Introduction to Building a Smart Farm Platform - IoT Smart Farm Starting with Arduino and AI Tutor

A FastAPI course for modern full-stack and backend development that is easy to use and delivers high performance. It covers backend concepts step by step, the core syntax of FastAPI, and asynchronous database usage, all organized clearly through well-structured materials and projects.

Build and deploy community applications using the MERN Stack (MongoDB, Express, React, Node + Firebase)

Based on the experience of wondering how facial recognition works in offline environments on-site, this course will guide you through building the basic structure of a facial recognition-based access and attendance management system using React + Electron and Python's FastAPI-based DeepFace. Rather than focusing on perfection, we focus on understanding the operating principles and laying the foundation for you to expand into your own personal projects.

This is a process for completing IoT products without complex coding, using the Vibe Coding methodology powered by AI agents. You can experience the entire product development cycle in a condensed format—from embedded hardware control and AI vision recognition to PCB design, 3D printing, and web/app service development—allowing you to launch functional, commercial-grade IoT devices.

In this course, we will practice step-by-step how to use React and FastAPI to implement social features such as real-time voting, comments, and likes, as well as how to monetize by integrating payment systems (Kakao Pay, Toss Pay, etc.).

We will completely resolve the anxiety of those who felt frustrated by blindly copying AI-generated code without understanding the internal workings of HTTP or the causes of unexpected hanging.

In this hyper-connected era, you can easily understand IoT concepts, business characteristics, key technologies, and operating principles that are essential for employment and practice in the IT field. You can learn how to utilize IoT using Raspberry Pi.

A portfolio-focused course on deploying a React frontend and Spring Boot backend directly to the AWS cloud! This course is helpful for those who want to quickly deploy frontend and backend projects for their portfolio. You will gain the necessary knowledge for deployment through short-term, step-by-step practice, covering everything from server configuration to Docker, domain connection, Nginx, and HTTPS application!

This is a practical course where you can learn the process of building a company website yourself using React, Node.js, and MongoDB. Let's build a full-stack project together with a basic background in React and Node.js!

We solve the fundamental problem of losing control amidst AI-generated fragmented code and the complex React ecosystem by opening the hood and engineeringly analyzing the internal workings of core React libraries such as Zustand, TanStack Query, React Hook Form, Zod, and the React Compiler. By moving beyond simple tool usage and mastering how to directly manipulate the performance and data flow of the React engine, you will build the solid 'enterprise architecture design muscle' necessary to respond immediately to any crisis.

Real-world practice is harsh. I will perfectly pass down my essence of optimization—designed to maintain a smooth user experience even in extreme environments where tens of thousands of real-time data points pour in—through a meticulous mission system. Go beyond simply being someone who writes code and leap forward into an irreplaceable senior engineer who orchestrates performance through the principles of the engine.

We will create an ad-monetized website using React. You will learn how to optimize advertisements, reduce server costs, and gain site operation tips according to the scale of your project.

You can learn the basics of React. You can organize HTML, JavaScript, DOM, Event, Object, Module, OOP, etc. related to React.

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.

Are you missing anomalies in complex manufacturing site data? I am sharing my know-how on how to accurately detect them using autoencoders with real-world IoT data.

