"Are you tired of repetitive daily tasks and expensive AI tool subscription fees?" This course teaches you how to create an 'Automation AI Agent' that will completely transform your work through intuitive 'Node' connections, even if you know nothing about coding. To solve the 'high cost' issue of existing tools like OpenClo, you will learn smart automation design methods where the computer quickly handles simple calculations and calls the AI only when absolutely necessary. From YouTube notifications and receipt bookkeeping to RAG document searching and automated email replies, build your own 24-hour customized AI assistant at almost zero cost through 9 practical examples!

A comprehensive AI video production course that even those without a related background can easily follow. You will build your creative production skills by practicing the entire process—from image generation and video creation to editing—using both free and paid AI tools. Experience every stage of AI-powered content creation through hands-on projects.

You have likely seen many misleading lectures about monetizing AI music. Yes, AI music monetization is possible. It is simply not difficult enough to justify paying expensive tuition, nor is it a way to make easy money. This is an action-oriented course for those who are interested in AI music monetization but feel lost on where to start. Based on my personal experience of creating AI music, organizing albums, distributing tracks, and utilizing YouTube and TikTok, I have structured the workflow—from music generation and album planning to file preparation, distribution, and monetization setup—so you can follow along from the very beginning. This course does not guarantee profits; rather, it is designed to help you experience the entire process of releasing AI music and preparing for monetization from start to finish.

You will systematically learn the principles and practical applications of the latest generative AI technologies, from ChatGPT to image and video generation AI and no-code tools. Through seven core topics, including prompt engineering, Copilot utilization, and AI extensions, you can maximize work efficiency and develop creative problem-solving skills.

