I am less of a traditional engineer defined by one language or framework and more of an AI-native builder who starts with the problem, learns whatever is necessary, connects the right tools, and keeps iterating until the system works.
My process is simple:
Define the goal → Research → Benchmark → Design → Build → Debug → Retry → Validate the entire system
AI has dramatically accelerated that process, but the way I work existed long before modern AI.
I Grew Up With Computers
I first used an Intel 386 when I was seven years old. I spent about a year learning DOS and BASIC at a computer academy, roughly two hours a day.
At nine, I received my first personal computer, an old monochrome laptop from my uncle. At twelve, I built my first desktop myself with guidance from the person who was assembling PCs for us.
Since then, solving computer problems on my own has simply been part of my life.
My First Automation Project
In 2005, at age 20, I volunteered to serve in the Republic of Korea Navy.
My primary specialty involved observing and forecasting weather and oceanographic information, and I also spent approximately one year working in military intelligence. A significant portion of my weather and oceanographic duties consisted of repeatedly collecting, organizing, and printing operational weather data.
I kept thinking:
Why should a person spend hours doing the same thing every day?
I wanted to automate it.
The problem was that I did not yet know enough programming to build the application from scratch, and because it involved military work, outside assistance was limited.
So I found another way.
I used Excel's macro recorder to capture individual tasks, opened the generated code, studied how each action translated into code, and began recombining those pieces.
When something failed, I recorded it again, compared the code, modified it, and tested it again.
After several months, I completed a program that automatically collected, organized, and prepared the weather information for output.
It remained in operational use until I left the Navy and was later used at the Republic of Korea Fleet Command as well.
That experience shaped the way I still build today.
I did not master programming first and then look for something to build.
I defined the result first and learned the technology required to reach it.
Technology Never Stopped Being Part of My Life
After four and a half years of military service, I moved to the United States.
I first studied ESL, then completed studies in Computer Specialist and Network Specialist, building a more formal foundation in hardware, software, and networking.
My professional career eventually moved in a different direction.
For more than a decade, I worked in restaurant operations, progressing from management into ownership and responsibility for the broader business.
But I never stopped building and experimenting with technology.
Over the years, I have worked with cryptocurrency mining systems, Linux environments, storage-based mining systems using dozens of hard drives, NAS infrastructure, home labs, n8n automation, Stable Diffusion, PC building and repair, Android rooting, iOS jailbreaking, networking, servers, design tools, video and image workflows, and many other technologies.
If I need a tool and do not know it yet, I learn it.
Operating a Business Made Me a Better Builder
Running a restaurant also taught me to think in systems.
When something broke, I rarely cared whether it technically belonged to IT, electrical work, operations, payments, or another category.
I cared about getting it working again.
I handled problems involving POS systems, networking, computers, payment systems, delivery platforms, equipment installation, electrical devices, and other parts of day-to-day operations.
A restaurant is a complex system involving people, customers, revenue, costs, equipment, suppliers, software, and operational processes.
More than a decade in that environment taught me something that pure technical work often does not:
technology only matters when it works inside a real business.
AI Changed the Scale of What I Could Build
I began using GPT early and became a heavy user of both GPT and Claude.
From the beginning, I viewed these systems as more than question-answering tools.
I used them to build.
When Claude introduced Model Context Protocol (MCP), I began experimenting with it early and connected AI directly to my local files, development environment, data, and tools.
That changed my understanding of what an AI development environment could be.
Instead of interacting with AI only through a browser, I could create a workflow where AI could inspect files, write code, run programs, review results, and iterate.
My PC itself began becoming an AI-assisted workspace.
This also led to another important idea:
the development process itself can become a compounding asset.
If code, documentation, tooling, experiments, failures, solutions, and architectural decisions are structured and preserved, the next project does not start from zero.
Previous work becomes context.
Solved problems become reusable knowledge.
Each project can make the next one faster.
How I Think About AI Development
AI has lowered the barrier to building software more than any technology I have experienced.
But I do not believe the value comes from simply asking AI to write code.
The important skills are increasingly:
understanding what should be built, designing the system, breaking work into the right pieces, finding the necessary information, connecting tools, validating outputs, identifying failures, and taking responsibility for the final result.
That is where I am strongest.
I am not someone who claims to have every syntax rule or framework memorized.
My strength is being able to understand the objective, rapidly acquire what I need, connect multiple technologies, and turn an idea into a functioning system.
I would describe myself as an AI-native builder with strong systems thinking, hands-on technical range, and real-world business operating experience.
Why I Am Looking for a Team
AI has greatly expanded what one person can build.
But there is still a limit to how much impact one person can create alone.
I can take ideas surprisingly far by myself.
What I want next is to work with strong people who can take those ideas further: building better products, reaching real users, learning from the market, scaling what works, and solving larger problems.
I am looking for a company where I can combine:
systems thinking, rapid technical learning, hands-on problem solving, business experience, and AI-accelerated execution.
I want to see how far those capabilities can go when they are applied inside the right team.