Hands-On Web Security Practice: Attack and Fix It Yourself, Part 1 — Beginner: Learn the OWASP Top 10:2025 with Spring Boot

Reactor, one of the core technologies of Spring WebFlux-based reactive applications, and Reactor Operator, which can be said to be the most important part of Reactor, if you want to make it yours, take the `Kevin's Easy-to-Understand Spring Reactive WebApplications: Reactor Part 2` lecture.

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.

This is the first lecture in the Clean Spring series, a course designed to let you experience the process of a fictional startup development team building a fast, stable, flexible, and maintainable system by following clean code and Spring development principles in a way that mirrors real-world work. You will learn the techniques and development strategies required for the initial domain-driven development phase using the Domain Model Pattern and Hexagonal Architecture.

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

The most definitive practical guide for developers who want to integrate AI into their Spring projects right away! Beyond simple LLM API calls, you will perfectly master building production-level AI environments that can be immediately applied to real business logic using RAG and MCP.

This course is a hands-on experience in load testing a Spring Boot server using K6. You will experience the process of generating traffic to a simple CRUD-based server with K6 and visualizing the results using Grafana and InfluxDB!

If you run your database in Docker, you’ll learn how to reproduce 30 scenarios on your own computer that can wipe your data or make it unreadable—from `down -v` and `prune` to folder names, password environment variables, 10-second shutdowns, memory limits, image tags, and volume-copy backups. You’ll read the database logs to find the cause, then learn how to restore your data using point-in-time recovery.

Have you wanted to implement RAG but felt overwhelmed by connecting everything from embeddings to vector storage and search? Based on real-world service development experience, this course explains the core concepts of pgvector and the service source code. By calling the API directly, you can quickly understand the entire flow of how RAG vector search works.

This course is a practical-focused learning program for JWT-based token authentication and security enhancement techniques that are essential in modern web service environments. Using Spring Boot 3, Spring Security 6, and React, you'll implement secure login systems and cover comprehensive security practices including XSS·CSRF countermeasures, HTTPS implementation, token reissuance and revocation strategies, and secure secret key management. Additionally, you'll practice real service-level environment configuration through AWS build·deployment and performance optimization using Redis, and the complete project source code provided in the course can be directly applied to real-world work.

For those aspiring to be Java-based Spring backend developers, to easily secure a job, we learn Java properly! Rigorously!

There are moments when a price changes in another window but doesn’t show up on your screen, even though it says “Connected.” Let’s walk through real execution logs, step by step—from how to choose between polling, long polling, SSE, and WebSocket with two virtual auction windows open, to resynchronizing after a disconnect and tracking changes across multiple servers and proxies.

Building an 'Intelligent Collaboration' Expert Agent Team Using Spring AI Router Pattern + RAG + MCP Beyond a Single Agent to Architecture: The Standard for Router Patterns and Agent Isolation Design

An AI system that grades and reviews your code in just 5 seconds—imagination becomes reality. This course is an all-in-one project curriculum for creating a **'GitHub PR Auto-Analysis and Grading Bot'** using Spring AI. When a student submits an assignment (Pull Request), the server detects it, and the AI analyzes the code changes (Diff). The grading results are then stored in a database, feedback is automatically posted as a GitHub comment, and the student can check their results on a dedicated dashboard. From backend to AI integration and frontend (Vaadin), all with just Java! This is the ultimate introductory guide for any developer who wants to build their own AI service from A to Z.

This covers the major updates from Java 9 to Java 21 in detail. It will be continuously updated as new versions are released up to Java 31.

This course goes beyond basic CRUD-level database usage, helping you develop the PostgreSQL skills needed in the AI era from a practical, real-world perspective. Drawing on AI development experience in Silicon Valley, you’ll gradually learn how to design and use JSON, indexes, transactions, and pgvector in actual services, while gaining the skills to solve complex data structure and performance problems.

If you’ve implemented AI features with Spring AI, it’s time to think about how to observe how AI behaves and control its costs, quality, risks, and workflows. Simply calling an LLM, connecting RAG, and receiving structured responses isn’t enough to make an AI application reliable. In a real application, you also need to consider how each request was handled, how much context you’re providing to the model, how much you can trust the generated results, and how to limit risky recommendations. In this course, we use a Spring AI–based Incident Responder application as a consistent example. Starting from a baseline that works but isn’t yet sufficiently controlled, we observe actual execution results, identify problems, add control points to the code, and compare the results before and after the improvements. Along the way, you’ll learn more than just how to use another AI feature. You’ll develop a design perspective on how to observe, constrain, and validate probabilistic AI within Java/Spring applications.

Are you using Java but still only utilizing Java 8 syntax? Discover the useful new features added in Java 11 LTS and become a more proficient developer by using Java more conveniently. The features introduced here can be used effectively in Java 11 LTS, 17 LTS, and 21 LTS versions.

Spring Web Programming learned while creating a calendar (schedule management)! Completely master Spring with examples you can use right away, not just short snippets~

Drawing from my own experience of feeling despondent when trying to implement practical code during my first MSA transition—despite knowing the theory—I will help you grow into a developer who guarantees perfect data consistency amidst any system failure by directly rolling back daunting compensation logic through 9-step practical patterns and AI Agent coding.

Why do requests wait when queries are fast? Locks. In a hands-on project using MySQL 8.0, Spring Boot, and JPA, reproduce record and gap locks, four types of deadlocks, long transactions and connection pool exhaustion, metadata locks during deployment, and lost updates with tests. Then fix them in code and verify the fixes with the same tests.