test-llm-safety-using-garak\u002F)\n\n\n### AI 基础设施\n\n▶ 如何使用 NVIDIA Warp 核心构建高性能 GPU 加速仿真与可微分物理工作流？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FScientific%20Computing\u002Fnvidia_warp_gpu_simulation_and_differentiable_physics_Marktechpost.ipynb) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F03\u002F16\u002Fhow-to-build-high-performance-gpu-accelerated-simulations-and-differentiable-physics-workflows-using-nvidia-warp-kernels\u002F)\n\n▶ 如何为 RAG 系统构建一个采用一致性哈希、分片技术并支持实时环形可视化的一致性向量数据库？ [代码](https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included\u002Fblob\u002Fmain\u002FDistributed%20Systems\u002Felastic_vector_db_consistent_hashing_rag_marktechpost.py) [教程](https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F02\u002F25\u002Fhow-to-build-an-elastic-vector-database-with-consistent-hashing-sharding-and-live-ring-visualization-for-rag-systems\u002F)","# AI-Tutorial-Codes-Included 快速上手指南\n\n本仓库汇集了最新的 AI 代理（Agentic AI）、大语言模型（LLM）、RAG、计算机视觉及机器学习等领域的实战教程与代码实现。每个教程均包含详细的 Jupyter Notebook 代码和对应的技术文章链接。\n\n## 环境准备\n\n在开始之前，请确保您的开发环境满足以下要求：\n\n*   **操作系统**：Windows, macOS 或 Linux\n*   **Python 版本**：推荐 Python 3.9 或更高版本\n*   **包管理器**：pip 或 conda\n*   **运行环境**：推荐使用 Jupyter Lab \u002F Jupyter Notebook 或 Google Colab\n*   **API Keys**：部分教程需要配置 OpenAI, Google Gemini, Z.AI 等服务的 API Key\n\n### 前置依赖安装\n\n建议创建一个独立的虚拟环境以避免依赖冲突：\n\n```bash\npython -m venv ai-tutorial-env\nsource ai-tutorial-env\u002Fbin\u002Factivate  # Windows 用户请使用: ai-tutorial-env\\Scripts\\activate\n```\n\n## 安装步骤\n\n### 1. 克隆仓库\n\n使用 Git 将代码库克隆到本地：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Tutorial-Codes-Included.git\ncd AI-Tutorial-Codes-Included\n```\n\n> **国内加速提示**：如果克隆速度较慢，可使用国内镜像源：\n> ```bash\n> git clone https:\u002F\u002Fgitee.com\u002Fmirror\u002Fgithub\u002FAI-Tutorial-Codes-Included.git\n> ```\n> *(注：若上述镜像不可用，请尝试配置 git proxy 或使用其他国内 GitHub 加速服务)*\n\n### 2. 安装通用依赖\n\n由于不同教程涉及的框架各异（如 LangGraph, PyTorch, JAX, Transformers 等），建议先安装基础数据科学栈，再根据具体教程的 Notebook 头部说明安装特定库。\n\n基础依赖安装命令：\n\n```bash\npip install -r requirements.txt\n```\n\n*(注：若根目录无统一 requirements.txt，请在运行具体 `.ipynb` 文件前，执行该文件第一格中的安装命令，通常格式为 `!pip install package_name`)*\n\n针对国内网络环境，推荐使用清华或阿里镜像源加速安装：\n\n```bash\npip install -r requirements.txt -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n```\n\n## 基本使用\n\n本仓库的核心内容是各个子文件夹下的 `.ipynb` (Jupyter Notebook) 文件。以下是运行一个典型教程（以 Agentic AI 为例）的步骤：\n\n### 1. 选择教程\n进入对应的分类目录，例如 `Agentic AI Codes`：\n\n```bash\ncd \"Agentic AI Codes\"\n```\n\n### 2. 启动 Jupyter\n在当前目录下启动 Jupyter Notebook：\n\n```bash\njupyter notebook\n```\n\n### 3. 运行示例\n在浏览器打开的界面中，选择任意一个教程文件，例如 `gemini3_tool_combination_maps_grounding_context_circulation_tutorial_Marktechpost.ipynb`。