[Hands-On] Building an Investment Research Bot for Fund Managers — Combining 7 Practical Investment Bots into One System

Bots are harder to keep running than to build. This covers how to operate bots you can trust for the long haul—from health-check bots and real-world outage cases to cost and security management, market-regime gauges, and backtesting.

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.

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!

From meeting preparation to writing minutes, AI does it all! The first in our series on using AI Agents for company work!

Build your own virtual office where multiple AI employees work across content, data, and finance departments—with self-hosted AI agent Hermes and OpenAI Codex—instead of just one AI assistant delivering your morning briefing. 74 units to complete, with unlimited access!

There are so many stories saying, "Automation is just one click away." However, it's easy to give up when you actually try to apply it to your own work. We have captured the raw record of a team that spent six weeks narrowing the gap between "it works" and "I am using this for my work!"

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 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%.

Have you introduced AI but still find yourself losing time to repetitive tasks? The problem isn't the AI; it's the 'system.' This course guides you through the process of building a team of six AI assistants—including an executive assistant, content assistant, marketing assistant, CRM assistant, and personal productivity assistant—all using Notion AI. Based on my experience automating hundreds of repetitive tasks in real-world business settings, we will build actual workflows from start to finish, covering everything from organizing notes and automatically generating meeting minutes to blog writing, CRM construction, and automated KakaoTalk notifications. After completing this course, you will have a system where your own AI assistant team works automatically, powered solely by a Notion Business plan.

The lecture explains why the harness is more important than the model through the lens of Claude Code architecture. It also covers how to design a Claude Code work system around six structures: Context Harness, Permission Harness, Verification Harness, Tool Harness, Subagent & Worktree Harness, and Debugging Harness. It then connects these concepts to CLAUDE.md, session management, controlling large codebases, parallel worktree operations, error log analysis, and tracking environment variable and configuration scope issues, helping you establish practical standards for continuing to use Claude Code in real-world backend and DevOps work.

Evolve Obsidian beyond a simple notepad into a personal knowledge database, and build an AI assistant that understands your notes and documents using Claude Code. Create your own workflows to automate repetitive tasks, completing a knowledge system where your daily work and learning compound over time and transform into your own unique expertise.

The ultimate profitable service: I create fortune-telling services that keep running even while I sleep. With a service built in just one week, I generated 2 million won in revenue during the first month without spending anything on advertising. My second service, built recently in just three days, generated 340,000 won in net revenue in a single day. This is not just a simple 'click-and-go' fortune-telling service. I am in charge of creating fortune-telling services for one of Korea’s leading associations of professional fortune-tellers, and I also develop and operate fortune-telling services made using IP purchased from Naver Webtoon. Starting from cases I have operated myself, this course walks you through the process of creating a high-quality fortune-telling service for your own brand by conversing with AI—even if you have no technical background. You will implement everything from characters and stories to fortune-telling results, payment and automated delivery, and an admin dashboard, then connect customer acquisition through automated Threads posts, Meta ads, and YouTube content. This is a 'real' fortune-telling course focused not just on the fun of creating, but on monetization.

From AWS EC2 backend operations to automatic error fixing, we are revealing the entire development process of Sidabari (https://github.com/cx8537/sidabari), a Claude Code-based automation tool. In this course, we provide all the actual prompts and conversation history used as learning materials, allowing you to experience the entire prompt input process involved in its creation.

AI handles the first draft; I handle the judgment and polish. Learn the practical skills and terminology that junior planners use from day one, and work with Claude to create a complete set of deliverables for a study café reservation service: a project proposal, menu structure, screen designs, and functional specifications.

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.

How much can you entrust to an AI Agent? IBM engineers answer with evidence they’ve put to the test, covering everything from LLM fundamentals to vendor-specific design, security, and evaluation—and help you create a Skill to take over your work.

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

A Practical AI Automation Series That Plugs Directly into Your Work If you’ve learned about AI but still feel unsure how to apply it to your work, what you need now isn’t an explanation of features—it’s a practical way to use AI that transforms your work right away. The SMART WORK ORDER series is a hands-on AI automation program designed to reduce repetitive tasks across marketing, planning, HR and general affairs, sales, customer support, data analysis, and team leadership and management—and turn them into real-world work outputs. From content drafts, meeting minutes, proposals, consultation scripts, FAQs, VOC reports, and data analysis reports to team prompt libraries. Each course is designed not as a “lecture you watch and understand,” but as a hands-on experience where you follow along and create tangible results. The prompts and templates you learn today can be applied to your work tomorrow, expanding into practical assets that improve not only individual work speed but also team productivity. AI-driven work transformation that reduces repetitive tasks while raising the quality of your deliverables. SMART WORK ORDER is a job-specific, practical AI program that changes the way you work—making it faster, more accurate, and smarter.

