[Chuseok Special] Building Embeddings and a Mini GPT from Scratch with a Silicon Valley Developer

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.

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 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.

Build a Kafka cluster with Spring Boot 3.x + Kotlin, and develop a scalable order processing system capable of real-time event streaming using PostgreSQL CDC, Debezium Connect, and Apache Avro schemas.

Learn the process of building essential observability in a microservices architecture (MSA) environment, from the basics to hands-on practice. Through step-by-step exercises, you will learn standardized data collection using OpenTelemetry, trace storage and search with Grafana Tempo, and visualization using TraceQL. You will also learn how to trace service flows through distributed tracing, identify bottlenecks, and analyze the causes of failures.

This course is a master class that systematically covers the fundamental principles and practical applications of MongoDB. Starting with core MongoDB concepts, you will systematically learn essential topics for practical work, such as database design, CRUD operations, indexing, aggregation pipelines, performance optimization, transactions, and security. It explains topics step-by-step so even beginners can understand, and you can deeply learn the working principles of MongoDB through exercises and examples. This is the best introductory and practical course for developers who want to build strong MongoDB skills for practical work.

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.

This course was created to solve a common problem many people face while preparing for developer jobs: "I studied CS and data structures, but I get tongue-tied during interviews." While conducting actual interviews at companies like Naver, Kakao, and Toss, I noticed that most candidates have memorized concepts but cannot explain why those concepts are necessary or how they are applied in real-world situations. Rather than simple rote memorization, this course teaches you how to understand the interviewer's intent and provide more persuasive answers to the same questions. Whether it's the OSI 7 layers, data structures, or coding tests, we focus on why you need to know them, when they are used, and how to speak about them to receive a positive evaluation in an interview. This course is for those who want to become developers who can explain what they understand in their own words, rather than just giving the same answers as everyone else.

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.

Learn how to build data pipelines with Apache Airflow, from the basics to real-world applications. Understand Airflow’s core concepts and architecture, and practice advanced design patterns frequently used in the field, including dynamic DAGs, parallel processing, distributed processing, and Custom Operators. Set up a hands-on environment with Python and Docker, design and operate real-world workflows, and develop practical skills for production use.

A Redis course created with a current Kakao interviewer. From hands-on practice with data structures ranging from String to Sorted Set to Sentinel and cluster configuration, you’ll build design rationale that holds up in interviews and real-world work.

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.

This course covers RPC communication techniques for maximizing performance in large-scale MSA (Microservice Architecture) environments. Going beyond simple gRPC practice, you will learn how to implement stable and efficient service-to-service communication in real-world production environments where hundreds of microservices operate simultaneously. Based on Golang, the course takes a practical, hands-on approach to writing Protocol Buffers (proto) syntax, generating code automatically and implementing services, understanding gRPC’s architecture and advantages over traditional RPC, and applying performance optimization strategies. To ensure that even non-majors and beginners in server development can easily follow along, it explains everything step by step—from the concept of RPC to gRPC’s internal operating principles—helping you develop practical skills that can be applied directly to real-world services.

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.

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.

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.

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.

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.

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.”