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.

Artificial Intelligence (AI) is revolutionizing software development and DevOps, offering capabilities that enhance productivity, streamline workflows, and foster innovation. Open-source solutions like Aider and Ollama exemplify how AI can seamlessly integrate into daily practices, enabling teams to deliver robust and efficient software solutions faster. This article explores the latest trends, tools, and future possibilities of AI in software development and DevOps.

Developing AI models for agglutinative languages like Turkish presents unique challenges. In this post, I’ll share my experiences and insights into creating a language-specific tokenizer and the impact it has on model performance. Understanding Agglutinative Languages Agglutinative languages, such as Turkish, Finnish, and Hungarian, build words by attaching affixes to a root in a highly systematic manner. A single word can encapsulate what might require multiple words or an entire phrase in non-agglutinative languages like English. For instance, the Turkish word “evlerimizde” translates to “in our houses,” combining a root (ev, “house”) with multiple affixes.