Those algorithms implement so random to me, with lack of explanation, no test cases, no formal proof, and often inconsistent naming or structure across languages. Many repositories like TheAlgorithms are great collections, but they feel more like code dumps than true learning resources. You can find an implementation of Dijkstra or QuickSort, but often there is no context: why it works, how to prove it correct, what the complexity is, or how to test it against edge cases. For someone who wants to learn algorithms deeply, that missing layer of reasoning and validation is critical.

No organization for learners either. It jumps straight into implementations without a logical flow from fundamentals. I want to build something more structured: start from the very foundation (like data structures, recursion, and complexity analysis), then move to classical algorithms (search, sort, graph, dynamic programming), and eventually extend to database internals, optimization, and even machine learning or AI algorithms. Basically, a single consistent roadmap from beginner to researcher level, where every algorithm connects to the next and builds intuition step by step.

Another very good resource for beginners is https://www.hello-algo.com. At first, i actually wanted to contribute there, since it explains algorithms visually and in simple language. But it mostly covers the basics and stops before more advanced or applied topics. I want to go deeper and treat algorithms as both code and theory, with mathematical rigor and formal proofs where possible. That is something I really liked about Introduction to Algorithms (CLRS) and of course The Art of Computer Programming (TAOCP) by Knuth. They combine reasoning, math, and practice. My goal is to make something in that spirit, but more practical and modern, bridging the gap between academic books and messy open source repos.