Building in public means sharing your journey, not just the final result. When you share what you're learning, building, failing at, and improving: 🧠 You learn by explaining 🤝 You attract people with similar interests 💡 You get feedback earlier 🌍 You build your network & reputation 🚀 You create opportunities you didn't expect You don't need to be an expert. Share the process. Share the mistakes. Share the progress. Someone might learn from you, someone might help you, and someone might even want to build with you. 🌱 Don't wait until you're successful to share. Share the journey that gets you there.

It has been a while since I made a post here. During this time, I explored quite a few Computer Science courses, curricula, and learning paths. I looked at what they do well, where they fall short, and, more importantly, what I felt was missing. And I kept coming back to one question: What if we didn't just learn how to USE today's technology... What if we actually understood the JOURNEY that created it? That question led us to something new. 🚀 Welcome to “The Complete Understanding of Computer Science — From Abacus to AI” This is not going to be another course where we rush through Python, learn a few frameworks, build a couple of projects, and immediately ask: “How can I make money with this?” There is absolutely nothing wrong with learning those things. But I believe there is a much deeper path. We are going back to the beginning. From numbers and the Abacus... → Mathematical Thinking → Logic → Algorithms → Information → Computation → Programming → Data Structures → Computer Architecture → Operating Systems → Computer Networks → Databases → Software Engineering → The Internet → Artificial Intelligence → Machine Learning → Deep Learning → Generative AI → Intelligent Agents → And beyond... But here is the important part: We won't simply study WHAT was built. We will try to understand WHY it had to be built. Why did humans need algorithms? Why weren't numbers enough? Why did we need abstraction? Why did programming languages emerge? Why did operating systems become necessary? Why did databases appear? Why did computer networks become essential? Why wasn't traditional programming enough? Why did Machine Learning become necessary? Why did neural networks return? Why did Deep Learning change AI? And finally... Why have we arrived at today's era of Generative AI and increasingly capable intelligent systems? This changes the entire way we learn. Instead of memorizing technologies, we begin to understand the PROBLEMS that create technologies. And I believe that is one of the most important abilities a Computer Scientist can develop.