AI Agent
AI agents today are incredible!!!
I delivered software with an estimated $1M engineering cost in just two days, and it only cost me $7.14 for the entire GitHub Copilot usage!
I built this website — 1chooo.com — from scratch in just two days. Not long ago, I spent over a year teaching myself the nuances of the React ecosystem just to make it as a large scale software builder. This week, using GitHub Copilot and the Claude Sonnet 4.6 model, I built a more advanced version of my vision in just two days. Guess how much it cost? ONLY $7.14 for the entire GitHub Copilot usage!
Building websites has become much more fun. Ideas that used to take days or weeks to deliver can now quickly turn into production-ready features. At this point, I'd say more than 80% of the code was generated by the AI agent. The remaining 20% was written by me — mostly code review, bug fixes, and small refinements.
It honestly feels like a new era of building.
What amazes me
In the past, integrating libraries and tools into a project often required a large amount of manual effort. I had to carefully read documentation, resolve compatibility issues, and gradually stitch everything together. It was a slow and sometimes tedious process.
Today, AI agents can handle much of this work automatically. Instead of integrating each dependency step by step, I can simply provide high-level instructions (prompts) and let the agent orchestrate the implementation. Tasks that once required hours of manual setup can now be completed in minutes.
This becomes particularly valuable when working with UI component libraries such as Shadcn. When starting a new project with a standardized component library, things are usually straightforward. However, integrating such libraries into an existing codebase is often difficult. Legacy code tends to accumulate different patterns over time — components written in different styles, inconsistent structures, and duplicated implementations of similar functionality.
With AI, the process becomes significantly easier. Instead of manually rewriting everything, I can provide the AI with a reference component that represents the desired style or architecture. The AI can then apply that pattern across the existing implementation, automatically adapting old components to match the new standard.
The result is a much more consistent interface with far less manual adjustment. For example, on my blog, this approach allows me to maintain a consistent visual style across different pages while quickly integrating small interactive toy examples alongside written explanations.
Pegatron
This shift reminds me of the AI agent I built during my internship as AI Engineer at Pegatron in the summer of 2023.
At that time, ChatGPT had just been released, and the industry was still exploring how to unlock its potential. I was already experimenting with building AI agents to integrate into existing workflows.
Back then, the limitations were obvious. A single API call could take around three seconds, and once the agent began reasoning about which tools to use, even a simple prompt could take over a minute to produce a response.
Looking at today's tooling, the difference is remarkable. Latency has decreased dramatically, model capabilities have improved, and the ecosystem around AI-assisted development has matured.
Next Steps
After this practical experience, I'm excited to explore how AI agents can become an integral part of my development workflow.
Rather than replacing thoughtful engineering practices, I want AI agents to become tools that help reinforce them — assisting with refactoring, enforcing conventions, improving consistency, and accelerating development without sacrificing long-term quality.
In other words, the goal isn't just to build faster. It's to build better systems, with AI as a collaborator rather than a shortcut.