[Understanding AI Foundation Models and Their Operating Principles: Engineering Control and System Architecture, Practical Methodologies for Resolving AI Uncertainty and Engineering Assetization] 1. Introduction: The Necessity of Engineering Control of Intelligence (Engineering Control vs. Systemic Chaos) A core conclusion derived from long-term practical insights in industrial fields is that power that is not properly controlled acts as a potential liability rather than an asset. Even a high-performance engine is nothing more than an unstable physical mass if it lacks sophisticated combustion logic and microsecond-level control systems. The organizational chaos currently appearing in the process of adopting Generative AI is judged to stem from a lack of understanding of these control principles and a blind faith in technical "black boxes." This masterclass redefines Artificial Intelligence not as a mysterious stochastic phenomenon, but from the perspective of Model-Based Engineering (MBE). By transforming the uncertain domain of intelligence into a predictable and reliable engineering framework, we aim to present a strategic methodology that allows organizations to secure strong leadership across the entire system without being dependent on technical trends. 2. The 4 Pillars for Solving Core Challenges ① Epistemological Paradigm Shift: Visualizing the Black Box and Assetizing Technical Debt Many companies are facing "technical debt"—characterized by exposure to security vulnerabilities and exponential increases in maintenance costs—by adopting AI models without a clear understanding of their internal structures. This course assetizes this through the following approaches: Deconstruction of Mechanisms: We engineeringly deconstruct the Self-Attention mechanism, the core of the Transformer architecture, from the perspective of numerical weight analysis. By understanding the numerical mechanisms that determine information priority, we visualize the basis for the model's judgment. Analysis of ID Formation: We transparently track the process by which a series of pipelines—leading from Pre-training to Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF)—forms the model's technical identity and ethical guidelines. This converts invisible threats into controllable system parameters. ② Securing Deterministic Reliability: Hallucination Control Strategies to Overcome Probabilistic Limits Large Language Models (LLMs) are not systems that reason for truth, but systems that generate the next most probable token. The "hallucination" phenomenon resulting from this inherent characteristic becomes a fatal flaw in engineering fields where reliability is vital. Constraints of Retrieval-Augmented Generation (RAG): We move away from closed structures that rely solely on the model's internal fixed memory (Internal Weights). We establish an "open-book strategy" that provides clear grounding for generated results by allowing real-time reference to trusted external knowledge bases. Hybrid Model Architecture: We design a redundancy strategy that achieves both accuracy and operational efficiency by deploying large models for areas requiring enterprise-wide knowledge and optimized Small Language Models (SLMs) for specific domains where security and real-time response are essential. ③ Computing Architecture Optimization: Overcoming Physical Bottlenecks (Memory Wall) While intelligence is implemented in software, its performance and economic sustainability are defined by the physical limits of hardware. Physical Constraint Analysis: We diagnose the "Memory Wall" problem, where data transfer speeds cannot keep up with the processing speeds of computing units, and heat generation issues resulting from high-density computation from an engineering perspective. Infrastructure Design Capability: We precisely analyze the physical impact of High Bandwidth Memory (HBM) stacking structures and 2.5D/3D advanced packaging technologies on inference efficiency. We cultivate design capabilities to optimize Total Cost of Ownership (TCO) through full-stack integrated insights that complement hardware limitations with software architecture. ④ Acceleration of Functional Expansion: Transitioning from Passive Tools to Autonomous Agent Systems Current AI remains at the level of simple Q&A, failing to create added value for practical business automation. This course evolves AI into an active subject that judges and executes on its own. Decomposition: We learn techniques for decomposing complex goals into achievable sub-tasks and logically organizing the execution sequence upon receiving them. Digital Workforce Deployment: We define the process of applying "Active Agent" systems to the field, which autonomously call internal ERPs, browsers, and external APIs to complete actual business logic and accept feedback on results. 3. Core Architecture: Closed-loop Control System The way an AI agent manifests intelligence and performs complex tasks is theoretically identical in logical structure to the closed-loop control system performed by an ECU (Electronic Control Unit), the core brain of a car. This course analyzes this in detail from the perspective of the ReAct (Reasoning and Acting) framework. First, the system begins at the Input stage, receiving ambiguous and complex requests from the user. This plays the same role as a sensor in control engineering collecting physical data from the external environment and delivering it to the system, serving as the standard for defining the initial state of the task at hand. Second, based on the received data, the Thought stage proceeds, where plans are established through logical reasoning within the LLM architecture. This is in line with the process where control algorithms in an ECU calculate optimal control values by processing input sensor data. At this stage, the agent sets the optimal path to achieve the goal and secures the logical rigor of the system. Third, the Action stage follows, where tasks are completed by calling external tools or APIs according to the established plan. This logically matches the mechanism where the calculation results of a control system are converted into physical power through an actuator to execute commands. Through this, intelligence exerts actual physical and digital influence beyond abstraction. Finally, the Observation stage is performed, analyzing the execution results and correcting errors relative to the initial goal. This is identical to the core principle of control engineering, which reduces system deviation through a feedback loop. The agent self-verifies whether the execution results meet the goal and continuously upgrades performance by reflecting occurred errors into the next action plan. AI equipped with such a closed-loop structure is no longer an incomplete system dependent on probability. By securing engineering rigor that self-verifies execution results and corrects errors, it functions as a trust-based partner capable of performing business-critical tasks. 4. Practical Application and Expansion: Software-Defined Vehicles (SDV) and Physical AI The final destination of AI architecture lies in the cross-industry proliferation of Software-Defined Vehicles (SDV) and Physical AI, which overcome and evolve physical constraints through software intelligence. This is the standard model for future System Integration (SI) across manufacturing and service industries. Securing Edge Intelligence and Data Sovereignty: Small models (SLMs) mounted on-device in vehicles or facilities learn real-time field data immediately. This minimizes cloud dependency, perfectly protecting data sovereignty—a core asset of the company—and enables precision services based on ultra-low latency. Hardware Optimization and Lightweight Engineering: To implement the best intelligence within limited power and computational resources, we actively introduce model compression technologies such as Quantization, Pruning, and Knowledge Distillation. Model deployment considering hardware bandwidth becomes a core competency that determines system response speed and user experience. Hybrid Orchestration: We design an integrated architecture that organically connects "Cloud LLMs" possessing broad general knowledge with "Edge SLMs" specialized for specific physical control and security. Integration from a full-stack perspective, penetrating from silicon chipsets to software stacks, provides a powerful competitive advantage that evolves the entire system through software updates alone. 5. Conclusion: The Role and Vision of the AI Architect The ultimate goal of this masterclass is to elevate students from the position of a "User" who passively relies on technology and hopes for luck, to a professional "AI Architect" who perfectly controls and tunes everything from the physical limits of the system to the depths of the software architecture. While the phenomenon of intelligence manifests from software logic, it is silicon (hardware) that defines the physical limits of that intelligence, and only sophisticated engineering can overcome those limits to complete actual business value. "Intelligence may reside in the realm of probability, but the vessel that contains that intelligence and makes it operate according to purpose must belong solely to the realm of rigorous and sophisticated engineering."

👉 Recommended for those who want to view AI results not as “values that simply came out,” but as understandable judgments. What exactly does AI use as the basis for making judgments? This course structurally explains how data affects AI’s judgments and why data is said to be important. You will learn how AI interprets numerical information and develops criteria for making judgments.

[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.

Create a wide range of design deliverables using ChatGPT Images 2.0, including logos, product photos, mockups, banners, wedding snapshots, wedding invitations, POP displays, menus, and product detail pages. This hands-on course teaches you how to write prompts to achieve the desired results and how to structure reusable templates.

We provide an easy understanding of the concepts and principles of generative AI from a humanities perspective, including literature, film, music, and history, while exploring the cultural impacts and life changes AI will bring. We offer practical insights and strategies on how individuals and companies should respond and prepare for the AI era.

