Blog Archive

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 isn’t just a technical breakthrough—it’s a societal one.From hiring to healthcare to content creation, AI systems are making decisions that affect lives, reputations, and livelihoods. That makes ethics not an afterthought—but a requirement. And while the field of AI ethics can get complicated fast, most of it boils…

Let’s settle something upfront:No—you don’t need to be a programmer to work in AI.But you do need to understand what you’re doing, why it matters, and where your limits are. AI isn’t a gated community for coders anymore. Today’s ecosystem is broad, fast-moving, and full of entry points for people…

If the last decade was about proving that AI works, the next will be about proving it can work responsibly, sustainably, and at scale. We’ve seen the prototypes. We’ve watched the demos. Now comes the harder part: integrating AI into daily life, critical systems, and economic structures without losing control—or…

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…

In today’s digital world, real-time data streaming is no longer a luxury—it’s a necessity. Businesses and AI-driven applications thrive on instant insights, requiring robust data pipelines that can process and react to events as they happen. Apache Kafka and Apache Flink form the backbone of modern real-time data processing, enabling…

As we have explored in the previous parts of this series, high-quality and real-time data are essential for AI and ML applications. Now, let’s take a deeper look into how to implement real-time data products effectively using Apache Kafka and Apache Flink. This part focuses on two crucial features of…

Building and maintaining data products for AI and ML is not just about collecting data—it is about ensuring data quality, scalability, and accessibility. Without addressing these challenges, AI models will produce unreliable results, and organizations will struggle to use data effectively. Two of the biggest challenges in this area are…

I still remember the days in the early 2010s, when the term „big data“ was widely discussed in the tech industry. Companies were encouraged to collect as much data as possible, seeing it as a key resource (or the new oil as we called it those days) for the digital…

The AI landscape is undergoing a seismic shift, and at the epicenter is DeepSeek—a $6M Chinese AI startup that has achieved what Silicon Valley deemed impossible. In doing so, it has wiped $2 trillion off the market capitalization of U.S. tech stocks, with NVIDIA alone losing over $500 billion. This…

Generative AI (GenAI) tools, such as ChatGPT or other large language models (LLMs), are hailed as groundbreaking innovations for generating human-like responses to diverse prompts. They excel at mimicking natural language, summarizing information, and even assisting with creative tasks. However, their technical foundation raises significant concerns about their suitability as…

Generative AI tools like ChatGPT are revolutionizing how we interact with information. They excel at producing natural, human-like responses, summarizing complex topics, and even offering creative insights. Yet, despite these capabilities, they fall short in one crucial area: literature research. Here’s why Generative AI (GenAI) tools aren’t designed to handle…

Financial markets generate one of the densest streams of real-time data we can observe today. Price ticks, order submissions, cancellations, executions, and settlement instructions all occur at millisecond scale. Within that torrent of activity, regulators and trading firms need to detect suspicious behavior: wash trades, spoofing, layering, or coordinated account…

Attending Gitex 2024 in Dubai felt like a step back into the golden days of CeBIT, Germany’s once world-renowned tech tradeshow. CeBIT was, for many years, the epicenter of technology and innovation, where businesses from all over the world came together to showcase cutting-edge technologies and close deals. I often…

In the evolving landscape of real-time data processing, Apache Flink has emerged as one of the leading stream processing frameworks, offering capabilities that go far beyond traditional data batch processing systems. As businesses increasingly rely on real-time data to drive decision-making, Flink has become an essential tool, enabling organizations to…

In today’s data-driven world, businesses need to process and analyze vast amounts of information in real time. This demand has led to the rise of data streaming platforms, with Apache Kafka emerging as one of the most powerful and widely adopted tools for real-time data streaming. Since its inception at…

In the landscape of AI and machine learning, Amazon Web Services (AWS) has introduced two major services—SageMaker and Bedrock—that cater to the needs of developers and businesses seeking to deploy machine learning (ML) models at scale. Although both services enable the integration of AI into various applications, their use cases…

Introduction The General Data Protection Regulation (GDPR), or Datenschutz-Grundverordnung (DSGVO) in German, mandates that organizations must be able to delete a user’s personal data upon request. Failure to comply with this requirement can result in severe fines, reaching up to 4% of a company’s global revenue or €20 million, whichever…