Mohammed Ghaban: Building, Learning, and Creating in AI
I’m Mohammed Ghaban, a Yemeni developer working on open-source projects across AI agents, generative AI, machine learning, AI evaluation, and AI security.
My journey as a developer has always been driven by curiosity.
Since I was young, I’ve enjoyed learning, experimenting, and trying to build things just to see if I could make an idea work. There is something special about creating something from nothing and then seeing it actually work in front of you.
That feeling continues to motivate me today.
From curiosity to AI
My interest in AI grew naturally from my interest in technology and programming.
I became particularly interested in AI agents and systems that can do more than simply generate text. I wanted to explore systems that can use tools, write code, execute actions, solve problems, and be evaluated based on what they actually accomplish.
This led me to explore generative AI, machine learning, AI evaluation, benchmarking, and AI security.
I also began sharing projects and experiments through open source and platforms such as GitHub, Kaggle, and Google Skills.
Building CodeBots Arena
One of the projects that represents this journey is CodeBots Arena.
The idea started with Kaggle.
I was interested in AI competitions and began thinking about what an AI-vs-AI programming competition could look like.
Instead of only participating in competitions, I asked myself:
Why not build one?
That question became CodeBots Arena.
CodeBots Arena is an open-source AI-vs-AI coding arena where AI agents receive programming challenges, generate code, execute their solutions in a controlled environment, and compete based on measurable results.
The project brings together several areas that I enjoy:
- AI agents
- Generative AI
- Programming
- Automated evaluation
- Sandboxed execution
- ELO rankings
- Open-source development
The central idea is simple:
AI coding agents shouldn’t only generate code — they should prove that their code works.
What building the project taught me
Building CodeBots Arena has taught me that generated code can look convincing without actually being correct.
An AI agent can produce a solution that seems reasonable but fails because of an edge case, an incorrect assumption, or a subtle implementation problem.
Running and evaluating the generated code changes the question.
Instead of asking only whether an answer looks good, we can ask whether the solution actually works.
That experience strengthened my interest in AI evaluation and benchmarking.
Learning from real engineering problems
The project has also involved ordinary software engineering challenges.
For example, I encountered a PYTHONPATH issue where tests passed locally but failed in CI.
It was a useful reminder that software is not finished simply because it works on one machine.
Reproducibility, testing, environment configuration, and CI are all important parts of building reliable software.
These experiences have shaped the way I approach development.
Continuing despite limited resources
My journey has also involved some practical challenges.
At one point, my computer stopped working, and because of my circumstances and the situation in Yemen, I wasn’t able to replace it.
I continued working through my iPhone, browser-based tools, and free resources.
It was difficult, especially when working on projects that normally require a full development environment.
But I decided to keep going.
That experience taught me to make the most of whatever resources are available and to adapt when circumstances are not ideal.
Progress may sometimes be slower, but it is still progress.
AI evaluation and benchmarking
Beyond CodeBots Arena, I’ve become increasingly interested in evaluating AI systems.
I’ve worked on projects related to AI hallucination benchmarking and other experiments designed to measure how AI systems behave.
As AI becomes more capable, I believe evaluation becomes increasingly important.
A system can sound intelligent while still producing incorrect results.
The same applies to AI-generated code.
We need ways to measure what systems actually accomplish, where they fail, and why.
Learning through Google and Kaggle
I have continued developing my skills through platforms such as Google Skills and Kaggle.
The badges, projects, experiments, and competitions are useful milestones, but for me they are not the final goal.
The real goal is learning enough to build something with what I’ve learned.
Whenever I discover a new technology, I want to experiment with it and understand what I can actually do with it.
Looking ahead
I want to continue working on AI agents, generative AI, machine learning, AI evaluation, and AI security.
I also want to continue developing CodeBots Arena and explore better ways to evaluate AI coding agents.
My long-term goal is to build useful open-source projects and contribute to the wider developer and AI community.
There is still a lot I want to learn.
Keep building
My journey has taught me that you don’t need perfect circumstances to start.
You can start with what you have.
You can learn through free resources.
You can build small projects.
You can fail, debug, and try again.
For me, curiosity has always been one of the strongest motivations to keep learning.
When an idea becomes a working project, even a small one, there is a feeling of achievement that makes the next challenge worth taking on.
I don’t know exactly where my journey will lead.
But I want to keep learning, experimenting, building, and contributing.
And I hope that the projects I’m building today will eventually become something meaningful.
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Read my Developer Story on CoderLegion
I recently shared a longer version of my developer journey and the story behind CodeBots Arena through CoderLegion:
CodeBots Arena — Open Source:
https://github.com/Mhmda1998/CodeBots-Arena
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I’m Mohammed Ghaban, a Yemeni developer working on open-source projects in AI agents, generative AI, machine learning, AI evaluation, and AI security.
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