A Practical Review Workflow for Validating and Deploying AI-Generated Code

Learn what JEV is in simple terms first. Then follow the process of checking the results in the TypeSafe AI Playground and creating a Mac app that recommends emojis matching a sentence by making verbal requests to Claude Code. From signing up and connecting your API key to making your code public on GitHub, learn together through five videos and learning materials that include screen captures and prompts you can copy.

How was a service that attracted 1.1 million visitors in just two weeks with zero advertising costs created? I’ll show you the entire process exactly as it happened: as a UX researcher with 8 years of experience, I turned an idea into a service in just 2 hours through vibe coding, then used real user behavior data to decide on the next experiments and monetization strategies. Ultimately, I’ll prove with real data from 1.2 million people that what matters isn’t the technical skill of building, but the ability to understand “what people want.”

You don’t need to know how to code. Once you learn how to “give the right instructions” to AI tools like Claude and Codex, you can create everything yourself—from cinematic websites and motion-graphics promotional videos to AI short dramas, ads, and even podcasts made with your own voice. You’ll receive more than 20 ready-to-use kits containing projects that are actually being created and used, and we’ll show you the entire process exactly as it appears on screen: handing the folder over to AI and converting it to your brand → fixing only the parts you want with a single screenshot → completing Vercel deployment and domain connection. Recorded in October 2026 using the latest AI tools (Claude Opus 5.5 · GPT-6.1 · Grok Build), this course focuses on workflows that continue to work even when the models change.

The “AI Team Members Do It All” online course is a hands-on program where you learn to use AI not simply as an automation tool, but as an “AI team member” that works alongside you in carrying out practical tasks. The course is structured around common real-world workflows, including market research, proposal writing, strategy report creation, brand content visualization, project promotion and operations, and post-project management. Participants assign various AI tools—such as ChatGPT, Perplexity, Copilot, Canva, Gamma, Miricanvas, and Vrew—to roles including planning partner, strategic research partner, design partner, and operations support partner, and use them to complete actual work deliverables. Across 30 sessions, you will learn step by step, from the fundamental mindset for working with AI to practical prompt writing, market research, proposal and report writing, brand content creation, and project operations. By following the course, you can develop the AI collaboration skills needed to work independently like an entire team.

For those of you who have completed intermediate SwiftUI, it is now time to integrate AI into your apps. Complete an on-device AI app that runs locally without an internet connection using Apple Intelligence and the FoundationModels framework.

No Coding Skills Required: Build Your Very First Service with Google Stitch An All-in-One AI Development Workflow That Seamlessly Connects Every Step from Planning to Deployment

Core Principles of n8n Automation and Workflow Design Basics for Non-Majors 5-Step Automation Built with n8n: API Connection Practice from Topic Input to Video Completion How to Automatically Create Videos in n8n Using AI APIs (ChatGPT, ElevenLabs, Creatomate)

: "From Mathematical Foundations to the Latest Models, Completing the Deep Learning Pipeline with PyTorch (44 Lectures in Total)" The era of simply learning how to call model.fit() is over. From the very basics of artificial neural networks—differentiation and gradient descent—to essential PyTorch techniques used in the industry, and CNN/RNN for handling image and time-series data! We will help you systematically master the entire process of deep learning. Go beyond analyzed data and step into the amazing world of deep learning, where artificial intelligence mimicking the human brain learns and makes decisions on its own.

Are you feeling stuck on how to apply what you've learned to real-world tasks even after mastering the basics? I will help you complete complex RAG and agent designs using my professional industry know-how.

Your AI is no longer in the cloud. It runs directly on your laptop. Learn how to build a completely local Private AI environment using LM Studio and Ollama—for everything from data processing and document analysis to code generation. With no security concerns, no costs, and greater speed— move from simply “using” AI to “putting it to work” yourself.

This is an introductory course for those using Claude Code for the first time. You won’t do the coding yourself—Claude writes both the code and the configuration files. Starting with creating a psychological test in the browser, you’ll build a household budget using card transaction records, create an escape room game, and make a travel itinerary that connects to external services like Google Calendar and Notion. Since you learn each feature as it becomes necessary, you’ll also understand why that feature is needed.

I will teach you Vibe Coding systematically from the ground up, following the exact curriculum refined while teaching over 10,000 students by a former Tech Lead with 10 years of experience in New York and LA startups. Together, we will build my own service, 'Kagong Map,' from scratch, learning the entire real-world workflow—from research, development, and deployment to operation and automation by delegating tasks to AI agents. I promise you two things: first, you will grasp the big picture of how a service operates so you can judge the AI's output yourself; second, you will learn how to embed workflows and standards into the environment so the AI produces consistent results every time. When you make the judgments and the AI follows orders effectively, your ideas become real services.

"In the age of AI, what should developers do?" Former Toss Payments Technical Director Jaemin Kim and Inflearn CTO Hyangro answer this question. Beyond flashy technology, discover the most realistic answer to 'how to grow steadily in the environment you’re given'!

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.

From coding basics to launching your own app all at once?! The App Build All-in-One, connecting fundamentals and practical application.

An advanced, hands-on course on designing an AI assistant with OpenClaw to perform repetitive tasks with consistent quality. You will configure memory, rule files, permissions, role separation, and stop conditions, then build a reliable AI assistant operating system you can trust with everything from recurring document creation to regular briefings.

Design by me, code by AI: Learn object-oriented design with Unity 6 and C#, Copilot / Claude prompting, and AI code verification to complete a 60-second survival 3D game. Become a game developer who knows how to work with AI.

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.

Part I Practiced in code the process of loading a PDF as page-by-page Documents, chunking it, converting the chunks into vectors through embeddings, and indexing them in a vector store. Learned the structure of converting questions into vectors, performing similarity searches (including scores, vectors, and MMR), wrapping the process as a Retriever (Runnable), and connecting it to RAG. Established practical standards for application by organizing search quality checks, security and governance (including prompt injection), and common mistakes. Part II Implemented in R the random forest algorithm, which overcomes the limitations of decision trees, and learned how to accurately interpret model results. Built the ability to read and explain key metrics such as OOB error, variable importance, and confusion matrices using iris data and real-world biomarker data. Completed an end-to-end workflow applicable directly in practice, covering hyperparameter tuning, cross-validation, and ROC curve analysis.

"Beyond Data Analysis: From Predictive Model Design to Optimization (29 Lectures in Total)" Have you finished your data analysis but feel stuck when it actually comes to building a model? We help you fully master not just how to call libraries, but the working principles of each algorithm and optimal model validation strategies.