Building Claude Code with Kotlin from Scratch

"Why did the AI write this code?" or "Why can't it remember what I just said?" The frustration you encounter when using AI tools like Claude Code stems from not knowing how the tool actually works. This course flings open that black box. Over 25 lessons, we will build our own Coding Agent from scratch that functions exactly like real-world professional tools—giving the LLM 'eyes' to see files (Read), 'hands' to fix code (Edit), and 'feet' to execute commands (Bash). This isn't just about learning how to use a library. You will gain hands-on experience by implementing code to understand how AI perceives project structures, why it loses its memory during long conversations, and how to solve those issues technically (Sliding Window, Summary). Through this process, you will evolve from being a mere 'user' of AI tools to an 'AI provider' who understands the underlying principles and can customize and evaluate tools to fit your specific needs.

This course covers the entire process of designing and implementing generative AI services centered on LangChain 1.0 and LangGraph through step-by-step hands-on practice. Beyond simple LLM calls, you'll directly implement operational AI system architectures that include agent-based architecture, state management, memory, streaming, middleware, and Human-in-the-Loop. Through hands-on practice with document/PDF/web data-based RAG systems, SQL Agent (Chinook DB), tool-calling-based Agents, Supervisor pattern multi-agents, and state machine-based workflows using LangGraph Graph API, you'll build reusable agent pipelines that can be immediately applied in real-world scenarios. Additionally, through structured output (Pydantic-based), agent middleware (Summarization, HITL, Retry, PII protection), and token/step-by-step streaming, you'll complete generative AI applications with the stability, scalability, and controllability required in actual services. 👉 For those who want to accurately understand the internal structure and execution flow of LangChain/LangGraph 👉 For those who want to implement RAG·Agent as a real service structure, not just a "demo" 👉 This is the optimal course for those who need a realistic practical roadmap covering state-based agents, SQL·document automation, and multi-agent orchestration.

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

Kotlin Programming Language, which you can learn by practicing with 76 examples. You have to develop in Kotlin starting tomorrow, but you don't have time to study the grammar? Let's quickly learn all the grammar of Kotlin with the examples provided in this lecture.

"Turn your Java code into the hands and feet of AI! Practical Agent Development with Kakao PlayMCP and Spring AI" Traditional LLMs are limited to answering based only on their trained data, which can lead to issues with recency and accuracy. In this course, you will learn **MCP (Model Context Protocol)**—the next-generation standard protocol for connecting AI with external tools. Using Spring AI, you will build a server that communicates with various platforms like Kakao PlayMCP through a single implementation. Master the entire process from theory to real-world deployment and platform registration through the 'Proverbs & Psalms Counseling Center (JAMSI)' project, and grow into a true AI agent developer who can empower AI to handle tools independently.

This course is designed to teach the entire process of 'AI Training Data Construction,' which is essential for successfully executing AI modeling projects. Learners will not simply remain at the theoretical knowledge level, but will experience step-by-step the data construction processes required in actual practice. Specifically, the course covers the entire process from AI training data planning → acquisition → storage → preprocessing → labeling → pseudonymization and transformation through hands-on practice, structured to develop practical skills that can be immediately applied in the field. Additionally, learners will systematically study the most important data management capabilities in AI projects, such as data quality management and security issues, personal information de-identification processing, and dataset optimization. Through this, learners can grow from simple data collectors into data specialists who can proactively plan and execute AI projects. 👉 Through this course, learners will understand how to construct high-quality training data that determines AI model performance and acquire practical capabilities to effectively apply this in real work environments.

It’s okay if you don’t know how to code. This course focuses on the experience of using AI to build an ad-revenue-generating Android app from scratch and successfully launching it in just one day. Many people stop at these stages: “I built the app, but how do I monetize it?” “I tried adding ads but got stuck on AdMob, policies, and reviews.” “I gave up right before launch because of the privacy policy.” In this course, we will navigate through every "drop-off point": 👉 Idea → App Creation → Ad Integration → Store Launch Essentials → Actual Deployment. We let the AI do the work, and we make the decisions. The goal is to complete an app with a "functioning revenue structure" within a single day.

Learn Springboot + Flutter RestAPI Board Creation Faster Than Anyone Develop a CRUD server with Springboot and create your own board with Flutter!!

Building your own emotional diary service with Spring AI + React It's time for our 4th project with Neo + Essential!

Through this lecture, you can learn about the characteristics and background of the Kotlin language, grammar and operation principles, usage examples, and things to be careful of when using Java and Kotlin together.

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.

Spring CRUD Starting with the Price of Chicken Start server development with the price of chicken!

"Beyond Data Analysis: From Predictive Model Design to Optimization (29 Lectures in Total)" Have you finished your data analysis but feel stuck when it actually comes to building a model? We help you fully master not just how to call libraries, but the working principles of each algorithm and optimal model validation strategies.

This course will give you a quick overview of the combination of Kotlin and Spring by implementing simple functions.

Learn Springboot + iOS RestAPI Board Creation Faster Than Anyone Develop a CRUD server with Springboot and create your own board with iOS!!

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!

You can try using jOOQ, a data access technology, and experience Spring development with Kotlin.

Learn Springboot + ReactNative RestAPI Board Creation Faster Than Anyone Develop a CRUD server with Springboot and create your own board with ReactNative!!

Learn Springboot + NextJS RestAPI Board Creation Faster Than Anyone! Develop a CRUD server with Springboot and create your own board with NextJS!

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!

This is a hands-on tutorial on how to integrate with IntelliJ and develop quickly using the Model Context Protocol (MCP).