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AI Learning for All Your Free Starter Kit to master Artificial Intelligence
Welcome inside the AI Black Box. It's brighter in here than you'd think. Dentro de AI translates to Inside AI, which is exactly what we do here - without drowning in code or calculus.
Hi, I’m a Senior IT Business Analyst who’s spent 30 years deep in tech - and discovered that explaining things simply is the hardest (and most rewarding) part. So I built this space to unpack Generative Artificial Intelligence in plain English - with analogies, visuals, and a dash of humor.
Over the past year, this site has become a growing library of insights designed for non-IT folks who just want AI to make sense. The star of the site is the How to Learn AI in 3 Weeks course that ties most of the site's content into a Learning Curriculum aimed at non-techies - it mirrors my own learning. Oh, and meet Dentroid - our slightly quirky mascot who helps turn abstract AI ideas into visuals that might or might not fit.
How to Learn AI in 3 Weeks
This is the complete Learning Curriculum that onboards you within only 3 weeks to the basic AI fundamentals:
- What is happening inside the hidden AI Black Box?
- How can I use that knowledge to better interact with AI models?
- How come that AI is sometimes genius, but other times dumb? And why?
- Token, inference, context - what is that all about?
See the course structure here:
Most of the content of this site has been woven into this coherent curriculum - it introduces the Mental Model: BEFORE → INSIDE → AFTER the AI Black Box, which assists nicely to explain how AI models work. Jump right to the course or read how it came about.
You find all site content via the navigation, but let me lay it out here too:
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AI Adoption Is a Trap
- Optimization cements you into structures that won't survive, consultants profit from selling adoption not warnings, and the execs skill gap makes it hard to see the trap at all.
AI Adoption vs AI Transformation
- Why bolting AI onto legacy structures fails - and what touring car racing teaches us about genuine transformation.
- Learn from DTM to build AI-native organizations.
The Great Squeeze - Understanding LLM Information Density
- How AI models distill trillions of words into a few billion parameters.
- Why this "lossy" process is actually a feature, not a bug.
Developing 3-Week AI Curriculum
- All modules at dentro.de/ai are now unified into one streamlined AI learning experience.
- Behind the scenes story of building the Learning Curriculum.
Understanding Open Source in AI Models
- Many models claim to be Open Source, while in reality they are just Open Weights - and why that is not enough.
How AI Large Language Models (LLM) Work
- What happens when you prompt ChatGPT?
- Explained in plain non-geek English.
AI Model Lifecycle - from Creation to Inference - Explained with Car Analogy
- The 9 steps of LLM lifecycle - from architecture design to user inference.
- Explained with an analogy from the industry: Automotive Manufacturing.
The Big AI Players - the Who Is Who of the Industry
- Who forms the industrial AI landscape?
- Introduction and background - from public companies to research institutes.
AI Visualizations
- Interactive Websites and Tools - plus Videos to explain AI.
- From single Token to high level architecture of deep learning systems.
AI Timeline
- How did we get here? Including entries you would not necessarily expect.
- All big moments and events - from Research Papers to Pop Culture.
AI Glossary
- AI/ML has a very special vocabulary - this is the place to get familiar with it.
- With Hundreds of Foundational Terms and their plain-English Explanation ... plus a Quiz!
AI News
- A curated roundup of AI's fast lane - crafted with a model's help.
- Spotlighting the biggest launches, research milestones, and what's making waves in the industry.
About
- Background on Dentro de AI Project and Author.
Future Content Ideas
Now that the Learning Curriculum is complete I'm aiming to dive deeper:
- AI Installation Use Cases for real world Business Solutions.
- Explainable AI / Interpretable AI / Mechanistic Interpretability
- AI Transformation: Winning Trust from the Legacy Workforce
- AI Agents Explained: From Chatbot to Autonomous Workflow
- The Price Tag of AI: Understanding Tokens, Compute, and Architecture Choices
We've Been Here Before
I was there when the first PCs arrived in our offices. I was there for the dot-com era. Looking back, those were fascinating times - we were live witnesses to technological shifts that changed everything. And now, here we are again.
This one feels bigger. AI is moving faster than anything before, and the stakes are higher. But that also makes it incredibly exciting. We get to be live witnesses once more - not just watching from the sidelines, but actually understanding what's happening as it unfolds.
That's what this site is about. Decision-makers don't need to become ML engineers, but knowing what's behind the curtain - what AI does well, where it stumbles, and why - makes all the difference. Challenging times, yes. But also genuinely interesting ones.
Pro tip: Press F anytime to search the site for any AI/ML term.
Let's Connect
I'm always happy to exchange ideas with like-minded people - whether at AI meetups in Cologne, on Twitter, or wherever the conversation happens.
What I find most interesting right now: AI transformation. What does it actually mean for companies? How is AI being implemented across industries? And the big question - what does a fully AI-driven organization even look like? Are we ready to build it, or are we still waiting for better tools? These strategic discussions are what I enjoy most.
If you're from Ford and stumbled across this site - say hello! I've run internal workshops and AI introductions before and always enjoyed it.
And if you want to use this site's content for your own learning or training - go ahead. The curriculum, analogies, and visualizations are here to be useful. Whether you need a 1-hour intro or a week-long deep dive, the building blocks are ready.
