Practical preparation for coding tests and interviews, directly taught by interviewers from Naver, Kakao, Line, Coupang, and Baemin.

You may have experienced Claude Code producing different results for the same request each time, violating the rules specified in its instructions, and being overly lenient in approving its own output. While developing platform servers in Pangyo and spending over $1,000 a month on Claude Code, I addressed these issues by building a harness consisting of instructions, Hooks, permissions, and a Sandbox instead of relying on prompts. In this course, you’ll build that harness from scratch, add Subagents, Agent Teams, and headless execution, and leave with a complete setup that you can copy and use as-is in other projects.

This course is for developers who have built an agent but are unsure how to connect it with other services and frameworks, or what formats to use when exchanging long-running tasks, approvals, statuses, and results. Using a LangGraph agent as the foundation, you will implement the A2A protocol yourself and learn step by step about Agent Cards, Tasks, Artifacts, streaming, authentication, webhooks, and gRPC. Finally, you will verify compatibility with the official SDK.

For developers blocked by double payments and discrepancies between external payments and databases, I design and implement robust settlement systems based on hands-on experience with Toss’s backend.

You know about AI agent frameworks, but understanding how they work and applying them in practice can be difficult, right? Drawing on firsthand experience from Silicon Valley, we’ll guide you from the core principles to practical applications.

The limitations of LLMs? I’ll share practical solutions to real-world problems based on RAG trial and error and operational know-how from working in Silicon Valley.

If you’ve ever had trouble determining whether the quality of responses declined after changing a prompt or model while building a service using an LLM, this course may be the solution. You’ll learn how to create your own evaluation criteria and automatically identify responses that have changed.

It was created to solve the problem of “not knowing where or how to start with Kubernetes.” Based on hands-on experience gained from operating financial infrastructure, it is structured to help learners understand Kubernetes by following the actual process of building and operating it, rather than focusing on theory alone. Through a learning progression covering its relationship with Docker, YAML, Pods, Deployments, and Services, learners will internalize Kubernetes not as a “concept,” but as a “usable technology.”

Most developers still think in terms of Batch and CronJob when discussing data processing. However, in real-world service environments, data is generated continuously, and failing to process that flow immediately leads to delays, bottlenecks, and data consistency issues. I have also repeatedly faced the question, “Is batch processing really the right approach?” while dealing firsthand with real-time recommendations, state synchronization, and event delays in high-traffic environments. This course starts from that very question. It explores, from a practical perspective, how to use Apache Flink to perform computations as data flows in, manage state safely, and produce accurate results based on Event Time. Rather than simply explaining theory, it allows you to experience how real-time stream processing systems are designed and operated through actual source code and architectures. For those who have found real-time processing vague or wondered what lies beyond messaging, this course will provide a clear direction.

This course explores microservice architecture and distributed environments not as a simple list of technologies, but from the “design perspective” required in real-world practice and technical interviews. Starting with the differences between monolithic architectures and MSA, it systematically covers gradual migration strategies, modular monoliths, clean architecture, and hexagonal architecture, clearly explaining why each choice is necessary and the trade-offs that come with it. Through hands-on exercises, you can directly verify the intentions behind each structure and learn about the core challenges inevitably encountered in distributed environments—such as data consistency, transactions, inter-service communication, and failure handling—through realistic scenarios. Based on actual questions and perspectives from Kakao interviewers, this course aims to do more than impart knowledge: it seeks to elevate your way of thinking about distributed systems to the next level.

This course goes beyond teaching Docker as merely a set of tools and approaches it from the perspective of solving the concerns and problems you will actually encounter in the field. It explains, based on firsthand experience operating financial infrastructure in the industry, everything from unpredictable errors caused by differences between development and production environments, and configuration mistakes stemming from vague concepts of images and containers, to the anxiety of wondering, “How much do I need to know for this to be useful in practice?” Rather than simply listing commands, the course focuses on real-world examples to explain why this structure is necessary, how to design images for easier maintenance, and how to manage containers to operate services reliably. In particular, it places greater emphasis on hands-on practice than theory, with the goal of helping students understand Docker visually and master it through practice—not merely reaching the level of “knowing how to use Docker,” but understanding it from an operational perspective. This course provides practical standards and direction for those encountering Docker for the first time, those who have used it but have not yet organized their understanding of the concepts, and developers who want to take their infrastructure skills to the next level.

