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.

Another reason for writing the book is that many developers see "algorithms" as something only needed for FAANG interviews, not for real work. For beginners and even seniors, learning algorithms often just means doing LeetCode problems, which most people dislike but feel forced to do.

I want to change that view and show that algorithms are beautiful and useful beyond interviews. They appear everywhere, from compilers to databases to the Linux kernel, where I found many interesting data structures worth exploring. (i will share more about this topic later)

I hope to share more of these insights and connect with others who enjoy discussing real world algorithm design, which is what I love most about the Hacker News community (except for the occasional trolls that show up from time to time).

For more context, I actually used The Algorithms as a reference when working on my own programming language, Mochi, which includes around 150–300 algorithms (I don't remember exactly) implemented directly in Mochi. These are then transpiled to over 25 programming languages such as C, Haskell, Java, Go, Scala, and more: https://github.com/mochilang/mochi/tree/main/tests/algorithm...

The VM and transpiler were originally implemented by hand, and later I used Codex to help polish the code. The generated output works, though it is a bit messy in places. Hopefully, after finishing a few books, I can return to the project with more experience and add better use cases for it.

Thanks! I completely agree. For more than ten years consulting, training and architecting systems for clients across government and enterprise, I have seen the same pattern. Long before "big data", "cloud" and now with "AI" and "GenAI" these buzzwords have often been misunderstood by most of the C-suite. In my entire career, explaining the basics and setting the right expectations has always been the hardest part.

I really like your idea of targeting executives and connecting it to real business outcomes. Getting decision makers to truly understand the fundamentals behind the technology would make a huge difference.

I hope the next generation learns to love "C" and Algorithms again. I have rediscovered my appreciation for C recently, even though Go is my main professional programming language.

My biggest inspiration for this project, though, is The Art of Computer Programming (TAOCP), that level of depth and precision is the ultimate goal. I'm also planning to include formal proofs of all algorithms in Lean, though that could easily turn into a 10-year project.

Sedgewick's Algorithms book is great for practical learning but too tied to Java and implementation details. It is a bit shallow on theory, though the community and resources for other languages help.

That said, I personally prefer Introduction to Algorithms (CLRS) for its formal rigor and clear proofs, and Grokking Algorithms for building intuition.

The broader goal of this project is to build a well tested, reference quality set of implementations in C, Python, and Go. That is the next milestone.

For visualization, I'm considering using the awesome Manim library by 3Blue1Brown (https://3b1b.github.io/manim/getting_started/quickstart.html). I'm not very good at visual design, but let see what I can come up with.

To reduce the current monomorphism, I might add a generic version using void* and a comparator, or generate code for a few key types, while keeping the simple monomorphic listings for readability. (Though this would make the code a bit more complex)