LangChain Fundamentals in One Hour

LangGraph, packed with a major corporation's AI Agent lead's know-how. We deliver knowledge gained from real-world challenges.

RAG. Learn from Silicon Valley GenAI Hackathon Winner. Packed with real-world know-how.

Artificial Intelligence, AI, agents... They might seem grand, but once you try them, they're not as difficult as you think. That's why it's important to implement simple features yourself. Through practical projects that can actually be used at a company, you'll directly experience various use cases and learn how to utilize and apply AI agents.

How to Use Storybooks A to Z. We show you everything about storybooks.

New to Flutter? A Flutter Contributor will guide you step-by-step! Start Flutter dev with an AI project using Gemini.

This is for those who want to deploy/operate services with AWS. From domain settings to Docker and serverless!

Test code! For those who are at a loss as to where to start, here it is. From writing tests to deploying through automation, all in one place!

We introduce basic prompt patterns for coding and advanced API prompt patterns for leveraging artificial intelligence.

From server setup to blog and YouTube automation, build a complete AI workflow with n8n. We will practice creating a stable operating environment, covering Contabo VPS-based server installation, Ubuntu/Docker/Portainer environment setup, domain connection, and HTTPS security configuration. The course covers Search API and file server development using FastAPI and Docker, Airtable and n8n integration, building blog AI agents, and image/video automation. You will be able to complete a profit-generating automation strategy that can be immediately applied to actual service operations.

This course on RAG (Retrieval-Augmented Generation) 👉 doesn't stop at conceptual explanations 👉 but involves building actual working structures hands-on 👉 and experiencing expansion and advancement through practice-focused learning. Starting from simple RAG examples, you'll progress step-by-step from Advanced RAG → Modular RAG → Agent-based RAG to a level that can be immediately applied in real-world work environments.

Simply by entering a US company ticker, this system automatically collects financial accounting data, competitor analysis, recent news, and stock price information, then performs in-depth analysis using the OpenAI GPT API to generate professional investment reports. The final results are automatically uploaded to Notion. By learning fundamental financial accounting concepts and terminology, even engineers can utilize this for competitor benchmarking, persuading supervisors, and planning new solutions. Build high-performance API servers with Python and FastAPI while learning asynchronous programming, AI integration, and practical fintech skills. Even beginners can easily follow along to complete a system applicable to real investment and business scenarios, helping them grow into developers suited for the AI era.

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.

We invite you to the fascinating world of coding where you analyze and visualize financial statements using only prompts. Without a single line of code, you create and deploy a service in consultation with AI.

Throw away those simple services you can make with vibe coding in just 10 minutes! You can learn 'real' practical vibe coding. Let's create advanced services at the level of actual services according to planning and design with Cursor AI NCOP. No Code, Only Prompt

Develop various features using Spring WebFlux. This course will teach you WebFlux's essential concepts and practical implementation.

This course goes beyond simple trends, covering how to systematically learn to collaborate with AI tools, verify and improve output quality, and build and deploy applications from start to finish. Anyone can take this course, from developers interested in AI to team leaders looking to boost team productivity.

LLM: A pro quickly delivers core content, from essentials to practical tips!

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

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

No coding knowledge required. Build it instantly in your browser with Google's latest tool (IDX)—no installation needed! Stop studying coding syntax! This is a high-speed practical course where you'll build an RAG chatbot that perfectly understands internal documents through AI conversation (Vibe Coding) in the Google IDX environment and deploy it live to the web.

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!

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.

With v0, cursor AI, and various LLMs, from PRD to AI product recommendations! Complete your Next.js shopping mall through a practical project.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is the technology that has come closest to AGI at this point in time. In this lecture, we explore LLM and Agent technologies from a general public perspective.

Learn the easiest way to handle AI with Spring AI. Let's learn Spring AI by building a RAG chatbot.