AI/ML

AI in Quantitative Investing: Limits of Autonomous Stock Picking Systems

Use Cases, Algorithm /

AI-driven stock picking agents are often presented as the next step in quantitative investing. The narrative is compelling: autonomous systems ingest market data, reason over it, and continuously improve decisions through feedback loops. In theory, this aligns well with modern machine learning paradigms and agent-based architectures. In practice, the situation is more constrained. These systems […]

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Hallucinations Are Not a Bug. They Are an Engineering Constraint.

Algorithm /

If you believe hallucinations in AI will disappear with the next model release, this blog post might be uncomfortable to read. Because they won’t. And this is not because the technology is broken or because engineers haven’t tried hard enough. It’s because this is not a product problem in the first place. And for everyone

Hallucinations Are Not a Bug. They Are an Engineering Constraint. Weiterlesen »

Everyone Talks About Agents. Nobody Talks About State.

Stream Pipelines, Algorithm, Architecture, Tools /

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 »

OpenClaw Is Not the Autonomy Revolution You Think It Is

Tools /

When you scroll through social media today, you might come away believing that OpenClaw has ushered in a new era of autonomous AI assistants that you can drop straight into production and have them “just work.” That impression is misleading. OpenClaw, formerly known as Clawdbot and Moltbot, is a clever and technically interesting side project

OpenClaw Is Not the Autonomy Revolution You Think It Is Weiterlesen »

Running Code AI Locally: An Engineering Reality Check

AI/ML, Tools /

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

Algorithm, Tools, Use Cases /

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

AI/ML, Tools /

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 »

Teaching a Machine to Clean Up My Document Chaos

AI/ML, Algorithm, Use Cases /

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 »

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

AI/ML /

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 »