This is a submission for the GitHub Copilot CLI Challenge
What I Built
I built MathPilot, a command-line tool designed to transform complex research papers into runnable code. As someone coming from a machine learning background, I know firsthand how intimidating it can be to implement algorithms directly from papers. Understanding the logic is one thing, but translating it into code that actually runs is often a barrier for students and researchers alike.
MathPilot aims to bridge that gap. It reads a research paper, helps scaffold the algorithm, and generates executable code that you can run immediately. For future iterations, I plan to integrate functionality that pushes the code directly to Google Colab, allowing resource-intensive algorithms to run on the cloud seamlessly. This means that even very complex ML models can be tested and iterated without worrying about local compute limitations.
Demo
My Experience with GitHub Copilot CLI
GitHub Copilot CLI was a game-changer for this project. It helped me:
Plan the architecture of MathPilot and break down the research-to-code pipeline.
Scaffold algorithms from complex papers quickly, giving me a runnable base to work from.
Write tests to validate that generated code works as expected.
Identify and fix bugs automatically during development.
Deploy the project to GitHub and manage commits efficiently.
While I made small tweaks to the generated algorithms for accuracy, Copilot handled the heavy lifting in generating the initial code and structuring the workflow. This made development much faster and allowed me to focus on refining the tool rather than getting stuck on boilerplate or low-level implementation details. Below are screenshots of Copilot-cli in action.
Credits to Rennagade
MathPilot started as a tool to solve a problem I personally faced: turning dense research papers into something tangible and runnable. As a student, I’ve always believed that understanding an idea truly begins when you can implement it. This project is my attempt to lower that barrier to make complex research more accessible, testable, and alive.
P.S we hit a rate limit during the video demo. But we just had to push it that way.





Top comments (0)