Solving Claude Code All at Once for Beginners - A Lecture by the Author of Claude Code Master

This is a partial lecture from the theory section of a comprehensive career guide course that covers everything you need to prepare for developer job hunting and career transitions, including resumes, portfolios, technical interviews, and personality interviews. The theory section covers the process of finding companies that fit you, analyzing job postings, preparing for interviews and coding tests, and everything you need to know to prepare your resume and portfolio.

Learn how to utilize RabbitMQ to implement asynchronous architecture—the most fundamental method for processing large-scale data—through a tutorial format designed for easy understanding from basics to advanced applications based on Spring Boot 3.3. Learning materials (Notion links and Git Branch URLs) are provided for each lecture chapter. Please be sure to check the class notes.

This course allows you to experience a comprehensive list of questions and answer strategies for both technical and behavioral interviews. In particular, the curriculum is designed to help you prepare for interviews by learning—through Q&A—server scaling strategies and large-scale data processing strategies essential for junior-level technical interviews, as well as how to provide the best answers to open-ended questions typically encountered in final interviews.

This is a career guide course that covers the entire process of landing a developer job or switching careers, including resumes, portfolios, technical interviews, and behavioral interviews. The theory section outlines the process of finding the right company for you, analyzing job postings, and preparing for interviews and coding tests.

This will be the definitive guide to Claude Code. This course dives deep into how and why Claude Code works, teaches you how to directly apply the components of harness engineering, and even dissects actual production code. Take the know-how of a developer with over 10 years of experience and transform into a key talent in the era of AI agents.

Build a Kiwoom Securities API-based automated stock trading system from scratch using the core features of Claude Code (MCP, Agents, Skills). It's perfectly fine for beginners. Get started with Claude Code right now!

This course is a practice-oriented lecture that goes beyond 👉 just explaining the concepts of RAG (Retrieval-Augmented Generation), 👉 to building the actual working architecture yourself, 👉 and experiencing expansion and optimization. Starting from simple RAG examples, you will learn step-by-step—from Advanced RAG to Modular RAG and Agent-based RAG—at a level that can be immediately applied in the field.

This is a process of developing a practical web service in collaboration with AI agents based on TARS prompt engineering principles. You will gain full-stack development experience covering everything from UI/UX component creation to CRUD functions, API integration, and data visualization, ultimately completing your own idea as a viable MVP.

Document (SDD) → Code Automation. Make the system work instead of typing prompts. Build your own AI development agent with Skills, Sub-agents, and Commands, and complete monetization services through 5 real-world projects.

AI agents, the latest trend in generative AI! How to stay ahead with Cursor, which handles all your development tasks, and MCP, which expands its capabilities!

If you follow OpenClaw Parts 1 and 2, you eventually end up making a single agent do everything. As you do this, there comes a moment when prompts get tangled, memory gets mixed up, folders get messy, and you feel stuck, not knowing how to organize things anymore. This lecture is the "Introduction to Multi-Agents (Part 2.5)," designed to help you move one step further from that point. While maintaining a single bot, we will separate roles into two agents—planner and editor—and connect each agent to different workspaces and Telegram forum topics. You will personally build a structure where "different agents respond depending on which room you speak in." By following the hands-on practice, you will have the following in your environment: - A configuration file defining two agents: `content_planner` and `content_editor`, - A content folder structure where planning and drafting are separated, - A Telegram group chat where different agents are attached to specific forum topics, - And one actual draft of an Inflearn course introduction created through the collaboration of the two agents. In short, this is a practical class that helps you design the first step of upgrading your Telegram bot from a "single AI assistant" into a "small AI team with divided roles."

This is a practice-oriented course for experienced developers with a computer science background transitioning to the latest AI technology stack. From the design to the implementation of LLM-based applications, you will establish AI solution adoption strategies by leveraging existing software engineering knowledge. Rebuild your career by integrating traditional development experience with modern AI technology.

From coding basics to running your own web service all at once?! Web Build All-in-One, connecting fundamentals and practical application.

Simple tutorials alone make it difficult to apply in practice. I will clearly pass on my professional know-how, covering complex state management and multi-agent design methods.

[Let's Build!] Have you ever given up because you were tired of connecting nodes? "Create a daily morning briefing on Slack" — that's all it takes. Claude Code designs it, and the n8n server runs it 24/7 for you. [Let's Operate!] If adopting n8n was easy but managing it felt overwhelming, this lecture is the answer. "Organize everything according to the conventions" — dozens of workflows are tidied up at once. Creation, organization, monitoring, and backup—operate everything using natural language.

Let's create a service that improves itself even while you sleep! Learn step-by-step how to build a 24-hour vibe coding system using loop engineering with Codex.

Learn Claude Code Faster Than Anyone | Developer Edition Learn the trending Claude Code quickly (installation, usage, skills, sub-agents) and create your own project!

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

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.

If you keep getting errors after asking AI to do it, the problem isn’t the AI—it’s that you lack the foundational knowledge to guide it. In this video, we’ll cover the seven foundational concepts that people who get stuck while vibe coding were missing, and explain what each one is and why it’s necessary. 👾 This is explained for people on day zero of coding who have never opened a terminal before.

Your colleagues are already using Claude Code for their work—are you still doing everything manually? Don't worry! This course will turn you into a vibe coding master.

From AWS EC2 backend operations to automatic error fixing, we are revealing the entire development process of Sidabari (https://github.com/cx8537/sidabari), a Claude Code-based automation tool. In this course, we provide all the actual prompts and conversation history used as learning materials, allowing you to experience the entire prompt input process involved in its creation.

Are you confused about which technology trends really matter among the flood of information? We've analyzed global reports from Gartner, Microsoft, and others to clearly explain 10 key technologies (multi-agent systems, physical AI, AI security, etc.) that will define 2026.

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.