In 2026, generative AI tools like ChatGPT, Copilot, and DeepSeek ( and more) are known by almost everyone. These tools can make you faster and more efficient. In project management, you can accomplish a lot with them. However, you'll only benefit if you use them correctly. Otherwise, you'll come across as unprofessional and amateurish.

Here are the 7 mistakes that instantly reduce trust in you (and how to avoid them). 

If you look closely at the current wave of generative AI, a sobering reality sets in: almost everything we call "artificial intelligence" today is actually just looking backward.

When we ask an AI to write a line of code, draft a strategy, or predict market trends, we aren't asking it to think. We are asking it to look at a mountain of historical data and make an educated guess.

To understand where the technology is actually going, we have to look at how AI processes time. The evolution of AI isn't just about getting "smarter"—it’s about moving from a system that remembers the past to a system that can simulate the future.

Here is the three-part framework defining the past, present, and frontier of AI.

Productivity is a topic that is being very important to me. We all have the same 24 hours. The goal has always been how can I get the best use of my time. However my life got drastically changed overtimes.

There is no doubt that Data Scientist is one of the coolest professions. Building machine learning models should be one of the coolest things in the tech world. However, This is not the only thing a data scientist does though.

In today’s world of software development, being agile and scalable is not just important, it’s essential for success. In this context, Kubernetes is a technology that stands out as a game-changer

In this blog, I share my two cents about how one can be more valuable as a data scientist.