2026 Claude Code in Practice: Automating AI Development from Design to Implementation, Testing, and PRs

When AI says “implementation complete,” do you take it at face value? This Step 2 course has Claude Code and Codex handle everything from understanding the repository to implementation, testing, review, and PRs, while teaching you to evaluate results based not on completion descriptions, but on the scope of approval, execution evidence, and change risks.

Are you simply telling Codex, “Build it for me”? It works well at first. But as the project grows, the AI modifies the wrong files, forgets the rules, and repeats the same mistakes. In this course, you’ll learn how to use the **latest Codex** to delegate coding while keeping control over **design, permissions, context, and verification** as a developer. **Design → Plan → Implement → Test → GitHub PR** Based on the developer’s judgment and review, AI development automation is applied throughout this entire process.

Claude Work Automation That Gets You Off Work Earlier! Don’t just ask Claude questions—put it to work on real tasks. Learn how to automate repetitive daily tasks such as Excel spreadsheets, documents, meeting minutes, reports, and PPTs, so you can leave work earlier. No coding knowledge is required; this is a practical, project-based course that’s easy to follow and can be applied directly to your own work.

- Practical Kaggle Competition Project: From Data Exploration to Submission - Beyond Tutorials: Follow a Real Kaggle Competition from Start to Finish Have you signed up for Kaggle and completed the Titanic tutorial, only to feel lost about where to start when facing a real competition? If you simply copy someone else’s notebook and submit it, you’ll eventually hit the wall of wondering, "Why isn’t my score improving?" In this course, we’ll work through an actual Kaggle competition from start to finish, covering data exploration, validation design, feature engineering, modeling, and submission. Instead of memorizing code, you’ll develop an intuitive understanding of the competition mindset: “What should I try and why, and how can I validate its effectiveness?” [One line about your competition participation, awards, or practical experience]

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!

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.

Have you ever actually run a service you built? Deployment and observability, external integrations and queues, files and extensions, and operating AI features. This is Part 2, where you learn what else needs to be decided to keep an AI-built backend serving real users continuously.

Does changing it on the screen mean it’s been saved? From server responses and data persistence to login and permissions, AI responses and performance, and validation and deployment—this is Part 2 on what else you need to decide and verify before releasing an AI-generated interface to real users.

Building an MVP with AI is no longer difficult. However, most projects stop at the next stage. 👉 You’ve built the features 👉 But development doesn't continue Why does this happen? The problem isn't the code; 👉 It's because there is no structure that allows AI to work continuously. --- In this course, based on an existing project, 👉 We will cover the process of building a structure 👉 That allows AI to continue development on its own. --- Instead of simply using AI tools, we will: * Create a docs structure * Define the SSOT (Single Source of Truth) * Execute development on a per-ticket basis * Connect QA and iteration cycles 👉 To complete a single "AI Development System." --- Through this process, you will: 👉 Create a structure where development continues 👉 Even without a human manually coding. In other words, you can: 👉 Build and understand a development system 👉 That operates AI like a team. --- This course is designed for: 👉 Those who want to build a structure applicable to their projects immediately 👉 Those who started development with AI but found it difficult to sustain 👉 Those who want to move to the next level after "Vibe Coding" --- Beyond just learning, we provide an experience where you: 👉 Take action 👉 Build a structure that actually works 👉 And walk away with a framework you can apply to your own projects.

This is a process of developing a practical web service in collaboration with AI agents based on TARS prompt engineering principles. You will gain full-stack development experience covering everything from UI/UX component creation to CRUD functions, API integration, and data visualization, ultimately completing your own idea as a viable MVP.

[Let's Build!] Have you ever given up because you were tired of connecting nodes? "Create a daily morning briefing on Slack" — that's all it takes. Claude Code designs it, and the n8n server runs it 24/7 for you. [Let's Operate!] If adopting n8n was easy but managing it felt overwhelming, this lecture is the answer. "Organize everything according to the conventions" — dozens of workflows are tidied up at once. Creation, organization, monitoring, and backup—operate everything using natural language.

I got answers from AI, but am I still doing the work myself? From the basics of using Claude Code to building apps, automating Instagram carousel posts, and researching competitors’ social media. Follow along while watching the screen and learn how to apply it to your own work. This includes 11 beginner-focused desktop app lessons, along with hands-on practice for real-world tasks.

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!

Have AI document its decisions and reasoning in the project wiki every time it works, safeguard that wiki, and check it to ensure it doesn’t break months later. Build the disclosure alert bot for real, establishing rules one by one whenever something goes wrong.

We will summarize the theoretical concepts of MNIST, the most fundamental dataset in computer vision, and learn how to implement it using Torchvision's API as well as how to implement a custom dataset class directly.

"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.

This course conducts hands-on practice related to Diffusion models among generative artificial intelligence models. By reading and implementing the prompt-to-prompt paper, which is a representative Diffusion model application paper, we expect to cultivate the ability to understand the latest artificial intelligence papers.

AI-generated SQL ran without errors—but does that mean the result is correct? Learn to identify missing criteria in a row’s meaning, values, queries, aggregations, changes, and concurrent requests, as well as which data rules to communicate to AI and what evidence to use when reviewing the results.

We delve into how AI coding tools work internally based on 510,000 lines of leaked Claude Code source code. Understand context management, memory processing, and prompt delivery mechanisms, and learn vibe coding practices for applying them to real-world development workflows.

The screen created by AI looks plausible, but can you actually use it from start to finish? Learn how to find missing conditions in HTML, CSS, JavaScript, state, input, and navigation—and what screen requirements to give the AI and what criteria to use when reviewing the results.