Generative AI is rewriting how the world works — and the people who learn it now will lead the next decade. Every industry is being reshaped in real time. Marketers are creating campaigns in minutes. Developers are shipping code faster than ever. Entire teams are automating work that used to take days. And at the center of it all sits one skill that separates those who get left behind from those who get ahead: knowing how to actually use Generative AI. The problem? Most people are still only scratching the surface. They've typed a question into ChatGPT, gotten an average answer, and assumed that's all there is. But beneath that simple chat box is one of the most powerful technologies ever built — and once you understand how it works and how to direct it, you'll never work the same way again. That's exactly what this course is built to do. Introduction to Generative AI takes you from complete beginner to confident, capable AI user — no coding, no maths, and no prior experience required. In clear, jargon-free language, you'll discover what Generative AI really is, how it works under the hood, and how to put it to work in your job, your business, and your everyday life. This isn't a dry, theoretical lecture series. It's a practical, demo-driven journey packed with real examples you can follow and copy immediately. You'll start with the foundations — what Artificial Intelligence, Machine Learning, and Deep Learning actually mean, and how Generative AI fits into the bigger picture. You'll explore ChatGPT's features and capabilities hands-on, understand why this technology has exploded so quickly, and trace its remarkable evolution. From there, you'll demystify the engine that powers it all: Large Language Models. You'll see exactly how next-word prediction, embeddings, and fine-tuning work, and you'll understand game-changing concepts like Retrieval Augmented Generation (RAG) and the rise of Agentic AI — the difference between an assistant that answers and one that acts. Then comes the real superpower: prompt engineering. This is where most learners go from "AI is interesting" to "AI is indispensable." You won't just learn what prompting is — you'll master more than a dozen proven techniques used by professionals every day, including chain-of-thought prompting, zero/one/few-shot prompting, tabular prompting, perspective prompting, comparative prompting, reverse prompting, the RGC framework, and many more. You'll get ready-to-use prompt starters, learn how to revise and sharpen your prompts for dramatically better results, and discover how to generate fresh ideas, marketing copy, client emails, and content at scale. By the end, you'll know how to make AI do precisely what you want — consistently. You'll see it all in action. This course is loaded with practical demonstrations across real business scenarios. Watch AI summarise meetings and capture insights with an AI notetaker, draft polished email responses in seconds, and spin up multiple marketing headline variations from a single product image. You'll explore how Generative AI is transforming software development, retail, and marketing, and you'll see real tools at work — generating working code with GitHub Copilot and creating images and video with cutting-edge tools like Amazon Nova. These aren't hypotheticals. They're workflows you can start using the moment you finish the lesson. But great power demands responsibility — and this course doesn't skip the hard parts. You'll dive into Responsible AI and learn how to spot and manage hallucinations, factual inaccuracies, bias, and fairness issues. You'll understand the genuine technical limitations of today's models and the practical workarounds that experienced users rely on. Through real examples — including safety refusals and bias-correction rewrites — you'll learn to use these tools wisely, ethically, and with confidence, the way professionals and organisations are expected to. And you'll finish looking forward, not just at today. You'll discover how Generative AI is disrupting search and reshaping the way the world finds information, explore the road toward Artificial General Intelligence (AGI), and get a clear-eyed view of the jobs, certifications, and fast-growing career opportunities this field is creating. Whether you want to future-proof your career, boost your productivity, or build something new, you'll leave knowing exactly where this technology is heading and how to ride the wave. Why learn from this course? You'll be joining a community of over 207,000+ students taught across leading platforms, backed by a 4.6 instructor rating and 13,000+ reviews. Every concept is explained simply, every technique is shown in practice, and every lesson is designed to take you one step closer to genuine confidence with AI. No fluff. No overwhelm. Just clear, practical, up-to-date learning that respects your time. This course is for you if you're a beginner curious about AI, a professional who wants to work faster and smarter, a marketer, creator, developer, entrepreneur, or simply someone who refuses to be left behind by the biggest technology shift of our generation. If you can use a web browser, you can do this course — and you can start today. The gap between people who use AI and people who master it is widening every single day. The tools are here. The opportunity is now. The only question is which side of that gap you want to be on. Enrol today and start your journey from AI curious to AI confident — one practical, powerful lesson at a time.

AI creates the first draft; I make the decisions and refine it. Using Figma’s free plan and free AI tools, I design a set of screens for a laundry pickup app, then create a clickable prototype and a feature specification to hand off to developers.