I’m absolutely thrilled to share some significant updates to my website! It’s been a labor of love, and I believe these additions will make the site even more valuable for anyone interested in robotics, computer vision, and embedded systems. My goal has always been to share my journey and the lessons I’ve learned, and these new sections are a big step in that direction.
First off, I’ve dedicated a considerable amount of time to creating detailed project pages for the MyzharBot v1, v2, and v3. For those unfamiliar, MyzharBot is my intelligent tracked mobile robot, which has served as my primary development and experimentation platform for years. These pages aren’t just technical specifications; they document the entire evolution of the MyzharBot series, from initial concepts and design choices to the features I implemented and, crucially, the challenges I encountered along the way.
I’ve poured a lot of personal experience into these write-ups, aiming to provide genuine insights into the development process of a complex robotic system. You’ll find information on the various technologies involved, the hardware iterations, and the software stacks that brought each version to life. My hope is that by sharing this journey, others can learn from my successes and, perhaps more importantly, my missteps. It’s a testament to how much a project can grow and change, and how continuous iteration is key in robotics. You can dive into the full story and explore the evolution of MyzharBot right here: Read more about MyzharBot.
But that’s not all! I’ve also brought back a highly requested and, in my opinion, absolutely essential resource: a simple yet comprehensive CUDA™ tutorial that explains CUDA™ Compute Capability. This tutorial is a direct replication and update of a very popular post from my old WordPress blog. I decided to port it over because I consistently receive questions about GPU programming, especially concerning how to optimize performance and ensure compatibility across different NVIDIA GPUs.
Understanding CUDA™ Compute Capability is fundamental for anyone serious about leveraging the power of GPUs in applications like computer vision, deep learning, and, of course, robotics. It helps you grasp why certain features are available on some GPUs but not others, and how to write code that can adapt or take full advantage of the underlying hardware. Choosing the right configuration and understanding these nuances can make a significant difference in the efficiency and speed of your CUDA™ applications. I’ve personally found this knowledge invaluable in my work with NVIDIA Jetson platforms and other GPU-accelerated systems. By making this tutorial easily accessible again, I aim to provide a solid foundation for developers looking to unlock the full potential of their GPU hardware. Check out the tutorial here.
These updates reflect my ongoing commitment to building a valuable resource for the robotics and software engineering community. I believe in open knowledge sharing, and I hope these new additions serve as useful tools and inspirations for your own projects.
Stay tuned for more updates and tutorials in the future!
