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Part II: From Models to Systems: Building Real AI Infrastructure

How streaming, feedback, and governance turn algorithms into intelligence. In Part I, we established that a Large Language Model is not Artificial Intelligence. LLMs generate text but AI systems generate outcomes. Now we’ll look at what makes those systems real: data flow, feedback, and accountability. The Lifecycle of Real Intelligence A genuine AI implementation is […]

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The „LLM = AI“ Myth

Algorithm / Dominique Ronde

Why equating generative models with intelligence is technically wrong — and dangerous. At some point in the last two years, the term Artificial Intelligence stopped meaning what engineers and scientists meant by it.It became shorthand for anything that calls an OpenAI API or produces text that sounds clever. But a system that completes your sentences

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Why I’m the BigData Pilot

AI/ML / Dominique Ronde

People often ask me why I call myself the BigData Pilot.It started as a metaphor but over time it became my way of working, thinking and leading projects in the world of data and AI. Checklists over ego In aviation, even a captain with 30,000 flight hours still uses a checklist. Not because they don’t

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AI Ethics 101: The 3 Big Questions

AI/ML / Dominique Ronde

Artificial Intelligence isn’t just a technical breakthrough—it’s a societal one.From hiring to healthcare to content creation, AI systems are making decisions that affect lives, reputations, and livelihoods. That makes ethics not an afterthought—but a requirement. And while the field of AI ethics can get complicated fast, most of it boils down to three core questions:

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Do You Need to Be a Programmer to Work in AI?

AI/ML / Dominique Ronde

Let’s settle something upfront:No—you don’t need to be a programmer to work in AI.But you do need to understand what you’re doing, why it matters, and where your limits are. AI isn’t a gated community for coders anymore. Today’s ecosystem is broad, fast-moving, and full of entry points for people with different skills. You can

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AI in 2030: 5 Predictions

AI/ML, Use Cases / Dominique Ronde

If the last decade was about proving that AI works, the next will be about proving it can work responsibly, sustainably, and at scale. We’ve seen the prototypes. We’ve watched the demos. Now comes the harder part: integrating AI into daily life, critical systems, and economic structures without losing control—or our common sense. So let’s

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5 Real AI Applications Already Improving Your Life

AI/ML, Use Cases / Dominique Ronde

AI often gets sold like a movie trailer: robots, revolutions, and the promise of sentient assistants that’ll someday file your taxes and raise your kids. But most real AI? It’s quieter. Less dramatic. And far more useful. If you think AI is all buzzwords and beta-stage experiments, you’re probably overlooking the systems already working behind

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How Machine Learning Works

AI/ML, Algorithm / Dominique Ronde

You’ve heard the buzz. AI is changing everything. Machine learning is everywhere. And yet, behind all the jargon and hype, the basic mechanics of how it works often get lost in translation. So let’s fix that.Here’s a no-fluff, clear-eyed walkthrough of how machine learning actually works—broken into five essential steps. No advanced math. No mysterious

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Vibe Coding Isn’t Engineering—It’s Playing With Matches in a Server Room

AI/ML / Dominique Ronde

You’ve seen them.The self-declared AI builders who’ve discovered ChatGPT’s code generation and now think they’ve unlocked developer superpowers. They copy, paste, tweak a few lines, and ship an app before your leftovers are warm in the microwave.No Git hygiene. No input validation. No tests. And definitely no clue.But hey—it compiles, so it must be fine,

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5 AI Myths That Need to Die (And the Truth Behind Them)

AI/ML / Dominique Ronde

AI has become the default headline material for everything—from the end of jobs to the rise of sentient machines to the next big creativity boom. But as the noise grows louder, the misconceptions do too. Let’s set the record straight. Here are five of the most persistent AI myths—and what’s actually true. Myth 1: AI

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