I’ve always wondered: what actually makes an email urgent? Is it the length? The words? The tone? So I built TriageAI - a model that classifies incoming emails into priority levels (Urgent, High, Medium, Low) to figure out what deserves attention right now.

I use PyTorch every day at work. It’s incredible - but I’ve always wondered: how does it actually work under the hood? How does backward() actually compute gradients through a neural network?

So I built my own minimal deep learning framework called Synap. It’s written in C++ for performance, with Python bindings via pybind11. No external ML libraries - just raw tensor operations and automatic differentiation from scratch.

Ever wanted to quickly understand what a GitHub repository looks like without cloning and exploring it yourself? I built a tool that does exactly that - paste any GitHub URL, and it analyzes the entire codebase, extracting complexity metrics, code structure, and language distribution.

The best part? It combines Go and Python in a way that showcases how to pick the right tool for each job.