Korean Version of an International Conference Presentation: Kubernetes and AI Agents

Building a Retrieval-Augmented Generation (RAG) Intelligent AI Agent with LangGraph! From theory to practice, this is a hands-on tutorial that even beginners can easily follow.

This course is designed to use only personal PCs and laptops. You can directly implement CI/CD automation processes by applying GitOps methodology in a k8s environment without using cost-incurring cloud environments like AWS or GCP.

This is a lecture that will help you develop the ability to see the essence by directly constructing the overall framework of the actual CI/CD work rather than just learning theoretical content.

This lecture covers RAG (Retrieval-Augmented Generation) system fundamentals and implementation using LangChain. Participants will understand RAG's core principles and learn to build and evaluate systems using LangChain.

I have created an 'Introduction to MSA' course that is easy for non-majors to understand and can be applied immediately in practice!

Done in a single line of command. Hermes, the AI agent that remembers, automates, and grows on its own. Even if you are not a developer, you can become someone who 'delegates' work to AI starting today. SMARTFORK will be with you from installation to practical application.

LangGraph, packed with the know-how of an AI Agent lead from a major corporation. I will pass on the knowledge I've gained through trial and error in the field.

It explains almost everything surrounding the cloud in an easy-to-understand manner. If you listen to it before the actual practice, you will be able to understand development or infrastructure cloud well.

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.

Do the results of vibe coding keep turning out differently from what you intended? Learn a workflow that uses specification-driven development (SDD) and Spec Kit to define goals, scope, business rules, and completion criteria, then shares the same specifications with Claude Code and Codex to reduce repetitive revisions.

This course covers the basics of Amazon EKS, an AWS-managed Kubernetes service, designed for beginner Amazon EKS users.

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.

This course is designed for Developers, DevOps Engineers, Security Engineers, Freshers, QA, Infrastructure, Build & Release, IT, and InfoSec/AppSec professionals who want to learn and implement DevSecOps practices using real-world tools, technologies, and industry security best practices. You will gain hands-on experience integrating security across the entire Software Development Life Cycle (SDLC)—from architecture and code to infrastructure, pipelines, containers, and runtime environments. This course equips you with high-demand DevSecOps skills that can help you secure top-quality roles with higher compensation, similar to current market trends in DevOps, Cloud, and Cybersecurity. In today’s digital-first world, embedding security into engineering workflows is no longer optional—it is essential for business success. DevSecOps: Integrating Security into the SDLC Using Jenkins and Modern Tooling This course is ideal for individuals and teams who want to shift security left and build secure, scalable, and automated DevOps pipelines. You will learn how to: Integrate security principles, architecture, and organizational best practices into DevOps workflows Prevent accidental exposure of secrets, credentials, and sensitive data in source code repositories Secure Linux systems, virtual machines, and CI/CD build agents Identify, fix, and patch vulnerabilities in: Application dependencies Dockerfiles and container images Runtime application environments Secure Docker images, containers, and containerized workloads Secure Infrastructure as Code (Terraform) using policy and security scanning Harden and secure CI/CD pipelines against misconfigurations and attacks Perform security testing using: Threat Modeling Unit and Integration Tests Software Composition Analysis (SCA) Static Application Security Testing (SAST) Dynamic Application Security Testing (DAST) Automate security testing during build, test, and deployment, instead of relying on manual post-release testing Learning Experience Each concept is explained in a simple, visual, and structured manner: Slide-based conceptual explanations Followed by hands-on, real-world labs You will: Set up your own Linux VMs, security tools, and CI/CD pipelines using Jenkins Secure Docker images, containers, and infrastructure Work with a dedicated GitHub repository containing all code, configurations, and examples used in the course Why Enroll in This Course? ✅ One of the most practical, hands-on DevSecOps Bootcamps available online ✅ Learn DevSecOps, Cloud Security, and Application Security without expensive institutes or tutors (often costing $400–$4000) ✅ Self-paced learning with a comprehensive, up-to-date curriculum ✅ Clear understanding of the difference between traditional DevOps vs modern DevSecOps ✅ Learn how to embed security at every stage of the pipeline ✅ Automate security scanning early instead of fixing issues after production ✅ Hands-on experience with free and open-source security tools ✅ Industry insights referenced from Gartner reports on DevOps and Security collaboration By the End of the Course By the end of this course, you will have gained strong theoretical knowledge and extensive hands-on experience with DevSecOps tools, technologies, and practices. You will be confident in designing, building, and implementing secure DevOps or DevSecOps pipelines independently for real-world projects and organizations. You will also have access to a DevSecOps Quiz to test and validate your foundational knowledge in DevSecOps and SecOps. Goals Learning Experience Each concept is explained in a simple, visual, and structured manner: Slide-based conceptual explanations Followed by hands-on, real-world labs You will: Set up your own Linux VMs, security tools, and CI/CD pipelines using Jenkins Secure Docker images, containers, and infrastructure Work with a dedicated GitHub repository containing all code, configurations, and examples used in the course Prerequisites Basic knowledge of the following is helpful but not mandatory—everything is covered from scratch: DevOps & Jenkins Docker & Containers Linux, CLI & Shell Scripting Security fundamentals

Learn cloud infrastructure automation technology using AWS and Terraform, and practice efficient infrastructure management and deployment. Manage AWS resources as code through Terraform, and learn how to deploy consistent infrastructure across various environments.

Before execution, define the scope, rules of engagement, test conditions, and grounds for assessment. Link AI assets and trust boundaries with STRIDE scenarios to create a red team plan and threat model, and distinguish what can be stated based on the results of repeated tests.

Systematically learn Docker, a core tool for container-based application deployment, from the basics to practical application. Starting with Docker's basic structure and commands, you will develop practical skills that can be immediately applied to real-world tasks, including container packaging in microservices architecture environments, deployment automation, and standardization of production environments.

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.

If you follow OpenClaw Parts 1 and 2, you eventually end up making a single agent do everything. As you do this, there comes a moment when prompts get tangled, memory gets mixed up, folders get messy, and you feel stuck, not knowing how to organize things anymore. This lecture is the "Introduction to Multi-Agents (Part 2.5)," designed to help you move one step further from that point. While maintaining a single bot, we will separate roles into two agents—planner and editor—and connect each agent to different workspaces and Telegram forum topics. You will personally build a structure where "different agents respond depending on which room you speak in." By following the hands-on practice, you will have the following in your environment: - A configuration file defining two agents: `content_planner` and `content_editor`, - A content folder structure where planning and drafting are separated, - A Telegram group chat where different agents are attached to specific forum topics, - And one actual draft of an Inflearn course introduction created through the collaboration of the two agents. In short, this is a practical class that helps you design the first step of upgrading your Telegram bot from a "single AI assistant" into a "small AI team with divided roles."

In the era of Agentic AI, where is AI development headed? Now, competitiveness lies not in using a single AI model well, but in the ability to select the right model for the task and connect AI Agents with development tools to solve real-world problems. We are releasing the presentation videos from the Qwen Conference 2026, a global AI conference hosted by Alibaba Cloud, for free. From the Qwen 2.5 Max announcement and the "Future of Reasoning" panel featuring NVIDIA and Fireworks AI, to multimodal models expanding into video and voice, and cases of AI Agents deployed in actual practice beyond mere demonstrations. Discover the direction of the Agent era as envisioned by a company that builds the entire stack—from models to infrastructure and applications.

Build a Kiwoom Securities API-based automated stock trading system from scratch using the core features of Claude Code (MCP, Agents, Skills). It's perfectly fine for beginners. Get started with Claude Code right now!