[n8n Beginner Course] How to Design n8n + AI Workflows

Beyond using AI just for copy-and-paste tasks, learn how to connect Claude Chat, Cowork, MCP, n8n, Supabase, and Vercel to actually automate repetitive work for non-developer office workers. Set in a fictional company called DingcoLabs, this course walks you through hands-on PM, marketing, sales, customer service, and HR scenarios to build a workflow automation system you can put to use at your company right away.

"Work automation... isn't that something only developers do?" "Even if I take an automation course, won't it be difficult without development knowledge?" No, it's not. Even if you aren't a developer and have zero development knowledge, you can apply work automation to your actual tasks. This course was created by me—someone who started as a complete beginner with no coding knowledge—for other complete beginners. Even if you are currently at a level where you've only tried asking questions to AI, you can fully understand and follow along. We will directly build automation cases actually used in the workplace, such as receiving daily automated summary reports of insightful articles and automatically detecting and announcing company anniversaries. Through this process, you will understand the 'methods of work automation' and gradually level up to build your own automation systems. Complete your first automation that can be applied to your work immediately in this course.

Claude Code handles judgment and execution, while n8n takes care of triggers, connections, and scheduling. By following the sequence of Concept → Connection → Practice, repetitive tasks are designed into a flow of Input → Judgment → Execution → Verification → Delivery. With error handling, retries, and human approval included, the system operates unmanned through scheduled execution. Even non-developers can build their own automation systems without having to learn coding from scratch.

Learn how to utilize various local LLMs (Qwen, Gemma) and explore different techniques to efficiently evaluate and improve the performance of LLM systems.

Complete Business Innovation with Google Workspace, Gemini, and NotebookLM! This is a practical course that takes you step-by-step through setting up a Google environment, utilizing Gemini, advanced searching, managing notes/tasks/schedules, using Drive and Photos, collaborating via Gmail/Meet/Chat, working with Docs/Sheets/Slides, creating images and videos, building forms and sites, analyzing data with NotebookLM, and automating tasks using Colab and Apps Script. Rather than a simple introduction to features, the course is centered on hands-on practice that can be immediately applied to real-world work scenarios, such as searching, document drafting, data organization, presentation creation, application processing, rulebook chatbots, schedule registration, and automated survey generation. 🚀 Key Course Points 1. Integrated Smart Work Connecting the Google Ecosystem Google allows you to organically connect and use search, email, calendar, documents, drive, and collaboration tools with a single account. This course helps you master this connection structure centered around your workflow. 2. Immediate AI Application and Automation The course covers document writing, summarization, chatbot configuration, schedule generation, survey creation, and automated email dispatch using Gemini, enabling you to transform repetitive tasks into actual business automation. 3. Data-Based Analysis and Highly Reliable Business Application By utilizing NotebookLM to find answers only within uploaded sources and performing summarization, analysis, learning, and report writing based on citations, you can increase the reliability of your practical work. "Add AI wings to the Google ecosystem." Now, leave the complex tasks to AI agents and focus on more valuable decision-making.

Developing an 'Autonomous' Premium Reservation Agent Backend Using Spring AI + JPA + MySQL Beyond Chatbots to Agents: The Standard of Tool Calling and Prompt Engineering

3 projects that compress 20 carefully selected concepts will build 80% of your n8n skills.

