[Practical AIoT] Perfect Preparation for Smart Mirror Makerthon: LLM, CV, and Hardware Design

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.

As you study CPUs and RISC-V, you may understand what the instructions do but still find yourself asking, “How are these instructions actually executed in hardware?” and “How can I verify that a CPU I designed myself really works correctly?” Many existing resources stop at ISA explanations or simple CPU examples, and many others rely on completed IP, making it difficult to experience the entire process of designing and verifying a CPU yourself and running programs on an actual FPGA. I have spent many years working in R&D and business in the system semiconductor field, and I have observed this learning gap firsthand while teaching semiconductor design and FPGA at a university. For this reason, rather than providing a completed CPU and simply checking that it operates, this course is structured around designing the CPU directly in a White Box approach, starting with the question, “What hardware is required to execute RV32I instructions?” In Volume 3, we expand Volume 2’s TinyRV32I into a Full-Version 32-bit CPU that uses 40 RV32I instructions. We extend the datapath, Decoder, Branch·Jump, and Byte/Halfword Memory Access, and implement Forwarding and Load-Use Interlock so that the 5-stage Pipeline can handle data dependencies on its own. We then integrate CSR, Exception, Interrupt, Trap, and MRET to complete a CPU capable of handling not only normal program execution but also exceptions and external events. The design does not end with RTL Simulation. You will run actual programs on the CPU, compare the results with the Spike Reference Model, and learn the purpose of the RISC-V Architecture Compatibility Test as well as how to perform and evaluate it through real verification cases. Finally, you will implement the verified CPU on an Arty S7-25 FPGA, receive input from Switches and Buttons, run programs, and confirm the results by displaying them on LEDs. The problem this course aims to solve is not simply learning more RISC-V instructions. Its goal is to connect instructions → CPU architecture → Verilog RTL → Pipeline control → program execution → standards-based verification → actual FPGA operation into a single flow, enabling students to fundamentally understand the internal structure of a CPU and develop the engineering skills to design and verify one themselves.

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.

Opening a new window, copying, and pasting into a messenger every single time — let today be the last! OpenClaw Practical Course: Your own AI assistant that works directly for you.

You can now watch the online version of the offline meetup where Jae-kyu Lee—developer of the open-source Agent OS 'Ouroboros,' which quadrupled its GitHub stars in just three months—shares design principles gained from directly integrating and dismantling eight coding agent runtimes!

Easy, Usable Computer Vision Projects: Step-by-step learning of practical CV projects. Boost skill with real examples.

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.

It goes without saying that AI artificial intelligence is the trend. But what can you use machine learning and deep learning for? Now, let's learn useful Internet of Things IoT Computer Vision projects that are used in daily life.

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

Complete Understanding of Robotics High- and Low-Level Controllers, Sensors, and Ultra-High-Speed Communication Concepts Through the Unitree Go2 Example

Do you still code manually these days? We are now in an era where even "vibe coding" feels like a chore! Become an AI automation expert with n8n, no coding required. A guide to designing automation workflows that can be applied immediately to real-world tasks. Practical automation strategies to maximize work efficiency. Build intuitive workflows with n8n and experience a revolution in the way you work.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

Do the results of vibe coding keep turning out differently from what you intended? Learn a workflow that uses specification-driven development (SDD) and Spec Kit to define goals, scope, business rules, and completion criteria, then shares the same specifications with Claude Code and Codex to reduce repetitive revisions.

AI proficiency determines your salary. Leave the tedious, repetitive tasks you do every day to AI. 'Almost Everything About Work Automation' teaches you how to create an automation system that handles your work on its own with just a few clicks, even without any coding knowledge. You will be freed from boring paperwork and data organization, and transform into an 'AI-savvy professional' who stays ahead of others.

Learn how to build a real-time fire detection system using Next.js, YOLOv11, and FastAPI.

[Sustainable AX and How to Collaborate with AI Colleagues] Have you encountered these limitations when trying to apply AI tools to your work? 🙋‍♂️ : "Entering the commands from scratch every time is actually more of a hassle." 👨‍💻 : "I’m tired of one-off tasks that simply involve repeating copy-and-paste." 🤷‍♂️ : "Since I don’t have a background in development, I can’t even imagine automating more advanced tasks." What we need now is not simply 'how to use tools'—learning the features. It is a mindset of cultivating your own AI colleague—one that understands the context of your work and grows smarter on its own over time. We transparently share a practical, field-oriented AX (AI transformation) guideline developed by a non-developer planner through countless trials and errors in the workplace.

This course covers the fundamentals of the Notion API and teaches you how to automate Notion by integrating AI (Claude) using Notion MCP. You'll learn to directly control blocks, pages, and databases by following the official API documentation, and ultimately complete a hands-on project that creates databases and automatically adds pages by analyzing text file content using only natural language commands (prompts).

This is the edited live video from the LLM Application Experience Sharing Session with knowledge sharer Kang Byung-jin :)

In an era of explosive data growth and crucial real-time processing, edge computing has established itself as an essential technology, no longer an option. This course is designed to systematically teach everything, from the core concepts of edge computing to deployment strategies for its application in real business environments. Through this course, students will firmly solidify the fundamental principles of edge computing and acquire practical knowledge and know-how applicable to real-world tasks and projects. Take the most certain first step towards becoming an edge computing expert, a core technology in the future IT environment!

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

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