I like projects where something is actually broken or missing — not the ones where the answer's already sitting in a tutorial three tabs over. Backend design, database schemas, the stretch of debugging right before something finally runs — that's the part that hooks me. I'd rather spend a weekend figuring out why a query is slow than follow a guide that tells me the fix upfront.
Lately I've been pulled toward ML and data science, but not in an import sklearn and move-on way. I want to know what's happening under the model before I trust what it outputs, which means sitting with the math and statistics most people are content to skip past. It takes longer. It's also the only way I end up believing the number at the end.
Outside of that: 400+ problems on LeetCode and GeeksforGeeks, mostly in C++. The count was never the goal — it's the speed at which I can look at a new problem and see its shape that I'm actually tracking.
If something in one of these repos is useful to you, dig in.
- Building — projects that force me to learn something the hard way
- Learning — Machine Learning · Data Science · Statistics (properly, not just the API)
- Sharpening — DSA in C++ · complexity analysis · graph problems
- Goal — a DSA foundation solid enough that everything else can sit on it
Comfortable: C++ · Python · JavaScript · Node · Express · MySQL · MongoDB · Git
Learning: ML fundamentals · Statistics · Data Science
Mostly DSA: data structures, algorithms, DP, graphs, greedy, binary search, complexity analysis. It's the thing I come back to when I want to get sharper, not just log more hours.
DSA & C++ ──▶ Math & Statistics ──▶ ML Fundamentals ──▶ Data Science
│ │ │ │
400+ problems probability, building from real datasets,
graphs, DP linear algebra scratch first honest results
Roughly the order I'm working through it, in public, mistakes included. DSA is the foundation everything else sits on — I'm not moving past it until it's actually solid.
If you've read this far, we should probably just talk.