Learn how to design and process large-scale data architectures capable of handling hundreds of billions of records, using only pure SQL and MySQL’s core features, alongside a Toss developer who handles hundreds of billions of financial transactions and a developer who started without a computer science background and now works on platform development in Pangyo.

From the fundamentals of MySQL usage to the advanced techniques required in real-world service environments, you will learn progressively. The course also covers a wide range of topics, including data modeling techniques based on various cases and practical data, evaluating the effectiveness of Foreign Key design, analyzing the Google Calendar schema DDL, and modern DDL design principles. It provides an in-depth understanding of how to use MySQL as a core component of a system, rather than merely as a database.

If you’ve found it difficult to explain what’s happening under the hood while using AI and embeddings, this course may be the answer. Drawing on the experience of a practicing AI developer in Silicon Valley, it helps you understand the principles of AI through code by implementing everything from embeddings and Word2Vec to automatic differentiation and transformers yourself.

How can you withstand skyrocketing traffic? Learn how to build a resilient and flexibly scalable architecture through practical design patterns based on EDA (Event-Driven Architecture) using Kafka, Spring, CDC, and Temporal. Explained clearly and simply—even for non-majors—alongside the real-world experience of a Kakao developer. A hands-on course covering everything from real-time data flows and event processing to microservice integration!

When Redis alone can no longer guarantee performance, what options do we have?? There is Valkey, which is 100% compatible with Redis and applies IO-based tuning. Through this course, let’s explore how Valkey guarantees 1M++ TPS and learn about the data structures suited to different traffic patterns and feature implementations.

Systematically learn the distributed NoSQL database Cassandra, from basic concepts to practical application. Develop high-volume data processing capabilities through practice-oriented theory and hands-on exercises, including data modeling, query optimization, and cluster configuration. Even without prior database experience, you can grow into a Cassandra expert based on the know-how of a current Silicon Valley developer.

When you find yourself constantly updating Swagger documentation, aligning API specs, and repeatedly communicating with the frontend, you start to wonder, "Why am I doing the same thing twice?" I also felt similar frustrations in my professional experience, which led me to use GraphQL extensively and develop an interest in data-centric design. This course goes beyond simple GraphQL syntax; it explores the real-world problems it was created to solve, along with practical considerations like Resolver structures, the N+1 problem, and Federation. I've structured this course to help you naturally learn how to reduce communication costs between developers and design more flexible API structures.

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.

The biggest challenges when operating a batch system that processes millions of records are performance degradation and the difficulty of recovery when failures occur. Simple loops or poor design cannot solve these problems. In this course, based on experience handling large volumes of data reliably in real-world service environments, you will learn the architecture and operating principles of Spring Batch, along with design methods that achieve both performance and stability. Going beyond a simple explanation of its features, this course helps you develop fundamental problem-solving skills for data processing through practical batch architectures that can be applied directly in the workplace.

This course is for developers who want to find fundamental solutions in environments where response times slow as traffic increases, thread pools are quickly exhausted, and bottlenecks become more severe as the number of external API calls grows. Rather than simply scaling up servers, it helps you look at these problems from the perspective of changing the underlying architecture itself. Through this course, you will first understand the limitations of the traditional Spring MVC thread-per-request model. You will examine at the code level why throughput fails to increase even when CPU resources remain available, and how blocking I/O actually occupies system resources. Next, you will learn the concepts and standard specifications of Reactive Streams, as well as how to handle data streams using Reactor’s Mono and Flux. Rather than focusing solely on basic usage, you will gain hands-on experience with how data flows begin and are consumed, and how throughput is controlled. In particular, you will implement designs that use back pressure to prevent OOM conditions and overload. You will also learn how Spring WebFlux’s event-loop-based processing structure works and conduct comparative exercises to see how non-blocking I/O operates during actual request processing. By examining MVC and WebFlux side by side, you will identify their structural differences and establish criteria for deciding when to choose Reactive. This course goes beyond simply “learning asynchronous programming.” You will learn how to handle more requests with fewer threads, maintain stable flows in environments with heavy reliance on external APIs, and develop the design instincts needed to achieve high throughput in highly concurrent environments.