Technical

AI agents are becoming harder to ignore. Every major platform seems to be moving in the same direction. ChatGPT, Claude, Gemini, and other AI systems are no longer being discussed only as tools that answer questions. They are increasingly being discussed as systems that can plan, act, call tools, complete workflows, and take on more of the work directly. For analysts, that creates two reactions at the same time. One is excitement, because better AI tools can clearly speed up

A lot of people talk about SQL, Python, and Power BI as if they are competing for the same place in analytics. They compare them, rank them, and ask which one matters most, as if choosing the “best” one will settle the bigger question. I think that framing causes more confusion than clarity. Most of the confusion is not really about tools. It is about workflow. People use the word analytics as if it describes one skill, when in practice it hides several different jobs. Pulli

Claude has been getting a lot of attention lately for how powerful it is. After using Claude Pro for a week, I understand why. The model is strong. But I think the more important question begins where most of the praise stops. Not how impressive the model feels. How long that usefulness actually holds once your workflow starts to depend on it. That was the shift for me. I was not using Claude for coding or technical builds. I was using it to stress test workflows, challenge

A lot of people new to analytics assume they need the full industry stack from day one. I do not think that is always true. When I first built my portfolio site, Wix Studio free was enough to help me build something real and start paying attention to how people moved through it. Wix supports published free sites, multi-page site structures, and CMS-backed content, which made it possible to build something with enough depth to actually observe. At that stage, I did not need a

One thing Power BI made much clearer to me is that a dashboard can look polished, interactive, and easy to use while still being built on weak logic underneath. That matters because dashboards are often the part people notice first. They see the charts, the slicers, the interactivity, the clean layout, and the speed at which they can move through the report. A good dashboard can absolutely make analysis easier to explore and understand. But that usefulness depends on somethin

Before spending money on agents, automation tools, or complex AI workflows, it may be worth fixing something more basic first: the way you prompt. The more I use AI tools like ChatGPT and Gemini, the more I notice the same pattern. Many people already use them every day, but still at the most surface level. They open the tool, type a request, read the answer, and judge the result from there. If the output feels weak, the conclusion often comes quickly: maybe the model is not