From my life's OS with Claude Code, to my team's OS with OpenClaude.

What if AI could directly open a browser and write shell scripts to resolve server issues? Through practical automation know-how implemented with OpenClaw across Windows and Linux, we guide you on the shortest path to completing your own powerful remote development environment without complex configurations.

Explains why LLMs are needed, along with their technical background and basic concepts, for those responsible for LLM planning and LLM application planning.

Learn the core techniques of 'Prompt Engineering' to get exactly the answers you want from AI. From a complete beginner's first steps to expert practical applications, maximize your AI utilization skills!

This is a tutorial-style Claude Code development course that beginners and those new to development can easily follow. Based on experience teaching planners, designers, and non-developers in offline classes, the course covers everything in one continuous flow—from beginner examples not covered in books to intermediate development examples. Using Claude Code, you will experience the step-by-step development process, starting with Hello World and progressing to a lucky draw, a device-optimized café menu management web page integrating Claude Design with Excel data, and a SaaS web application that can be connected to a database and deployed to cloud services. Tutorials are provided at each stage—theory, practice, and projects. Through hands-on exercises across 6 levels, you will learn AI-collaborative development methodologies and experience the entire development cycle, including cloud deployment and operations.

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.

The more you modify, the more anxious vibe coding can become. Now create save points you can return to anytime. You don’t need to know Git commands. Just tell the AI what to do, and it will handle saving, backups, and restoration.

We will look at how to collect and analyze user requirements for planning an LLM service.

The course consists of a practical curriculum that can be immediately applied to the field, ranging from how to use the latest agent tools such as Genspark and Manus to corporate security guidelines. Through this, you can directly build an AI agent collaboration system that automates repetitive tasks such as information gathering, data analysis, and basic report writing.

Build a Saju (Four Pillars of Destiny) analysis AI agent that integrates Manseryeok, Astrology, Zi Wei Dou Shu, Onomancy (Name Study), and MBTI using n8n workflows and Python algorithms. We will complete a production-level service system from start to finish where 13 specialized agents collaborate to generate professional-grade Saju reports.

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

A comprehensive course covering AI-era-proof Silicon Valley internships, 30 big-tech system designs, and hands-on open-source contributions. Using real-world cases from Netflix, Tesla, Meta, Tinder, and other big tech companies, you’ll design and scale 30 practical system designs. Finally, you’ll design your own service from architecture to database to scaling.

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."

Go beyond a simple system prompt to build an AI character chatbot that remembers users and whose relationship with them evolves, using persona design and Structured Outputs. In this 30-minute hands-on session, you’ll learn how to safely call the OpenAI API based on the provided starter project, process emotions, trust levels, and memories in a verifiable format using JSON Schema and Zod, and use an AI coding agent within defined constraints.

The era of Physical AI, as mentioned by Jensen Huang in 2025, is coming—alongside Perception AI, Generative AI, and Agentic AI. Experience Physical AI firsthand, quickly and easily. Explore Physical AI using Hugging Face's LeRobot library and actual physical robots.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

It is difficult to complete a classification service using only AI API calls. Based on practical experience, I guide you through everything from implementation to deployment.

Learn how to efficiently manage multiple AI coding agents, including Claude Code, Codex, and pi, from a single screen. Starting with tmux concepts, you’ll practice using Herdr’s sessions, panes, plugins, and CLI to build an agent workflow that keeps your work running even when your SSH connection is interrupted. Complete the essential development environment for the Agent era in just one day.

AI is only as good as you are. Learn vibe coding properly in three steps: how to use AI → essential foundational knowledge → project development. For those who want to learn vibe coding the right way, we deliver the core concepts that remain unchanged regardless of which AI you use, broken down into bite-sized pieces.

In this lecture, you’ll see in practice how AI agents can use the Browser MCP built into Antigravity to open a real browser and research and make judgments on the web. Rather than merely covering how to use it, the focus is on understanding the execution flow from Agent → Tool → Browser and the MCP-based extensibility structure. In 30 minutes, you’ll go beyond the concept of browser automation and structurally organize “how AI uses the web as a tool.” https://antigravity.google/download

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.