There is a strange contradiction in modern society that becomes harder to ignore the deeper artificial intelligence moves into everyday life. And no worries, this won’t become a ethical lecture about lifestyle. We became incredibly disciplined about visible consumption. People replace old lightbulbs with LEDs because saving a few watts matters. The Food gets sourced […]
Over the last few days, a small AI experiment has been circulating across LinkedIn and Reddit: open a completely fresh ChatGPT session, ask for a random number between 1 and 100, and observe what happens. Surprisingly often, the answer is 73. Sometimes it is 42. In English sessions, 79 also appears more often than true
Understanding Randomness in LLMs: Why ChatGPT Often Picks 73, 42, or 79 Weiterlesen »
There is a pattern you start to recognize after a few AI projects. The demos work and the models look promising. The internal presentations create momentum., but somewhere between pilot and production, everything slows down or disappears somehow. Most people explain that gap with vague statements about „organizational readiness“ or „change management“. But in reality,
Why AI Compliance Fails Without Data Lineage, Auditability, and Reproducible Decisions Weiterlesen »
Crew and aircraft coordination depends on real-time operational data. This article explains why late data affects crew legality, aircraft availability, gate planning, turnaround, and disruption recovery.
Crew and Aircraft Coordination: Why Real-Time Data Matters in Airline Operations Weiterlesen »
Aviation does not lack data. It struggles with data movement. This article explains why real-time data exchange is becoming critical for AI, disruption management, cargo, crew coordination, airport operations, and maintenance.
Real-Time Data in Aviation: Why Late Data Is Becoming an Operational Risk Weiterlesen »
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
AI in Quantitative Investing: Limits of Autonomous Stock Picking Systems Weiterlesen »
You open your editor, describe what you want, and a few seconds later there is code on the screen. It is not perfect, but it is usually good enough to keep moving — especially when you are exploring something new or trying to get to a first working version. That removes a lot of friction,
Coding Agents Feel Cheap. That Might Not Last. Weiterlesen »
The medallion architecture is one of those concepts that keeps showing up in conversations, often wrapped in different terminology, but rarely explained in a way that connects to how systems are actually built. Most explanations stay abstract. Bronze, silver, gold sounds more like the Olympics than an actual data model, and terms like raw, refined,
What’s The Medallion Architecture Weiterlesen »
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 »
What recent regional disruptions remind us about resilience, locality, and architecture Recent geopolitical tensions in the Middle East coincided with service disruptions across parts of a major hyperscale cloud platform. Public reporting indicated that more than one region and several availability zones experienced degradation during the same period. Situations like this are complicated and affect
Cloud Is Not a Disaster Strategy Weiterlesen »