What is going on with AI these days? That's what I'm trying to figure out, and this blog tracks that effort. I'm taking complex AI subjects, pulling them apart, and aiming for explanations that click, especially if you're looking at AI from a business or practical IT angle rather than deep research.

  • 7 min read

Developing a Three-Week AI Curriculum as a Personal Side Project

About half a year ago I launched dentro.de/ai with a simple idea in mind: provide clear, simplified explanations of modern AI in one place: "Inside AI".

In the background there was always a bigger plan. I wanted all those loose pieces to eventually line up as a complete scaleable learning path. Not just a glossary here, a visualizer there, a blog post over there, but something you could actually follow from start to finish.

That missing piece is finally here: a 3 Week Learning Curriculum that ties everything together and walks you through the AI Black Box from the outside in.

This post is a bit of a behind the scenes story of how and why I built it, what ended up in the course, and how you might use it, whether for yourself or to teach others.

Developing a Three-Week AI Curriculum as a Personal Side Project

  • 4 min read

Analogy to understand Open Source in generative AI Models

We often hear the terms "Open Source" and "Open Weights" in the world of AI models - but what is the difference? Traditional software uses open source for full transparency and reproducibility. AI is not like traditional software, because the "brain" - our fully trained model - is a compressed representation of the huge amount of training data.

The good news is we can use our Mental Model BEFORE → INSIDE → AFTER the AI Black Box again - but this time to explain Open Source vs. Open Weights in AI:

BLACK BOX

Let's dive deeper and explain Open Source vs Open Weights in the context of AI models - with the help a car analogy ( again!) for clarity and to help making informed decisions. For terms like Parameters (the Model Weights in "Open Weights") or Training, see the AI Glossary.

  • 7 min read

How Large Language Models like ChatGPT Work

You're likely interacting with AI (Artificial Intelligence) more and more, perhaps using tools like ChatGPT for drafting emails, summarizing reports, or even brainstorming ideas. These Large Language Models (LLMs) have become remarkably capable, but how do they actually work? What's happening behind the screen when you type a prompt and receive a well informed response?

This post aims to provide a high-level, conceptual understanding of the core mechanics behind LLMs. It's designed for the tech-interested who want to grasp the fundamentals without needing a deep dive into complex mathematics or code. Think of it as looking under the hood to see the main components, not rebuilding the engine. We'll explore how these models process language, how they "learn," and what's actually happening during that near-instantaneous generation of text.

Prompt to Text

  • 4 min read

AI Model Lifecycle - Analogy Explained with Cars

The process of creating, deploying, and using Large Language Models (LLMs) involves numerous intricate steps. Given the complexity in AI development, drawing parallels to more familiar industrial processes can aid understanding. This guide presents an analogy, comparing each stage of the LLM lifecycle to the established process of designing, building, distributing, and using a car.

LLM vs Car

While AI models and automobiles are fundamentally different, examining their lifecycles side-by-side offers insights into the structure and challenges of LLM development. A key contrast arises during the core "production" phase: car manufacturing centers on the physical assembly of components, whereas LLM creation hinges on a vast computational training process to embed knowledge from data.