Latest Update:
Running Code AI Locally: An Engineering Reality Check
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,…
Most recent updates
Teaching a Machine to Recognize Traveling Bears
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 beaches. Airline by airline, the family slowly grew. Each bear developed its own character, its own role, and eventually its own name. They are not famous on Social Media, and they were never meant to…
Vibe Coding: Why It Feels Productive and Why It Fails Engineering
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 not consider dismissal without evidence a serious position. Instead of arguing in the abstract, I tested the approach myself under real constraints, using a realistic technology stack and enough complexity to move beyond toy examples….
Teaching a Machine to Clean Up My Document Chaos
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 documents, but assisting with a very concrete task: taking a new PDF and proposing the folder where it belongs, based on how similar documents were filed in the past. And for safety reasons, the system…
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 a closed-loop system. It doesn’t stop when the model outputs a prediction. It begins there. Every iteration passes through five continuous phases: Sense – collect signals from the world (transactions, sensors, logs, or user actions).Infer…
The „LLM = AI“ Myth
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 isn’t the same as a system that learns from experience. Calling a Large Language Model „AI“ is like calling a typewriter a journalist. LLMs Are Statistical Text Engines, Not Cognitive Systems A Large Language Model…
Why I’m the BigData Pilot
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 know what to do, but because precision and repeatability matter more than ego and a complex system don’t reward improvisation. They reward discipline, awareness, and a clear order of operations. It’s the same in data….