Vibe Coding for Immediate Use – $0 Operating Cost

Introducing a DevOps course based on hands-on case studies experienced directly on the front lines of Silicon Valley. This course goes beyond mere theory and covers CI/CD-related tools in a K8S environment that can be applied immediately in real-world engineering fields.

DevOps Interview Preparation: Master your interview preparation for Git, Docker, CI/CD, Terraform, Ansible, Kubernetes, and Monitoring.

DevOps Interview Preparation: Master your interview preparation for Cloud, CI/CD, Infrastructure as Code, Monitoring, Linux, Docker, and Kubernetes.

We introduce a DevOps lecture based on practical cases experienced directly on the front lines of Silicon Valley. This lecture does not just stop at theory, but covers CI/CD related tools in an EKS environment that can be applied directly in the actual engineering field.

In the era of swift execution and validation, execute faster than anyone with VIBE Coding! Achieve complete monetization, from ads to payments, all by yourself.

Using generative AI tools such as ChatGPT and Claude, you will turn ideas into actual web services and experience the entire development and operations process in a single project—from Docker containerization and server deployment to domain connection, HTTPS implementation, and CI/CD setup. Rather than simply generating code, you will develop practical skills by completing and maintaining a service that real users can access.

Building a Full-Stack System Without Coding: Complete the entire pipeline from data collection to AI/ML analysis and web dashboards using only Korean prompts (Vibe Coding). Owning a 100% Automated Pipeline: Secure an independent stock analysis tool that sends S&P 500 screening and investment opinions via Telegram every morning. Acquiring a Practical Data-Based Investment Framework: Master institutional-level data analysis and risk management capabilities based on source code currently in actual operation.

When you study machine learning, you learn how to improve accuracy. But the moment you try to deploy that model onto an actual chip, you are left with questions no one ever taught you to ask: How many multiplications does this model require? What does it need to remember, and how much? Can the target chip specifications handle these demands? Machine learning textbooks focus on accuracy, while semiconductor textbooks focus on circuits, yet resources bridging the two are scarce. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs—facing situations where the model with the highest accuracy simply wouldn't fit on the board. In this course, you will build six algorithms from scratch in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy for each. You will then translate these metrics into actual FPGA board resources. While knowing the number of multiplications alone doesn't give you a sense of scale, expressing it as a percentage of the board's resources makes it easy to assess. After completing nine labs and compiling the six models into a single table, an unexpected picture emerges. We spent nine sessions counting multiplications, only to find that multiplications weren't the actual bottleneck. From there, we move on to quantizing floating-point numbers to integers, managing bit widths, and even establishing criteria for grading circuits. An FPGA board is not required. The course can be completed using Python alone, with the board serving merely as a reference for translating the measured metrics.

This course is designed for those who use AI but are confused about how much they can trust it. It covers how to use AI in your work or creative process without overdoing it, ensuring it provides actual value. Rather than requiring professional development knowledge or complex tools, it focuses on how anyone can effectively integrate AI into their own workflow. Instead of teaching complex prompt engineering, the course focuses on the mindset of **knowing when to use AI and when it's better not to**, as well as **how to verify and refine AI outputs** rather than trusting them blindly. Through this course, you will establish criteria for using AI not as an automation tool that replaces everything, but as a **practical partner** that helps organize your thoughts and assists in your work.

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.

Learn deep learning through PyTorch. Ultimately, the course covers image classification.

After using it for 14 days starting immediately upon release, here are the reasons why I recommend Claude Code (AI Coding) to everyone—from job seekers to industry professionals—as something they can apply right away.

Non-Majors Welcome: Real-World Vibe Coding Projects Created Through Conversations with AI

This is a course where you'll learn the latest Next.js v15 App Router while building a practical developer blog based on Notion together. Additionally, you can gain various insights including how to utilize Cursor AI.

This course is designed to help you quickly learn server deployment methods using Nginx, Docker, and Spring Boot through hands-on practice. Learn Blue-Green deployment, a frequently used technique in the industry, faster than anyone else!

