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 »
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 »
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 »
Some days ago in the evening, after I finished speaking at a meetup in Dubai, two young guys waited until most people had left the room. They were not interested in debating Kafka internals or LLM benchmarks. They asked something much more personal, and much more relevant. Where should we put our effort? What will
Why Chasing the Next Big Thing Is a Career Trap Weiterlesen »
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 »