In the world of AI agents, even the most self-proclaimed "worst coder" can make strides. This is the story of how a coding novice took on the challenge of building an agent to crack a leaderboard—learning valuable lessons in programming, iteration, and the true meaning of 'agentic' along the way. Below are the key questions and answers from that journey.

What motivated you, a coding newbie, to build an AI agent for cracking a leaderboard?

As someone who jokingly calls themselves "The Worst Coder in the World," my motivation was simple: I wanted to see if I could actually make something functional. Leaderboards are everywhere—whether in gaming, work dashboards, or coding challenges—and I was tired of being near the bottom. I figured an AI agent could automate the repetitive tasks needed to climb the ranks. More importantly, I was curious about agentic AI (AI that takes autonomous actions) and wanted to understand how they work by building one from scratch. The project became a personal experiment: could a non-coder, with enough persistence and the right tools, create a tool that competes with more experienced developers? That question kept me going through every bug and broken script.

What were the biggest challenges you faced while building the agent?

The first challenge was architecting the agent itself. I had to decide between using existing frameworks (like LangChain or AutoGPT) or building from scratch. I chose a hybrid approach, which led to confusion about tool integrations. The second challenge was token limits—the agent would often lose context after long runs. I had to implement memory management, but my coding skills were rusty. Debugging was a nightmare: I'd spend hours fixing syntax errors only to realize the logic was flawed. The third challenge was leaderboard politics—some boards had anti-bot measures that required human-like delays. I had to add randomization and rate limiting, which felt ironic for a "worst coder." But each failure taught me a new lesson in Python, APIs, and agentic design.

How did you approach learning to code during this project?

I adopted a just-in-time learning strategy. Instead of taking a full course, I Googled specific problems as they arose. For instance, when I needed to parse HTML leaderboard data, I learned BeautifulSoup in two hours. When the agent needed to remember past actions, I studied vector databases like Chroma. I also kept a "mistake log"—a simple markdown file where I noted every error and its fix. Reviewing that log weekly turned bugs into lessons. I relied heavily on Stack Overflow and ChatGPT for code explanations, but I always made sure to understand why a solution worked before copying it. The biggest leap came when I started reading other people's open-source agents on GitHub—I learned by modifying their code to suit my needs.

What unexpected rewards did you discover along the way?

The most rewarding part wasn't cracking the leaderboard (though that felt great). It was understanding the iterative process of building AI. Each failed attempt taught me something about prompt engineering, API rate limits, or state management. I also discovered a supportive community of beginner AI builders on platforms like Discord and Reddit. Sharing my mistakes actually helped others avoid them. Another surprise was how creative I became: I built a small dashboard to visualize agent performance in real-time, which taught me data visualization. Finally, I gained confidence. The "worst coder" label faded as I realized that coding is a spectrum—everyone has gaps. The project proved that you don't need to be a genius to build something that works.

Can you walk us through the basic steps you took to create the agent?

1. Define the goal: I wanted the agent to submit optimized solutions to a coding challenge leaderboard automatically.
2. Choose the tech stack: Python, with the 'requests' library for HTTP, BeautifulSoup for HTML parsing, and OpenAI's API for the core decision-making.
3. Build the loop: The agent would fetch current leaderboard data, analyze which submissions were top-ranked, then generate and test new solutions in a sandbox environment.
4. Add memory: I used a simple JSON file to store past successful strategies and errors.
5. Handle errors: I wrapped each API call in try-except blocks and implemented exponential backoff for rate limits.
6. Iterate: I ran the agent overnight, then reviewed logs in the morning to tweak prompts and logic.

It wasn't elegant, but it worked—much to my surprise.

What advice would you give to other beginners considering building an AI agent?

First, start small. Don't aim to crack a leaderboard on day one. Build a simple agent that sends an email or scrapes a static page. Second, embrace failure—your agent will break, and that's okay. Every crash is a learning opportunity. Third, use existing tools. You don't have to build everything from scratch. Libraries like LangChain or frameworks like CrewAI can save you weeks. Fourth, document everything. Keep logs of inputs, outputs, and errors; they'll help you debug faster. Finally, share your journey. Post on forums, write about your mistakes—the feedback from other builders is invaluable. And remember: being a "worst coder" is a state of mind. With persistence and the right resources, you can build AI agents that actually work.

What are your next steps after this project?

I'm now exploring multi-agent systems—having multiple agents collaborate to tackle more complex problems. I also want to add a web interface so others can test my agent. But most importantly, I'm continuing to learn by building. My next project is an agent that helps me organize research papers. I'm also mentoring a few other "worst coders" in a small online group. The journey from newbie to someone who can build functional AI hasn't made me a expert, but it's shown me that agentic AI is accessible to anyone willing to iterate, fail, and learn. And who knows—maybe next time I'll aim for the top of the leaderboard without an agent's help.