AI Agent Engineering

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

Silicon Valley Internship and 30 Big Tech System Design & Open Source Practical Contribution Completion Course that survives even in the AI era. All students who submit their missions can apply for an internship at a local AI startup in Palo Alto, USA, and will be granted an interview opportunity with a document screening waiver. This course involves designing and scaling 30 practical system designs based on Big Tech cases such as Netflix, Tesla, Meta, and Tinder. Finally, you will design your own service from architecture to DB to scaling, and complete your portfolio and resume by contributing to an actual operational open-source AI coding agent project (which has achieved 8.6K+ cumulative downloads).

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

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.

I highly recommend Spring AI to developers using Spring Boot and React, as well as those considering the introduction of AI systems in corporate environments. This course goes beyond a simple introduction to libraries; it helps you understand the core concepts and internal structure of Spring AI, and strengthens your practical implementation skills through various hands-on examples. The course is based on Spring AI version 1.1.x and provides continuous technical support and expansion strategies to ensure you can adapt to future version upgrades. Furthermore, we expand the practical value of Spring AI technology by utilizing MCP (Model Context Protocol) to achieve cross-domain context integration, AI linkage in distributed environments, and AI architecture design suitable for enterprise environments. With the goal of building AI systems that can actually be deployed in corporate settings, this will serve as a practical guide that balances theory and hands-on experience.

How long are you going to manually look through 2,500 stocks every day? Searching through Naver Finance to find surging stocks after the market closes, Reading news to judge whether it's good or bad news, Checking foreign/institutional supply and demand one by one, Opening charts to analyze patterns... Aren't you repeating this every single day? I used to do that too. I spent 2 to 3 hours after work analyzing stocks, and yet I still missed more stocks than I found. With over 2,500 stocks across KOSPI and KOSDAQ combined, it's impossible for a human to see them all every day. --- So, I built a system. I created a system that automatically analyzes 2,500 stocks every day after the market closes. - Automatically collects market prices, supply/demand, and news - AI (Gemini) reads the news and determines if it's positive or negative - Scores stocks out of 15 points based on 6 factors - Selects only the stocks that pass the criteria and calculates entry, stop-loss, and take-profit prices - Sends notifications via Telegram Now, I just check my phone after work. Additionally, every Saturday, the system analyzes its own performance from the past week and automatically adjusts stop-loss lines, take-profit lines, and holding periods. It is a structure where the system learns and improves on its own. --- But I am not a developer. I didn't write the code for this system myself. I gave all the instructions to the AI verbally. "Filter out stocks that rose more than 5% today with a trading volume exceeding 50 billion won." "Send these 3 news articles to Gemini and have it judge if they are positive." "Create a scheduler so this runs automatically every day at 4 PM." When I speak like this, the AI (Claude) generates the code. This is "Vibe Coding." --- In this course, we will build this exact system. Over 58 lessons, we will build the system I actually use every day from start to finish. Starting from data collection, AI news analysis, scoring engine, signal generation, Flask API server, Next.js web dashboard, Telegram automatic notifications, and even the self-learning system. The final product is not a Jupyter Notebook. The final product is a web dashboard and Telegram notifications that actually run every day. You don't need to know how to code. In every lesson, I will show you how to talk to Claude, and if you follow along, you will get the same results. --- Recommended for: - Office worker investors who lack the time to analyze stocks every day - Those who want an automated system but don't know how to code - Those curious about quant/system trading but don't know where to start - Those curious about how to utilize AI in practice --- ⚠️ This course does not guarantee investment returns. This is a programming course on building your own stock analysis tools. Actual investment decisions are the responsibility of the student.

Are your colleagues already working with ChatGPT while you're still doing everything manually? Don't worry! After taking this course, you'll become an AI expert too.

[2nd edition scheduled for completion in 2026] We explore how to create an AI judge by combining various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology, along with knowledge graphs and reasoning systems.

Create a program using the GPT API, and even generate images and videos!

You can learn the core concepts of complex Next.js in a short time. We'll teach you only the essential practical knowledge for those learning Next.js for the first time. Captain Pangyo's new Next.js course, who has been conducting framework courses for 8 years. You can learn the coding and learning methods of "Frontend Developers in the AI Era"! 😊

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!

After ChatGPT was released in 2022, AI has become a very close software in our lives. The performance of models is improving day by day, and many new software applications that didn't exist before are emerging using AI. In 2025, as AI agents become a hot topic, many people are starting to develop AI agents. You may have heard the term AI agent, but this is an introductory tutorial that will help you understand what an AI agent is, why it has suddenly become a topic of discussion among so many people, how to create an AI agent, and how to easily build one using ADK.

Are you still manually planning and writing marketing reports these days? This course covers how to use GPT to establish strategies, automatically execute ads and content, and even generate real-time KPI reports. Experience the "efficient way of working" that every marketer wonders about firsthand in this course.

The goal is to master the complex GPT API and Python in the easiest way possible through hands-on practice. You will develop a YouTube video summarization AI using the latest ChatGPT API and implement it as a web application using Streamlit.

This is a Vibe Coding lecture where you can create a real-world web service using public data and Google AI Studio (free), even with no prior coding experience. We will build a complete service in Korean—including search functions, map integration, and AI analysis reports—using the Public Facility Safety Information API. You will learn the entire development process, from PRD planning to deployment, which can be applied immediately in practice.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

The era of creating designs one by one is over. Experience the speed of turning "thoughts" into "results" with Canva AI.

This course covers how to leverage AI in the food and pharmaceutical manufacturing industry to enhance productivity and quality. Through real-world case studies, you'll learn how AI is applied in key stages such as raw material management, production processes, quality inspection, and regulatory compliance, while understanding defect rate reduction and efficient operational strategies. Through this course, learners will master ▲characteristics of the food and pharmaceutical industry and the necessity of AI adoption ▲data-driven quality management ▲predictive maintenance and equipment optimization ▲regulatory compliance monitoring and risk management ▲analysis of domestic and international implementation cases, gaining strategies and insights that can be immediately applied in the field.

This course goes beyond simple trends, covering how to systematically learn to collaborate with AI tools, verify and improve output quality, and build and deploy applications from start to finish. Anyone can take this course, from developers interested in AI to team leaders looking to boost team productivity.

AI will not replace people. However, 'you who utilize AI' will replace everyone who does not.