Complete a company introduction landing page with hands-on vibe coding. Using Cursor AI + Next.js + TypeScript + TailwindCSS + shadcn/ui + MCP stack, proceed from design to deployment all at once. The key is "fast implementation + SEO foundation that performs well in search engines." 🏁 Course Objectives Landing creation → Performance optimization → SEO implementation → Deployment/Indexing Real-time coding (vibe coding) to immediately check completed sections Direct experience including search indexing 🛠️ Practice Stack Cursor AI, Next.js(App Router), TypeScript TailwindCSS, shadcn/ui, MCP workflow 🔍 SEO Practice Apply SEO optimization MD files provided by instructor Metadata, OG/Twitter cards, robots/sitemap, structured data (JSON-LD) Optional multilingual routing implementation 🎯 Final Product Responsive company introduction landing + contact form Target Lighthouse 95+ Vercel deployment followed by Search Console registration and indexing request 📚 What You'll Learn from This Course How to quickly compose hero/trust/features/FAQ sections Clean UI composition with shadcn/ui Form validation and basic conversion tracking point design Boosting SEO scores with structured data + multilingual routing Understanding the indexing process through Search Console after deployment 💡 Industry Insights (Course Bonus) Beyond just code implementation, we'll briefly cover marketing and exposure methods actually used in the real market. Realistic pricing ranges for advertising, blog posting, and comment work How outsourcing and contracting companies actually proceed with SEO and exposure work Cost/efficiency differences between direct developer participation and outsourcing Through this section, you can develop a sense of how technology is actually utilized in real industry work and what cost structures drive it, beyond just the technical aspects. One-Line Summary Following the flow of "implementation → optimization → deployment → indexing," learn the web creation process that connects to actual search exposure and real industry market structures.

This is not just a lecture for memorizing commands. It is a practical introductory course designed to help you intuitively understand the "flow" of technology—why containers emerged and the principles behind how Docker creates and runs images. Even those new to Docker will be able to explain for themselves "why containers are a necessary technology" after taking this course.

This course is a practice-oriented program that goes beyond simple chatbots to build multi-agent systems where multiple agents collaborate. 👉 You will learn three frameworks—Swarm, LangGraph, and DeepAgents—step-by-step, completing six practical projects including a customer support chatbot, blog content creation, a PostgreSQL query agent, and an autonomous research agent. 👉 By implementing enterprise-grade features such as long-term memory using Redis and PostgreSQL, Human-in-the-loop security mechanisms, and cost monitoring, you will be able to build production-level systems that can be applied to real-world services immediately after the course.

Using ChatGPT is completely different from developing an actual generative AI service. This is because you must master everything from API calls to conversation state management, structured outputs, function calling, document-based RAG, MCP integration, and AI agent implementation. Based on extensive experience in data analysis and AI instruction, this course explains complex concepts through Python-centered practice. You will learn step-by-step—from the principles of generative AI to the OpenAI Responses API, multimodal features, File Search-based RAG, external tool integration, and the Agents SDK—to complete a fully functional AI application.

In an era where AI writes code for you, will you remain a 'Coder' or leap forward to become an 'Architect'? This course is designed for junior developers who can handle basic implementation but feel stuck about the next steps or are anxious about falling into the trap of 'fake experience'. We aim to go beyond simply learning clean architecture theory and turn Robert C. Martin's (Uncle Bob) high-level design principles into weapons for the AI era.

This is a practical project course where you will build a complete web service from planning to deployment using AI coding tools. You will gain hands-on experience with the entire process—including database design, file storage implementation, and service operation—within a Windows environment to acquire practical development skills for the AI era.

We will summarize the theoretical concepts of MNIST, the most fundamental dataset in computer vision, and learn how to implement it using Torchvision's API as well as how to implement a custom dataset class directly.

In an era where AI writes code for us, the value of a "true architect" who can see through and control the macroscopic framework of a system becomes overwhelmingly higher. This course digs into the essence of low-level networks and TCP/UDP sockets hidden behind frameworks, cultivating the ability to design robust core engines that never crash even in the face of millions of traffic hits and large-scale data. Go beyond being a coder who blindly repeats copy-pasting, and leap forward to become an irreplaceable master who directs and controls AI with clear, system-level solutions.

From coding basics to launching your own app all at once?! The App Build All-in-One, connecting fundamentals and practical application.