Blog Archive

AI has a visibility problem, but not in the way most people think. The models get the spotlight, the keynote demos, the investor excitement, and the polished slide decks. Meanwhile, the work that decides whether AI actually works in production often happens somewhere less glamorous, inside pipelines, data contracts, feature…

The issue is not that agents are overhyped. The issue is that people are forgetting what they already knew about distributed systems. Over the past two decades, we collectively agreed on a few basic principles in software architecture. Modular systems make change easier, loose coupling keeps components from holding each…

Someone came up to me at a conference and asked how they should implement the Saga pattern in Apache Flink and I think this is worth a written blogpost as well. Their team came from a classic microservices architecture, where Sagas had become the standard answer whenever one business transaction…

The original plan was never to do a world tour. It just slowly escalated from „one conference and a few customer meetings“ into Dubai, Portugal, Germany, Singapore, Japan, Hawaii, Los Angeles, London, Switzerland, and back to Dubai with enough flight segments to emotionally qualify as cabin crew. Looking back, the…

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…

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…

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…

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…

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…

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…

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,…

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…

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…

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…

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…

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…

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…

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…

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…

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…

Artificial Intelligence is often presented as a technical breakthrough, but that is only half the story. The more interesting half starts when the model leaves the notebook, enters a workflow, influences a decision, and suddenly has consequences for people who never agreed to become part of an experiment. That is…

You don’t need to code to work in AI. But you do need to understand what you’re doing, why it matters, and where your expertise ends. The AI conversation today swings between two false extremes. Some still treat AI as a technical fortress for machine learning engineers and data scientists…

If the last decade proved that AI works, the next one will prove whether it can work responsibly, sustainably, and at scale. We have seen the prototypes, watched the demos, read the pitch decks, and survived enough “AI will replace everyone” panels to qualify for hazard pay. The technology is…

AI often gets sold like a movie trailer: robots, revolutions, and the promise of sentient assistants that’ll someday file your taxes and raise your kids. But most real AI? It’s quieter. Less dramatic. And far more useful. If you think AI is all buzzwords and beta-stage experiments, you’re probably overlooking…

You’ve heard the buzz. AI is changing everything. Machine learning is everywhere. And yet, behind all the jargon and hype, the basic mechanics of how it works often get lost in translation. So let’s fix that.Here’s a no-fluff, clear-eyed walkthrough of how machine learning actually works—broken into five essential steps….

You’ve seen them.The self-declared AI builders who’ve discovered ChatGPT’s code generation and now think they’ve unlocked developer superpowers. They copy, paste, tweak a few lines, and ship an app before your leftovers are warm in the microwave.No Git hygiene. No input validation. No tests. And definitely no clue.But hey—it compiles,…

AI has become the default headline material for everything—from the end of jobs to the rise of sentient machines to the next big creativity boom. But as the noise grows louder, the misconceptions do too. Let’s set the record straight. Here are five of the most persistent AI myths—and what’s…

If you’ve ever heard someone say “We’re doing AI” and then describe a dashboard… you’re not alone. Too many conversations about modern tech start with AI hype, run through ML buzzwords, and land in Data Science dashboards—as if they all mean the same thing. They don’t.Let’s set the record straight….

Let’s get something out of the way:Your data isn’t perfect. It never was. It never will be. It’s late. It’s missing. It’s mislabeled. The schema changed without warning. A key field is suddenly NULL for 3,000 rows. And the lookup table you depend on? It got overwritten at 2 a.m….

Ask any AI leader how their last project went and you’ll likely hear about accuracy. F1 scores. AUC. The model “performed well.” Ask them how it’s doing in production, and things get quieter. The truth is uncomfortable but necessary:Most AI doesn’t fail in training. It fails in deployment. Accuracy Is…

Spend enough time in the AI space and you start to notice a pattern. There’s a lot of talk about modeling—neural architectures, parameter tuning, accuracy curves, and leaderboard rankings. And yet, when you actually try to bring an ML system into production, the modeling phase feels oddly… smooth. Controlled. Even…

There’s a disconnect we don’t talk about enough.Data systems have gone real-time.Enterprise planning hasn’t. While Kafka pipes millions of events per second and Flink runs stateful computations in motion, most enterprises are still operating on a roadmap that looks like a spreadsheet and moves like a barge. This isn’t just…

You don’t fly a plane with a keynote. You fly it with systems that work under pressure. There’s something strangely comforting about a well-designed slide deck. It’s clean, it’s abstract, it’s full of possibilities. But here’s the problem: planes don’t fly on possibilities. They fly on systems. On gauges, sensors,…

Why real-time systems aren’t luxury infrastructure—they’re how smart businesses stay ahead. Let’s get one thing out of the way:Batch is fine—for laundry. Not for decisions. Most companies still move data the same way they moved it in 2005: extract, load, wait, analyze, repeat. It’s comfortable. It’s familiar. But it’s also…

Why the future of AI depends on more than fancy prompts and flashy demos? A Useful Tool—But Not a Mastermind Let’s start here: I use GenAI every day. It drafts outlines, rewrites emails, summarizes documents, and helps me explore code ideas at 2 a.m. when my brain stalls. I value…

Expectations Have Changed—Permanently We live in a world that expects everything now. People track their parcels obsessively. They refresh flight apps every five minutes to check for gate changes. They want instant payment confirmations, real-time fraud checks, and status updates before they even think to ask. The modern customer is…

When people hear I’m pursuing a PhD in Artificial Intelligence, the reactions are nearly predictable: “You must be incredibly smart,” or “Wow, working on the future of humanity?” The assumptions are flattering—but often far from accurate. The truth? Research in AI isn’t some linear march toward breakthrough innovation. It’s a…

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…

As we move into the second quarter of 2025, it is clear that AI and data streaming are evolving at an unprecedented pace. With businesses increasingly relying on real-time insights, AI-driven automation, and event-driven architectures, the way we handle data is undergoing a fundamental transformation. While Generative AI continues to…

If you’ve spent any time on social media recently, you’ve probably seen the trend: people uploading selfies to AI services that render them as retro action figures sealed in plastic blister packs. The results are undeniably creative—quirky accessories, heroic stances, vintage fonts. It’s nostalgia wrapped in AI-powered novelty. But beneath…

Artificial Intelligence has become synonymous with Generative AI in recent years. Whenever AI is discussed in public forums, mainstream media, or boardroom meetings, the focus is almost exclusively on chatbots, image generation, and large language models like ChatGPT. While these technologies are indeed impressive, they represent only a fraction of…

The world of investing has always been a numbers game, but in the age of AI, those numbers are being processed at an unprecedented scale and speed. Financial markets generate vast amounts of data every second—price movements, trading volumes, earnings reports, economic indicators, and news sentiment. AI is transforming how…

The phrase “data is the new oil” has become a common metaphor in the digital economy, emphasizing data’s immense value as a driver of business and technological innovation. However, like crude oil, raw data in its unprocessed form is not inherently useful. Just as oil must undergo refining before it…