I’m a Senior DevOps Engineer and AI Researcher with a deep passion for Free/Libre/Open Source Software and expertise in Cybersecurity who loves to solve problems using computers. With a Ph.D. in Computer Engineering, I specialize in Natural Language Processing (NLP), Machine Learning, and Deep Learning.
With multiple certifications, I combine cutting-edge AI research with practical DevOps and software solutions to build secure, scalable, and innovative systems. My expertise spans AI, cloud infrastructure, embedded and IoT systems, and high-performance computing.
I have extensive experience in various programming paradigms, including embedded development, scripting, web development, and high-level algorithmic design. My expertise also extends to architectural patterns, distributed systems, and scalable software design, enabling me to build efficient, resilient, and maintainable solutions.
Although Python is my preferred language, over the years, I have worked with a broad spectrum of programming languages, including: C/C++, C#, Go, Java, and Pascal. I am also particularly interested in Rust and WebAssembly as I believe that they represent the future of secure, high-performance, and portable computing.
Check out my resume for more details on my background and experience.
AI and Automation Is Quietly Dismantling the White-Collar World: I’ve spent years working at the intersection of automation, machine learning, and large-scale systems and recently there’s been a quiet shift happening in the background of our digital lives.
Dario Amodei, CEO of Anthropic, recently warned (*) that AI could wipe out up to 50% of all entry-level white-collar jobs within the next five years confirming what many in tech already see coming. And I also am here to confirm: This isn’t fearmongering. It’s what we’re building. But unlike the doomsayers, I believe this disruption is both inevitable and manageable—if we act strategically.
Deep learning is undergoing a pivotal transformation, driven by ever-larger foundation models, multimodal breakthroughs, and a vibrant open-source ecosystem that is reshaping the boundaries of what’s possible in AI.
I’ve been closely following the evolution of open-source AI, and it’s fascinating to see how quickly foundational models and tools are developing. What excites me the most is how these technologies are becoming more open, accessible, and collaborative. This article captures my reflections on these changes and how they might shape the future of AI.
For years, Docker was synonymous with containers, revolutionizing DevOps with portability and ease of use. But as cloud-native demands evolved, so did the ecosystem. Today, Docker remains a favorite for local development, while production increasingly embraces modular, lightweight alternatives. Here’s why modern infrastructure is moving beyond its monolithic design.
The Rise of Docker (and Containers)
Docker began in 2013 as an internal project at a PaaS company called dotCloud, founded by Solomon Hykes. The goal? Solve the headaches of environment consistency and application deployment.
