AI Tooling

Everyone Talks About Agents. Nobody Talks About State.

Over the past year, the discussion in AI has gradually shifted away from models as isolated reasoning engines and toward agents as autonomous operational systems. Large language models are no longer framed merely as tools for generating text or answering questions. They are presented as components capable of planning, acting, coordinating across APIs, and making […]

Everyone Talks About Agents. Nobody Talks About State. Weiterlesen »

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, 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 »

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

Teaching a Machine to Recognize Traveling Bears Weiterlesen »

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

Vibe Coding: Why It Feels Productive and Why It Fails Engineering Weiterlesen »

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

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

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

The „LLM = AI“ Myth Weiterlesen »

Tech Is Only as Smart as the People Behind It

Artificial Intelligence continues to redefine industries, promising automation, efficiency, and unprecedented insights. From self-driving cars to generative language models, AI is being positioned as a revolutionary force capable of transforming business and society. Yet, as impressive as these advancements are, there is one fundamental truth that often gets overlooked: technology is only as smart as

Tech Is Only as Smart as the People Behind It Weiterlesen »