Practical Harness Engineering for Senior Engineers at Korea’s Top Tech Companies: Claude Code in a Real-World Spring Boot Environment

Building an MVP with AI is no longer difficult. However, most projects stop at the next stage. 👉 You’ve built the features 👉 But development doesn't continue Why does this happen? The problem isn't the code; 👉 It's because there is no structure that allows AI to work continuously. --- In this course, based on an existing project, 👉 We will cover the process of building a structure 👉 That allows AI to continue development on its own. --- Instead of simply using AI tools, we will: * Create a docs structure * Define the SSOT (Single Source of Truth) * Execute development on a per-ticket basis * Connect QA and iteration cycles 👉 To complete a single "AI Development System." --- Through this process, you will: 👉 Create a structure where development continues 👉 Even without a human manually coding. In other words, you can: 👉 Build and understand a development system 👉 That operates AI like a team. --- This course is designed for: 👉 Those who want to build a structure applicable to their projects immediately 👉 Those who started development with AI but found it difficult to sustain 👉 Those who want to move to the next level after "Vibe Coding" --- Beyond just learning, we provide an experience where you: 👉 Take action 👉 Build a structure that actually works 👉 And walk away with a framework you can apply to your own projects.

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!

Instead of writing longer prompts, I create rule files, logs, permissions, hooks, and validation routines one by one whenever something goes wrong in a real project. With a news alert bot that I actually built and deployed to a server in five days, 211 screenshots and 87 instructions exactly as originally written.

The time when your imagination becomes reality—welcome to the world of 'Vibe Coding.' We prepared this course for those of you who may not know coding but know exactly what you want to create. With the emergence of AI tools like ChatGPT and Claude, people say that anyone can develop software. But what happens when you actually try? - "It writes simple code well, but once the number of files increases, the AI forgets what came before." - "It looks plausible, but when I run it, errors occur—and when I ask it to fix them, it gives me even more unrelated code." You’ve probably had experiences like these at least once. I experienced the same frustrations when I first started collaborating with AI. AI was an excellent 'assistant,' but it had limitations because it couldn’t see the entire forest. However, after discovering OpenAI Codex CLI and learning a new approach called 'Vibe Coding,' my development process completely changed. This course will solve the problems you’re experiencing while transforming you from someone who isn’t a coder into a director who commands AI agents. Equip yourself with the powerful tool Codex CLI, and join me on the exhilarating journey of Vibe Coding, where imagination becomes reality.

Are you feeling stuck on how to apply what you've learned to real-world tasks even after mastering the basics? I will help you complete complex RAG and agent designs using my professional industry know-how.

Do the results of vibe coding keep turning out differently from what you intended? Learn a workflow that uses specification-driven development (SDD) and Spec Kit to define goals, scope, business rules, and completion criteria, then shares the same specifications with Claude Code and Codex to reduce repetitive revisions.

From coding basics to launching your own app all at once?! The App Build All-in-One, connecting fundamentals and practical application.

This is an advanced course focused on designing and implementing Multi-LLM architectures and orchestration-centered Agent systems (Main/Sub, Tool, Task Runtime, Agent Registry) by strategically combining GPT, Gemini, and LLaMA (local) based on Spring AI and Spring Boot. Moving beyond simple LLM calls, the curriculum covers the implementation of scalable, stable, and continuously improving AI systems. This involves applying Agentic Workflow Patterns (Chain, Parallel, Routing, Orchestrator–Workers, Evaluator–Optimizer) and Multi-Agent structures, separating execution layers like RAG and external APIs/DBs via Tool/ToolRegistry, and utilizing DAG engines, YAML declarative workflows, and Validated DSL (validation immediately after loading). Furthermore, the course includes Circuit Breakers, Reactive Streams, Redis monitoring, parallel processing, and iterative evaluation loops. It expands from Thymeleaf (SSR) exercises to decoupled Front-end/Back-end architectures using React and REST, while integrating tool and agent runtimes via MCP (Model Context Protocol) to build AI architecture design capabilities at a production-ready level. The ultimate goal is to evolve beyond being a simple AI user who merely integrates APIs and writes prompts, becoming a developer capable of designing AI systems—explaining and balancing Multi-LLM, agents, workflows, declarations, and validation within a single execution architecture.

AI is only as good as you are. Learn vibe coding properly in three steps: how to use AI → essential foundational knowledge → project development. For those who want to learn vibe coding the right way, we deliver the core concepts that remain unchanged regardless of which AI you use, broken down into bite-sized pieces.

Learn the architecture of Decision AI, which assesses situations and selects appropriate actions. Through hands-on practice with the JEV Decision Model and the open-source Laya model, you will learn how to apply confidence-based decision-making, policy integration, and Human Review design in real-world settings. You will also design your own validation framework to automate repetitive decision-making in your work.

This course trains you to think in terms of frontend system design through real-world service cases such as Threads feeds, Amazon shopping carts, the Netflix streaming UI, Google Docs collaborative editing, Micro Frontends, Agentic UI, and Observability.

Messy code, a flood of requirements... If you're looking for a breakthrough with AI, Learn how to become a "high-performing developer" by leveraging AI in legacy environments, taught by the former VP of Engineering at Toss Payments. Legacy code isn't going anywhere, even in the age of AI. It’s no exaggeration to say that every company has legacy code, unless they were founded today. So, how should we utilize AI in this reality? In this course, the former VP of Engineering at Toss Payments, who led a large-scale payment service organization, demonstrates firsthand how to work effectively using AI in legacy environments. This isn't just a course about how to use AI or basic coding skills. The goal is to help you develop your "critical thinking" as a developer and truly understand what it means to work effectively.

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.

A comprehensive course covering AI-era-proof Silicon Valley internships, 30 big-tech system designs, and hands-on open-source contributions. Using real-world cases from Netflix, Tesla, Meta, Tinder, and other big tech companies, you’ll design and scale 30 practical system designs. Finally, you’ll design your own service from architecture to database to scaling.

: "From environment setup to 7 practical projects, a deep learning full-stack journey across data domains (35 lectures in total)" Go beyond simply following example code and learn how to cook various types of real-world data using deep learning. Starting from building CPU/GPU development environments, you will cover waste sorting classification (Image), stock price prediction (Time-series), ECG anomaly detection (AE), animation stylization (GAN), medical image segmentation (U-Net), and sound classification (Audio)! Systematically master core deep learning algorithms through hands-on projects.

If you feel lost about what to learn in an era where AI writes code, you can find clear answers in the principles of UNIX and the kernel—the unchanging essence of systems. Through a practical project that integrates Node.js with a C language engine (FFmpeg), this course delivers the perspective of the top 1% of architects who command entire systems without being swayed by technology trends.

[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.

Learn how to automate daily tasks using AI tools and apply AI code to actual service development. We provide practical know-how that everyone from developers to non-developers can immediately apply to their own work environments.

For those of you who have completed intermediate SwiftUI, it is now time to integrate AI into your apps. Complete an on-device AI app that runs locally without an internet connection using Apple Intelligence and the FoundationModels framework.

Now we need to move beyond the beginner level and reach the level of a professional developer. I’ll reveal how games are actually made in the industry.