We invite you to become a ‘Top-Tier Enterprise AI Systems Architect’ Do not remain a micro-level technician (Prompt Writer) who merely assembles libraries created by others or polishes prompt sentences. From mathematical first principles to runtime control-loop kernels and autonomous recovery infrastructure — through 40 years of practical engineering insight that has driven industrial operations in South Korea, I will firmly guide you to become a top-level ‘Enterprise AI Systems Architect’ who protects your company’s business and digital sovereignty. Now, begin the great journey of transforming uncontrollable AI into a perfect industrial core. “Why does an AI that ran perfectly in a demo start spouting strange nonsense or bring the pipeline to a halt as soon as it is deployed to production?” “Why do AI projects that so many companies launch with great enthusiasm ultimately fail to escape the ‘PoC (Proof of Concept) swamp’ and get abandoned?” These are the most desperate questions that countless developers and systems architects I have met in the field have asked me. Hello. I am Hongjip Kim, who spent more than 40 years in the powertrain R&D division of the South Korean automotive industry (including 17 years as an executive in Hyundai Motor Group R&D), led the export of engine and drivetrain technologies worth 130 billion won, and commanded operations as the CEO/CTO of a listed mid-sized manufacturing company. From the perspective of an engineer who has spent decades in mechanical engineering and precision-control environments, a ‘risk of fatal uncertainty’ lurks behind today’s frenzy to adopt generative AI. In mission-critical industrial environments where even a mere 0.1% error or mistake can directly result in the destruction of physical equipment worth hundreds of millions of won and an enterprise-wide shutdown, integrating a probabilistic chatbot that simply relies on probability to generate plausible text directly into a service is like carrying a ticking time bomb into a fire. This course was designed to impart the “practical engineering methodology for training uncontrollable probabilistic AI into the most robust industrial control core, one that allows not even 0.000% of syntax errors.” 1. The 3 Major Fatal Bottlenecks You Face in the Field (Problem) Many engineers attempt to challenge production environments using an approach limited to one-off prompt tuning (Vibe Prompting), only to become frustrated when they encounter the following 3 structural defects: Finite Context and ‘Lost in the Middle’: As conversations or multistep executions grow longer, critical system instructions are pushed aside and the context window is exhausted, causing the system to lose its memory and amplifying hallucinations. Nondeterministic Outputs and Cascading Pipeline Collapse: Tiny probabilistic fluctuations, such as one missing closing bracket in JSON or an incorrect type, can paralyze legacy ERP/CRM API calls and destroy data integrity. Untrusted Code Execution and Infrastructure Security Bankruptcy: When scripts autonomously generated by an agent run in a non-isolated host OS environment, they create risks such as kernel escapes and sensitive-data leakage. As such, AI malfunctions in industrial environments are not merely quantitative accuracy problems; they become a massive financial disaster in the form of asymmetric business loss (Asymmetric Business Loss). 2. ‘How’ to Solve These Problems Perfectly (Solution) This course differs fundamentally from commercial courses that teach only how to use fragmented off-the-shelf tools. By combining 40 years of hands-on field expertise with computer systems engineering, it delivers 3 hardcore architectures for vertically integrated, system-level control of probabilistic AI: ① Move Beyond Prompting to ‘LLM-OS & Context Orchestration’ Following Andrej Karpathy’s paradigm, redefine the LLM not as a conversational respondent but as the central computational core (CPU) of a computer. Systematically organize the context window as ultra-low-latency volatile RAM, the internal vector DB as Disk, and external APIs as an I/O bus. Through MemGPT hierarchical virtual-memory paging, preserve critical state without saturation even during infinite conversations, while scaling a dynamic agent network through MCP-based Liquid Swarm MAS, minimizing token costs. ② ‘Deterministic Harness Engineering’ That Constrains Probabilistic Hallucinations to 0.000% Leave the language model’s creative flexibility open only during the Reasoning phase, while firmly confining the physical output layer that interacts with external systems using mathematical Boolean algebra and compilers. Apply EBNF / XGrammar-based CUDA logit masking to forcibly block the generation probability of invalid tokens at P = 0.000%. Form a physical security isolation ring so that code generated by agents runs only once inside a Firecracker MicroVM micro-virtualization sandbox with 5ms snapshot restoration capability. ③ ‘High-Reliability MLOps·LLMOps Autonomous Recovery Infrastructure’ Without Wasting GPU Resources Build vLLM PagedAttention and S-LoRA hot-swapping to suppress idle VRAM waste to below 4%. When a multi-agent API fails, restore data consistency 100% through SagaLLM reverse compensation transactions. Through an OpenTelemetry/Langfuse distributed observability network, defend system availability by executing an unmanned automatic canary rollback whenever a decline in the RAGAS faithfulness score (Data Drift) is detected. 3. Practical Value That Drives Spiral Growth (Pedagogy & ROI) This course is systematically designed according to the 5 levels of Bloom's Taxonomy. Starting from simple theoretical recall (Remember), learners directly code and validate a 5-stage debugging pipeline (analyzing deceptive baseline trap source code ➔ deconstructing adversarial engineering ➔ establishing a Robust integrity pipeline ➔ calculating the TEI ROI formula ➔ SOTIF/ISO 42001 governance). Going beyond the intuitive satisfaction of simply “working well,” I will apply the Forrester TEI (Total Economic Impact) financial model to visualize, from an executive perspective, how much the AI system you build reduces your company’s rework cost (Rework Cost), avoids failure risks worth hundreds of millions of won, and proves an ROI of more than 200%.

Do you still code manually these days? We are now in an era where even "vibe coding" feels like a chore! Become an AI automation expert with n8n, no coding required. A guide to designing automation workflows that can be applied immediately to real-world tasks. Practical automation strategies to maximize work efficiency. Build intuitive workflows with n8n and experience a revolution in the way you work.

