Creating a Chat GPT based on Next.js App router

You can learn React, an essential course for aspiring frontend developers, and even build a portfolio project.

Clone YoutubeMusic website with React, Next.JS 14, TailwindCss, RadixUI, Shadcn, and zustand. You can master the basics of web front-end with a focus on UI, starting from NextJS basics.!

Does changing it on the screen mean it’s been saved? From server responses and data persistence to login and permissions, AI responses and performance, and validation and deployment—this is Part 2 on what else you need to decide and verify before releasing an AI-generated interface to real users.

A full course on Next.js you can sink your teeth into! Complete a real-world project using the latest technologies such as GraphQL, Redis, RabbitMQ, Prisma, TailwindCSS, Docker Compose, Cypress, Prometheus, and Grafana, and experience both the App Router and Page Router.

"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 teaches you the latest Next.js v15 App Router while building a practical, Notion-based developer blog together. You’ll also gain various insights, including how to use Cursor AI.

Completing an integration project between an API server built with Spring Boot (ver 3.4.x) and React 19 (with TypeScript)! Applying front-end to a back-end portfolio! A course written for working professionals (not a beginner-level course).

Have you ever thought about building a 'real system' beyond simply rendering screens? The real problem many developers commonly face is something else entirely. It's the limitation of a 'fake full-stack'—where things look plausible on the surface, but data synchronization frequently tangles, speed is frustrating, and you lose track of where to even start when just a few features are added. This isn't a problem that can be solved by simply memorizing one more Next.js feature. This course will resolve that frustration from the roots. Encyclopedia-style classes that just list features are boring, aren't they? Instead, we have tightly integrated the fundamental principles of Computer Science (CS) with the advanced architecture of Next.js. We will directly transplant into your fingertips the enterprise-level design skills required to flexibly withstand millions of visitors and maintain perfect data consistency.

In the AI era, web developers need more than coding skills—they need the ability to use AI to turn ideas into real-world results. Build your AI web development skills for practical work and portfolios by gaining hands-on experience creating UIs with Stitch and Figma Make, implementing them in React, and deploying them.

This is a practical project course on running Whisper locally and quickly developing an AI speech recognition app using FastAPI and Next.js. You'll implement real-time speech recognition and complete a project that can be used as a simple portfolio piece in a short amount of time.

This is mission content for a development project. We provide a PRD planning document, Figma designs, and API endpoints so that you can practice by implementing a timer app called DevTime yourself. Make the knowledge you have learned completely your own by implementing a project at a level similar to an actual service 🚀

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

A Complete End-to-End Developer Journey — Build Lightning-Fast APIs, Modern Frontend Interfaces, Real Projects, Authentication Systems, Dashboards & Full Deployments Using FastAPI, React, and Vibe Coding Techniques.

We have entered an era where it has become incredibly easy for web developers (especially React developers) to launch apps. With Expo, you can release both Android and iOS apps even faster. Let's become developers who can quickly launch apps through Expo.

A 30-minute free introductory course that starts with just the OpenAI API. Quickly learn the essentials—from the Responses API and JSON response handling to validation, idempotency, and logs—and learn a practical backend workflow that you can test right away without external service keys.

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.

In this course, you will learn about TypeScript.

This is a course to learn the concepts and usage methods of the LangChain library, and to build your own ChatGPT using the LangChain library.

Go beyond a simple system prompt to build an AI character chatbot that remembers users and whose relationship with them evolves, using persona design and Structured Outputs. In this 30-minute hands-on session, you’ll learn how to safely call the OpenAI API based on the provided starter project, process emotions, trust levels, and memories in a verifiable format using JSON Schema and Zod, and use an AI coding agent within defined constraints.

Learn the core principles and practical implementation of Classic RAG, Graph RAG, and Agentic RAG. Design a dynamic routing system that optimizes token efficiency and latency, and build a long-term memory system that combines GraphRAG’s relational network reasoning with Agentic RAG’s self-evaluation loop. This course develops advanced RAG architecture design skills that can be applied directly in real-world settings.