The AI development team I built on my own by taming Claude Code after spending $1,000 a month learning it

A course comprising 5 modules and 18 lessons that transforms generative AI LLM agents that only drain money through API costs into a structure that controls expenses and actually generates margins through model routing, cascading, caching, and loop guardrails. You’ll build everything hands-on, from analyzing real-world cost explosion cases and simulating profitability to designing the architecture. Recommended for AI developers and makers burdened by API costs, as well as anyone looking to operate or plan agent-based services.

Efficiently tackle the typical office tasks that keep employees working late—such as recurring monthly report writing and large-scale data organization—with the AI assistant Claude. Learn practical techniques for freely designing and enhancing long-form text visualizations and slides while preserving the unique formatting and layout of your company’s templates. Through this process, you can perfectly complete the manual documents that accumulate over time within the allotted deadline.

The starting point for a successful AX (AI Transformation) is not technology, but the leadership's understanding of AI. This course is designed to help executives grasp the overall flow of AI easily and clearly, while covering ways to maximize business productivity through the familiar Google Workspace and Gemini. Moving beyond mere theory, we provide 5-minute hands-on tasks for each session and a 1:1 customized online meeting (30 minutes) with AX Cody to systematically guide your company's tailored AI adoption strategy.

[Source Code Provided] No coding knowledge required. Build it instantly in your browser using Google's latest tool (IDX) without any installation! Stop studying coding syntax! This is an ultra-fast, hands-on course where you'll build an RAG chatbot that perfectly understands internal company documents through AI conversation (Vibe Coding) in the Google IDX environment and deploy it live to the web.

The Gemini agent that works across Google Drive! Put the finishing touches on your Google AI workflow with overwhelming productivity.

This course covers techniques for automating the entire marketing and sales process that drives revenue—from content planning and sales funnel design to CRM-based customer management—using AI. It transforms repetitive, labor-intensive tasks such as writing ad copy, analyzing target customer behavior, and organizing large volumes of customer consultations and histories through a systematic framework. Ultimately, you will build your own dedicated, GPTs-based AI marketing partner that fully adopts your role and brand identity, operates 24/7, and establish a continuous collaboration system.

You know about AI agent frameworks, but understanding how they work and applying them in practice can be difficult, right? Drawing on firsthand experience from Silicon Valley, we’ll guide you from the core principles to practical applications.

Have you ever wished you had Iron Man’s perfect assistant, JARVIS, too…?! Create JARVIS for everyone to help you work smarter!

From a developer who only used ChatGPT to a developer who handles AI agents. Learn practical ways to maximize coding productivity using Codex's Rules, Hooks, Skills, and MCP.

Even people who know nothing about coding will be able to give AI commands in Korean and create their own automated stock trading program!

Instead of writing longer prompts, I create rule files, logs, permissions, hooks, and validation routines one by one whenever something goes wrong in a real project. With a news alert bot that I actually built and deployed to a server in five days, 211 screenshots and 87 instructions exactly as originally written.

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

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%.

Connect the diagnostic basis from the previous lecture to the organization’s improvement plan. Review Shadow AI’s data flows and control application requirements, and write a control design document and red-team report containing observed evidence, impacts, limitations, action owners, and retesting criteria.

AI writes on Slack and Notion for me? Work smarter with MCP, which goes beyond the limits of existing AI!

Learn how to structure diverse data—including students’ grades, school records, and areas of interest—using the AI-SPARS tool, and leverage an LLM to design everything from major exploration and college/admissions strategies to personalized roadmaps. Rather than simple Q&A, you can immediately apply in practice a systematic AX-style career and college counseling process that progresses from Profile-Interview-Major-College-Admissions Track-Gap-Roadmap-Smart Report-Portfolio.

To avoid making a definitive diagnosis based on a single response, compare the input conditions, repeated results, errors, and logs. Observe direct and indirect prompt injections and create one evidence-backed Mission 4 diagnostic record. The hands-on environment from Part 1 and the threat model document from Part 3 are required.

There may be people who have never taken my lecture, but no one has taken it only once. After teaching in person for so long, I have finally had the opportunity to create this online course. Since the commercialization of AI, I have produced and researched countless pieces of AI content. Based on that knowledge and experience, I have built a logical philosophy of my own and organized it into concepts that are easy for anyone to understand. For those who have been generating AI images without considering the underlying concepts, I will instill a proper foundation in you. Once you take this course, you will be able to create any AI image in the future. Have you never created an AI image before? That’s perfectly fine. You don't need any prior experience in AI image generation. By simply learning and practicing through this lecture, you can become an expert in AI content creation.

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.

The rules of every game are changing — a new dimension of business opened by quantum computing. Moving beyond simple technological advancement, quantum computing has already emerged as a key variable determining the future business landscape, poised to shift paradigms across all industries. This analysis provides insightful perspectives on the massive wave of technological change, covering everything from the basic principles of quantum computers to innovation cases in finance, pharmaceuticals, security, and the strategic moves of global Big Tech. It offers the most definitive roadmap for professionals who wish to gain quantum literacy without complex physics knowledge and transform into future-ready talent.