Workplace innovation completed with Google Workspace, Gemini, and NotebookLM! This is a practical course that takes you step-by-step from setting up the Google environment to utilizing Gemini, advanced search, managing notes/tasks/schedules, using Drive and Photos, collaborating via Gmail/Meet/Chat, working with Docs/Sheets/Slides, creating images and videos, building forms and sites, analyzing data based on NotebookLM, and automating tasks using Colab and Apps Script. Rather than a simple introduction of features, it is structured around hands-on practice—such as searching, drafting documents, organizing data, creating presentation materials, receiving applications, building rulebook chatbots, registering schedules, and automatically generating surveys—so that it can be applied immediately to real-world business scenarios. 🚀 Key Course Points 1. Integrated Smart Work Connecting the Google Ecosystem as One Google allows you to organically connect and use search, email, calendar, documents, drive, and collaboration tools with a single account. This course helps you master this connection structure centered on workflow. 2. Immediate AI Practical Application and Automation It covers everything from document writing, summarization, chatbot configuration, schedule generation, survey creation, and email automation using Gemini, enabling you to transform repetitive tasks into actual business automation. 3. Data-Based Analysis and Highly Reliable Business Application By using NotebookLM to find answers only within uploaded sources and performing summarization, analysis, learning, and report writing based on citations, it enhances the reliability of your practical work. "Add AI wings to the Google ecosystem." Now, leave the complex tasks to AI agents and focus on more valuable decision-making.

Which AI tool should I learn first? Stop worrying now! - Rated 4.8 by 80 students ⭐️⭐️⭐️⭐️⭐️ - ChatGPT, Claude, Gemini, Genspark, NotebookLM, Gamma, Skywork, Manus, Antigravity, Claude Code, MiriCanvas, Opal, CapCut, and more - Learn the core features of over 15 tools very easily over 20 days. - Through weekly assignments, we provide a course you can "internalize" rather than just "passively listen to." - All-in-one coverage: Prompt Engineering > Image Generation > PPT & Video Creation > Vibe Coding - 4 hours of lecture videos + weekly study materials + assignment certification system

Have you introduced AI but still find yourself losing time to repetitive tasks? The problem isn't the AI; it's the 'system.' This course guides you through the process of building a team of six AI assistants—including an executive assistant, content assistant, marketing assistant, CRM assistant, and personal productivity assistant—all using Notion AI. Based on my experience automating hundreds of repetitive tasks in real-world business settings, we will build actual workflows from start to finish, covering everything from organizing notes and automatically generating meeting minutes to blog writing, CRM construction, and automated KakaoTalk notifications. After completing this course, you will have a system where your own AI assistant team works automatically, powered solely by a Notion Business plan.

One of the greatest challenges I felt while working in HRD at a human resources development center for 12 years was that while I had plenty of ideas, the coding barrier to implementing them was simply too high. This course starts from that very point. The goal of this course is to help HRD professionals implement their own ideas without having to become developers. This is a practical, vibe-coding-based course designed for those in HR, HRD, General Affairs (GA), and Organizational Culture. We will build the services you want using only free features, without the need for paid services.

Learn how to efficiently manage multiple AI coding agents, including Claude Code, Codex, and pi, from a single screen. Starting with tmux concepts, you’ll practice using Herdr’s sessions, panes, plugins, and CLI to build an agent workflow that keeps your work running even when your SSH connection is interrupted. Complete the essential development environment for the Agent era in just one day.

Automate YouTube Shorts creation with AI!! Learn how to automatically create YouTube Shorts using n8n from scratch. We will personally build a workflow that handles everything at once: Text → Image/Music → Video Creation → Uploading. Practice includes distinguishing between test/production modes and tips for saving costs. By the end of the lecture, you will complete 3 automation templates that can be used immediately. We will learn step-by-step, starting from node placement, so you can follow along even without coding knowledge. Join us in automating AI YouTube Shorts production!

While your colleagues are already working with Claude Code, are you still doing all of this manually? Don’t worry! Take this course, and you too will become a vibe-coding master.

This session is prepared for those who already have some understanding of or experience with automation or n8n. It covers the real-world problems and solutions Selfish Club encountered while building DB and CRM automation using n8n, so we highly recommend it to anyone considering implementing n8n automation but unsure of how to start.

Beyond a chatbot that simply generates answers, you will directly implement an autonomous AI Agent that makes its own judgments, calls tools, and performs multi-step tasks. You will learn through step-by-step practice, covering everything from OpenAI Agents SDK's Tool Calling, Handoff, Guardrail, Session, and Tracing to connecting external tools and databases using MCP, as well as LangChain and Deep Agents' Planning, Subagents, Filesystem, and Memory. The ultimate goal is to equip you with the ability to design and implement tool-integrated AI Agents that can be applied to real-world tasks.

AI writes on Slack and Notion for me? Work smarter with MCP, which goes beyond the limits of existing AI!

We're building an AI-powered video conferencing application with real-time AI agents, meeting summary features, and post-call functionality.