We invite you to become a ‘Top-Tier Enterprise AI Systems Architect’ Do not remain a micro-level technician (Prompt Writer) who merely assembles libraries created by others or polishes prompt sentences. From mathematical first principles to runtime control-loop kernels and autonomous recovery infrastructure — through 40 years of practical engineering insight that has driven industrial operations in South Korea, I will firmly guide you to become a top-level ‘Enterprise AI Systems Architect’ who protects your company’s business and digital sovereignty. Now, begin the great journey of transforming uncontrollable AI into a perfect industrial core. “Why does an AI that ran perfectly in a demo start spouting strange nonsense or bring the pipeline to a halt as soon as it is deployed to production?” “Why do AI projects that so many companies launch with great enthusiasm ultimately fail to escape the ‘PoC (Proof of Concept) swamp’ and get abandoned?” These are the most desperate questions that countless developers and systems architects I have met in the field have asked me. Hello. I am Hongjip Kim, who spent more than 40 years in the powertrain R&D division of the South Korean automotive industry (including 17 years as an executive in Hyundai Motor Group R&D), led the export of engine and drivetrain technologies worth 130 billion won, and commanded operations as the CEO/CTO of a listed mid-sized manufacturing company. From the perspective of an engineer who has spent decades in mechanical engineering and precision-control environments, a ‘risk of fatal uncertainty’ lurks behind today’s frenzy to adopt generative AI. In mission-critical industrial environments where even a mere 0.1% error or mistake can directly result in the destruction of physical equipment worth hundreds of millions of won and an enterprise-wide shutdown, integrating a probabilistic chatbot that simply relies on probability to generate plausible text directly into a service is like carrying a ticking time bomb into a fire. This course was designed to impart the “practical engineering methodology for training uncontrollable probabilistic AI into the most robust industrial control core, one that allows not even 0.000% of syntax errors.” 1. The 3 Major Fatal Bottlenecks You Face in the Field (Problem) Many engineers attempt to challenge production environments using an approach limited to one-off prompt tuning (Vibe Prompting), only to become frustrated when they encounter the following 3 structural defects: Finite Context and ‘Lost in the Middle’: As conversations or multistep executions grow longer, critical system instructions are pushed aside and the context window is exhausted, causing the system to lose its memory and amplifying hallucinations. Nondeterministic Outputs and Cascading Pipeline Collapse: Tiny probabilistic fluctuations, such as one missing closing bracket in JSON or an incorrect type, can paralyze legacy ERP/CRM API calls and destroy data integrity. Untrusted Code Execution and Infrastructure Security Bankruptcy: When scripts autonomously generated by an agent run in a non-isolated host OS environment, they create risks such as kernel escapes and sensitive-data leakage. As such, AI malfunctions in industrial environments are not merely quantitative accuracy problems; they become a massive financial disaster in the form of asymmetric business loss (Asymmetric Business Loss). 2. ‘How’ to Solve These Problems Perfectly (Solution) This course differs fundamentally from commercial courses that teach only how to use fragmented off-the-shelf tools. By combining 40 years of hands-on field expertise with computer systems engineering, it delivers 3 hardcore architectures for vertically integrated, system-level control of probabilistic AI: ① Move Beyond Prompting to ‘LLM-OS & Context Orchestration’ Following Andrej Karpathy’s paradigm, redefine the LLM not as a conversational respondent but as the central computational core (CPU) of a computer. Systematically organize the context window as ultra-low-latency volatile RAM, the internal vector DB as Disk, and external APIs as an I/O bus. Through MemGPT hierarchical virtual-memory paging, preserve critical state without saturation even during infinite conversations, while scaling a dynamic agent network through MCP-based Liquid Swarm MAS, minimizing token costs. ② ‘Deterministic Harness Engineering’ That Constrains Probabilistic Hallucinations to 0.000% Leave the language model’s creative flexibility open only during the Reasoning phase, while firmly confining the physical output layer that interacts with external systems using mathematical Boolean algebra and compilers. Apply EBNF / XGrammar-based CUDA logit masking to forcibly block the generation probability of invalid tokens at P = 0.000%. Form a physical security isolation ring so that code generated by agents runs only once inside a Firecracker MicroVM micro-virtualization sandbox with 5ms snapshot restoration capability. ③ ‘High-Reliability MLOps·LLMOps Autonomous Recovery Infrastructure’ Without Wasting GPU Resources Build vLLM PagedAttention and S-LoRA hot-swapping to suppress idle VRAM waste to below 4%. When a multi-agent API fails, restore data consistency 100% through SagaLLM reverse compensation transactions. Through an OpenTelemetry/Langfuse distributed observability network, defend system availability by executing an unmanned automatic canary rollback whenever a decline in the RAGAS faithfulness score (Data Drift) is detected. 3. Practical Value That Drives Spiral Growth (Pedagogy & ROI) This course is systematically designed according to the 5 levels of Bloom's Taxonomy. Starting from simple theoretical recall (Remember), learners directly code and validate a 5-stage debugging pipeline (analyzing deceptive baseline trap source code ➔ deconstructing adversarial engineering ➔ establishing a Robust integrity pipeline ➔ calculating the TEI ROI formula ➔ SOTIF/ISO 42001 governance). Going beyond the intuitive satisfaction of simply “working well,” I will apply the Forrester TEI (Total Economic Impact) financial model to visualize, from an executive perspective, how much the AI system you build reduces your company’s rework cost (Rework Cost), avoids failure risks worth hundreds of millions of won, and proves an ROI of more than 200%.

It is difficult to complete a classification service using only AI API calls. Based on practical experience, I guide you through everything from implementation to deployment.

"Next.js Master Class: Part 3 - Completing Real-World Architecture" goes beyond tutorial-level projects that only run locally; it is a guide to designing "real production" architectures capable of handling massive traffic. It covers all the core technologies used in the industry, from flawless state synchronization combining TanStack Query and Zustand, to Turbopack-based bundle optimization under 100KB, advanced SEO injection, and building ironclad full-stack security networks using proxies and JWT sessions. I strongly recommend this course to those who want to overcome tedious hydration errors and CORS barriers on their own, moving beyond being a simple coder to becoming a "Senior Architect" who commands the entire frontend system.

★PYTHON★ This is a basic lecture on server development using Fast API. Learn how to develop servers easily, quickly, and conveniently using various packages such as Fast API, SQLAlchemy, Pydantic, and Starlette! Don't worry, we have prepared a DB backup file as well as integration with PostgreSQL! We will teach you the "basics" of UI design using Dash and Plotly and provide the source code used in the lecture :)

This introductory course for AI engineers is designed to provide a brief hands-on experience of the entire process, from data processing and model development to cloud, MLOps, and ethical considerations. It focuses on building practical skills by helping students understand the process of connecting models to actual services, rather than just creating them. The course includes hands-on exercises and examples so that even those learning AI for the first time can easily follow along.