1. What is the real 'problem' you are facing? Those who have worked as engineers, researchers, or business writers and have attempted to introduce AI into their workflow usually hit the following fatal walls: The Paradox of 'Volume vs. Value': When you ask a question, plausible-sounding text pours out, but when you try to apply it to actual work, it is useless because the facts are wrong or it lacks depth. The uncontrolled mass production of content ultimately only results in costs such as data bleeding and hallucinations. The Limits of Casual Chatting: The quality of the output depends on 'luck' every time. An AI that was smart yesterday gives nonsensical answers today, and when you force a specific template format, a 'degradation of intelligence' occurs where the model's reasoning ability plummets. Isolation of Knowledge: There is no standardized methodology to convert the experienced 'tacit knowledge' accumulated in the field over decades into 'explicit knowledge assets' that all organization members can copy, paste, and use. Ultimately, many people have downgraded AI to a mere "assistant for light summaries or email drafts," while still suffering through manual labor for critical business tasks such as high-density engineering or strategic planning. 2. How to solve these problems with 40 years of R&D tacit knowledge and systems engineering Throughout my life, I have successfully led projects worth hundreds of billions of won by passing through the rigorous process gates of ultra-precision internal combustion engines and Systems Engineering (MBSE). I have transplanted this engineering intuition directly into prompt design, embedding a paradigm into this course that treats prompts not as simple questions, but as 'system configuration files (Config Files)' that control statistical uncertainty. Through this course, students will acquire the following specific solutions: Introduction of XML-based Structured Architecture: We exclude ambiguous prose prompts and design guardrails to prevent information bleeding and prompt decay through precise XML tags and Markdown fences such as <role>, <task>, <constraints>, and <context>. 7-Step Gate Process Chaining: By borrowing the step-by-step control processes of automotive R&D, you will be able to perfectly command a 7-step chaining workflow—from instruction standby to literature review, critique and self-verification, and final compilation—where the AI verifies its own logic as it progresses. Hybrid Decoupled Pipeline Control: When utilizing the latest reasoning models like o1 or DeepSeek-R1, you will secure the control to preserve 100% of intelligence by handling skeleton code for a 'Reasoning-Formatting Hybrid Decoupled Pipeline' that bypasses the phenomenon of intelligence degradation caused by format constraints. Quantitative ROI Quantification of Business Value: You will master ROI formulas and KPI models to prove validity to C-level executives with figures. For example, when this pipeline is actually built in-house, the average lead time for writing technical white papers or regulatory compliance documents is reduced from 4–6 months to within 10 days (a 93.3% reduction), and more than 65% of research costs are saved as marginal costs.

Are you unable to use ChatGPT because you’re on an internal or isolated network, concerned about the subscription cost, or worried about company data being sent to external servers? In this course, you’ll build an AI agent directly on your PC that runs without an internet connection, using just one 8GB graphics card released in 2017. Even if you don’t know any commands, installation is complete after double-clicking three files. We’ll disclose everything, including errors encountered during the setup process and how they were resolved, as well as directly measured speeds and records of external communications.

You can learn the core concepts of complex Next.js in a short time. We'll teach you only the essential practical knowledge for those learning Next.js for the first time. Captain Pangyo's new Next.js course, who has been conducting framework courses for 8 years. You can learn the coding and learning methods of "Frontend Developers in the AI Era"! 😊

Many people want to create dog videos with AI but don’t know which tools to use or feel overwhelmed about how to write prompts. Designed for beginners, this course focuses on the practical process of creating actual results rather than complex theory. Using a dog interview video as an example, it explains the key steps—from image generation and prompt writing to video production—so you can learn them easily and quickly. Although the course is short, it covers only the parts that beginners find most confusing, helping you understand the overall AI video creation process and apply it to creating your own content after completing the course.

This course covers implementing combat, UI, data, and server functionality for a Unity idle game using CLI- and MCP-based AI tools. You’ll learn the entire process of applying Firebase, ads, in-app purchases, and Addressables to launch a mobile game on Google Play.

👉 A starting point for those who want to properly understand AI for the first time. If you understand what AI does but aren’t quite sure why it works the way it does, this is the course to start with. In this course, we’ll explain step by step, from the beginning, how artificial intelligence differs from traditional programs and how learning and decision-making take place. Without complicated equations or coding, the course is designed to help you understand the big picture of how AI works.

Stop getting student-record planning. I’ll reveal the entire consulting playbook used by a college admissions consultant with 30 years of experience.

The ultimate video editing powerhouse 🟪 Learn Premiere Pro and 9 AI services while creating 5 real-world projects to become a high-quality AI Shorts creator upon course completion 🔥 Don't learn from beginners—learn from an expert 😎

The real story of an AI-native company run by GolbinHacker, the 1st place winner of Kakao AI TOP 100 and author of the bestseller "The Best Prompt Engineering Lecture." It explores how just two developers build and operate a service, along with the new competencies developers and organizations need in the AI era through real-world cases.

Using AI has become easier, but model cards and papers are still difficult to read, even just a few lines. Although you can look up the meanings of individual words, it’s not clear how those words connect to one another. This course builds up around 160 essential terms that frequently appear in AI documents, in the easiest order to understand them, so that in each lesson you can accurately read a single sentence from an actual document.

During the Gold Rush, it was said that it was easier to become rich by selling gold mining tools than by mining gold yourself. How about selling programs in the ChatGPT Age of Exploration?