Over the last couple of days, my LinkedIn feed has been flooded with euphoric posts about “Code AI” and “local coding assistants”. Screenshots of terminals, bold claims about productivity exploding, and the familiar undertone that if you are not running an LLM locally via Ollama, OpenCode, or Copilot, you are already falling behind. I know […]
Running Code AI Locally: An Engineering Reality Check Weiterlesen »
This project did not start as an attempt to build a generic image recognition system or to benchmark computer vision frameworks. It started with three teddy bears that have been traveling with me since 2017. Over the years, they have accompanied me on flights, through airports, into hotel rooms, conference venues, cafés, and occasionally onto
Teaching a Machine to Recognize Traveling Bears Weiterlesen »
There is a growing belief that software engineering has become an optional skill and a 20-dollar subscription with the right prompts can build complex systems without understanding architecture, versioning, security, or operational reality. Engineers, according to this narrative, are a bottleneck that can be removed. I am skeptical of claims like these, but I do
Vibe Coding: Why It Feels Productive and Why It Fails Engineering Weiterlesen »
This „project“ did not start with the ambition to build a generic document classifier or to compete with existing document management systems. It started with a much more personal and probably familiar situation. I wanted to explore whether machine learning could help me to organize my PDFs better. Not reminding me of deadlines or summarizing
Teaching a Machine to Clean Up My Document Chaos Weiterlesen »
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
Part II: From Models to Systems: Building Real AI Infrastructure Weiterlesen »
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
The „LLM = AI“ Myth Weiterlesen »
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
Why I’m the BigData Pilot Weiterlesen »
Artificial Intelligence is often presented as a technical breakthrough, but that is only half the story. The more interesting half starts when the model leaves the notebook, enters a workflow, influences a decision, and suddenly has consequences for people who never agreed to become part of an experiment. That is where AI ethics becomes practical.
AI Ethics: The 3 Critical Questions on Bias, Accountability, and Transparency Weiterlesen »
You don’t need to code to work in AI. But you do need to understand what you’re doing, why it matters, and where your expertise ends. The AI conversation today swings between two false extremes. Some still treat AI as a technical fortress for machine learning engineers and data scientists alone. Others claim AI tools
Non-Coding AI Roles: Why Business, Product, and Domain Experts Drive AI Success Weiterlesen »
If the last decade proved that AI works, the next one will prove whether it can work responsibly, sustainably, and at scale. We have seen the prototypes, watched the demos, read the pitch decks, and survived enough “AI will replace everyone” panels to qualify for hazard pay. The technology is impressive, but the real test