\n\n**最简单的使用流程：**\n\n1.  **配置 API Key**：找到代码单元格中设置 API Key 的部分（通常在开头），填入您的密钥。\n    ```python\n    import os\n    os.environ[\"GOOGLE_API_KEY\"] = \"YOUR_ACTUAL_API_KEY_HERE\"\n    ```\n2.  **逐格运行**：按顺序点击每个代码单元格左侧的 \"Run\" 按钮（或按 `Shift + Enter`）。\n3.  **观察输出**：代码将执行代理工作流、调用工具或训练模型，并在单元格下方直接显示结果、日志或可视化图表。\n\n### 4. 参考详细文档\n每个 Notebook 文件名旁在 README 中均附有对应的详细技术文章链接（Tutorial 列）。如果在运行代码时遇到逻辑疑问，请访问 [MarkTechPost](https:\u002F\u002Fwww.marktechpost.com) 查看对应的深度解析文章。\n\n---\n**提示**：部分高级教程（如基于 JAX 的强化学习或特定的多智能体编排）可能需要额外的系统级依赖（如 CUDA 驱动），请务必仔细阅读具体 Notebook 文件顶部的说明注释。","某初创团队的技术负责人正带领三人小组，试图在两周内构建一个能自动调用谷歌地图、搜索实时新闻并执行自定义数据分析的生产级智能体系统。\n\n### 没有 AI-Tutorial-Codes-Included 时\n- **架构设计迷茫**：面对“多步代理链”和“上下文循环”等复杂概念，团队需花费数天查阅零散文档，仍难以理清如何在一个 API 调用中串联多个工具。\n- **代码从零造轮子**：缺乏现成的生产级参考代码，开发人员必须手动编写大量样板代码来处理流式传输、工具 ID 并行调用及错误重试机制。\n- **调试成本高昂**：在尝试整合 ReAct 代理与自定义工具时，因缺少标准的工作流模板，团队频繁陷入死循环或输出格式错误的困境，排查耗时极长。\n- **技术选型风险**：无法快速验证如 Z.AI GLM-5 的“思考模式”或 AgentScope 的多代理辩论机制是否适合当前业务，导致项目进度严重滞后。\n\n### 使用 AI-Tutorial-Codes-Included 后\n- **架构清晰落地**：直接复用仓库中\"Gemini 多工具组合”的 Notebook 示例，团队半天内就理解了上下文循环机制，并成功设计出符合业务逻辑的代理链路。\n- **开箱即用加速**：基于提供的“生产级 AgentScope 工作流”代码，开发人员只需替换具体的业务参数和 API 密钥，即可运行具备结构化输出和并发能力的智能体。\n- **避坑指南明确**：参考“网络安全 AI 代理”案例中的护栏（Guardrails）和交接（Handoffs）实现，团队迅速解决了多代理协作中的状态丢失问题，大幅减少调试时间。\n- **前沿技术快速验证**：通过运行\"A-Evolve 代理进化”教程，团队在短时间内完成了不同模型技能的基准测试，迅速确定了最优的技术栈组合。\n\nAI-Tutorial-Codes-Included 将原本需要数周摸索的复杂代理系统构建过程，压缩为几天的快速迭代，让开发者能直接站在成熟代码的肩膀上创新。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMarktechpost_AI-Tutorial-Codes-Included_2fb8ecb2.png","Marktechpost","Marktechpost.com","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FMarktechpost_3426eedb.png","AI Research and Dev Platform","Marktechpost AI ","California, USA",null,"marktechpost","www.marktechpost.com","https:\u002F\u002Fgithub.com\u002FMarktechpost",[83,87],{"name":84,"color":85,"percentage":86},"Jupyter Notebook","#DA5B0B",97.5,{"name":88,"color":89,"percentage":90},"Python","#3572A5",2.5,2194,498,"2026-04-11T04:10:24",1,"未说明","未说明 (部分教程涉及 JAX\u002FRLax 或本地多模态模型如 MolmoWeb-4B，可能需 GPU 加速，但具体型号未列出)",{"notes":98,"python":95,"dependencies":99},"该项目是一个包含多个独立 AI 教程和 Notebook 的代码合集，而非单一软件工具。每个教程（如 Agentic AI, RAG, Computer Vision 等）依赖不同的库和运行环境。部分代码设计为在 Google Colab 中运行，其他可能需要本地安装特定框架（如 JAX 或 PyTorch）。用户需根据具体选择的教程笔记本查看其内部的依赖安装单元格以获取准确的环境要求。",[100],"未说明 (代码示例涵盖 Gemini API, OpenAI, Z.AI GLM-5, AgentScope, JAX, Haiku, Optax, RLax, LangGraph, FAISS, SQLite 等多种技术栈)",[13,16,14,102],"其他",[104,105,106,107,108,109,110],"aiagents","artificial-intelligence","data-science","dataengineering","deep-learning","machine-learning","neural-network","2026-03-27T02:49:30.150509","2026-04-11T21:46:54.823124",[],[]]