Big Data

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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If Your AI Use Case Needs Perfect Data, It’s Not a Use Case—It’s a Wishlist

AI/ML /

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. by someone testing a new

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Real-Time AI Isn’t Built in Slides—It’s Built Like a Cockpit

Tech /

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, feedback loops, and real-time decisions.

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Batch Is Fine—for Laundry. Not for Business Decisions

Stream Pipelines /

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 impatient—not because they’re difficult, but

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PhD Diaries: Research Isn’t What You Think

AI/ML /

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 nuanced, uncertain, often messy process

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Tech Is Only as Smart as the People Behind It

AI/ML /

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 is only as smart as

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