Practical Methodologies No One Told You About Since the Launch of Claude Code

This course is a practical, hands-on training program that automates various document creation tasks—including repetitive document automation, report summarization, PPT and presentation script generation, meeting minutes organization, and smart research—by leveraging ChatGPT and generative AI tools. It is designed with a focus on practice and real-world examples, covering automation strategies for different document types and AI-based information collection and utilization methods, enabling learners to immediately apply what they learn to their actual work.

There's so much talk about AX these days. At Selfish Club, we started asking ourselves, "How 'agentic' can our way of working actually become?" So, for two months, we just dove right in. From how we drew the roadmap and what our non-developer crew members built with Claude, to how we transformed the process of creating product detail pages—which we used to build from scratch every time—into a system. Instead of a polished conclusion, we're sharing the raw, chaotic process of our trial and error.

This sharing session was prepared not for those who are already proficient in Claude, but for those who are just starting out and feel overwhelmed by where to begin. We will show you the entire process of a real-world project—from planning and CRM automation to content creation and ad analysis—all handled in one-stop within a single conversation, using actual cases.

This course is for non-developers who want to learn Claude Code but find the official documentation full of developer jargon, while YouTube focuses only on vibe coding (app/web development) and doesn’t explain how to use it for automating real-world work. Concepts are explained through comics and easy-to-understand analogies, with hands-on practice in a desktop app environment without using the terminal. It also walks you through three practical work-automation examples from start to finish: work automation, creating detailed product pages, and automating the production of card news, blog posts, and short-form videos.

Learn how to efficiently handle document creation and everyday tasks needed in the workplace using Claude Sonnet AI. You will learn the basic concepts of AI and how to use the Claude platform, as well as prompt-writing techniques that can be applied immediately through real-world work and daily life examples.

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.

A practical introductory course on Claude Code for Unity developers! From installing Claude Code to Unity MCP integration, managing project conventions using CLAUDE.md, custom slash commands, and collaboration workflows—we will teach you everything you need to know for real-world application. Finally, you will gain hands-on experience effectively applying Claude Code to a Unity project by building a Survivor-style game from scratch.

Get Started with Claude Code Faster Than Anyone Else | E-book Learn Claude Code quickly and start automating your work!

Master Claude Code from the basics to a professional level. Learn core features (CLAUDE.md · subagents · MCP · Hooks, etc.), develop and deploy a real-world service, and wrap up with foundational appendices and advanced-use lessons for non-majors.

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.

Simple tutorials alone make it difficult to apply in practice. I will clearly pass on my professional know-how, covering complex state management and multi-agent design methods.

AI is no longer a choice—it’s a matter of survival. Cut development time from 3 days to 30 minutes, delegate repetitive tasks to AI, and focus on solving more important problems. With AI-native development techniques you can apply immediately in real-world work—not just theory—we’ll show you a surefire way to be recognized at your company as a “developer who excels at AI.”

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%.

If you’ve found it difficult to explain what’s happening under the hood while using AI and embeddings, this course may be the answer. Drawing on the experience of a practicing AI developer in Silicon Valley, it helps you understand the principles of AI through code by implementing everything from embeddings and Word2Vec to automatic differentiation and transformers yourself.

I will show you "where to start" through a case study of building a service with non-developers in just one day, and explain "how to manage and collaborate" using the example of designing the GitHub structure for the Selfish Membership team.

We're building an AI-powered video conferencing application with real-time AI agents, meeting summary features, and post-call functionality.

A selfish era where only those who know how to properly utilize AI can generate profit. Essential web development concepts for profitability and a realistic guide you must know to 'properly' direct AI coding agents. From the mindset of coding without writing a single line of code to realistic methods for building a product with zero budget, and even practical details on business registration and tax processing. This is an AI-driven profitable web programming guide filled with insights gained from the actual field.

Beyond a chatbot that simply generates answers, you will directly implement an autonomous AI Agent that makes its own judgments, calls tools, and performs multi-step tasks. You will learn through step-by-step practice, covering everything from OpenAI Agents SDK's Tool Calling, Handoff, Guardrail, Session, and Tracing to connecting external tools and databases using MCP, as well as LangChain and Deep Agents' Planning, Subagents, Filesystem, and Memory. The ultimate goal is to equip you with the ability to design and implement tool-integrated AI Agents that can be applied to real-world tasks.

Strategic Resume Writing for Experienced Developers: Insights from an Interviewer Who Has Reviewed Thousands of Resumes ✅ Real insights from actual hiring decision-makers ✅ Strategies based on experience at both large corporations and startups ✅ Content specialized for server developers with 3+ years of experience ✅ "Interviewers are busy too" - How to win within 30 seconds ✅ AI is a tool, storytelling is the core

Are you tired of repetitive tasks? This one lecture is enough. Combine Make, Apps Script, and AI to build your own business automation system. From souvenir recommendations to receipt OCR processing, this is a practical, hands-on course where you just need to follow along, even if you don't know coding.