2025

We’ve all used ChatGPT to write a function or debug a regex. But that’s just the tip of the iceberg. The real power of Large Language Models (LLMs) isn’t in the “chat”; it’s in the integration. As I explored in my 2025 series on Jupyter and AI, the real value of these tools comes when they are part of a structured thinking process. By combining the interactive execution of Jupyter Notebooks with the reasoning power of Local LLMs, we can build something much more powerful: a Personal Knowledge Engine. ...

Every developer has been there: you inherit a codebase that looks like a bowl of spaghetti, and your first instinct is to say, “We need to rewrite this.” You see the outdated libraries, the inconsistent naming conventions, and the lack of unit tests, and you think, “I could do this so much better from scratch.” But a rewrite is rarely just a technical decision. It’s a social and ethical one. Legacy code is code that is working. It’s code that is paying the bills, processing the transactions, and serving the users. When we dismiss it as “trash,” we are dismissing the context, the constraints, and the hard work of the engineers who came before us. ...

When you open up a 1970s radio, you aren’t met with logs or stack traces. You’re met with voltages, currents, and signals. If the audio is distorted, you don’t “grep” for an error; you trace the signal path from the antenna to the speaker. Modern microservices aren’t that different, though we often forget it. We’ve traded copper wires for HTTP requests and vacuum tubes for Docker containers, but the fundamental challenge of observability remains the same: how do you understand what’s happening inside a complex, distributed system without tearing it apart? ...

Design systems are promised as the ultimate productivity booster. “Build once, use everywhere.” And for the first six months, it’s true. You see the velocity of your feature teams skyrocket as they stop arguing about hex codes and start assembling pages from a library of pre-built components. But as your team grows and your product evolves, the very system that was supposed to speed you up can start to slow you down. At work, we built a comprehensive Angular-based design system that initially reduced delivery time by 40%. However, as we scaled, we hit the “maintenance phase”: the point where the cost of consistency began to rival the cost of development itself. ...

Angular Signals have changed the way we think about reactivity in the frontend. But if you step outside the world of JavaScript, the concept of a “Signal” has a much older, much deeper history in Control Theory and Electrical Engineering. When we talk about “glitch-free” execution in Angular, we are actually talking about maintaining the integrity of a signal graph. I’ll explore the connection between the physics of signals and the architecture of modern web applications. ...

Full source Jupyter Blog Starter A complete working example for this article — explore the full source on GitHub. View source In the previous parts, we discussed why Jupyter is a “thinking environment.” In this final part, we’ll walk through four concrete scenarios where a notebook outperforms a traditional IDE for a senior engineer. 1. API Archaeology: Mapping the Unknown When you’re dealing with a complex API, you don’t want to build a full client just to see what the data looks like. ...

Full source Jupyter Blog Starter A complete working example for this article — explore the full source on GitHub. View source The Modern Jupyter Stack For a software engineer, the “standard” way of installing Jupyter (global pip install) is often the wrong way. It leads to dependency hell and “it works on my machine” syndrome. Here is a professional setup guide. ...

If you come from a traditional software engineering background (frontend, backend, systems), chances are you’ve seen Project Jupyter everywhere, from notebooks and extensions to cloud platforms, and thought: “This looks huge… but I don’t really see where I fit in.” I had the same confusion. Let’s look at it clearly, using roles, not buzzwords. First: What Jupyter Is Not Jupyter is not: A programming language (unlike R or Python) A replacement for IDEs like VS Code A production development environment A competitor to ChatGPT or Copilot If you try to use it as any of those, it will feel awkward. ...

Conditioned taste aversion, sometimes called the Garcia effect in honor of John Garcia who first described its unusual properties, is a powerful and fascinating form of learning. It is not only an interesting phenomenon in behavioral science, it also matters a great deal in medicine, nutrition, oncology, pediatrics, and everyday life. This long-form post explains what conditioned taste aversion is, how and why it happens, how to recognize it, the problems it can cause, ways to prevent and manage it, how it differs from allergies and other food-related issues, and where to look for more detailed scientific and clinical information. ...