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AI in Quantitative Investing: Limits of Autonomous Stock Picking Systems
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…
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Coding Agents Feel Cheap. That Might Not Last.
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, and it is easy to see why most teams have folded these tools into their daily workflow. After a while, the interaction starts to feel normal. You stop thinking about what happens behind the scenes…
Hallucinations Are Not a Bug. They Are an Engineering Constraint.
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 who still believes this technology is pure magic running on fairy tale dust, it isn’t. It’s just math. In the current AI conversation, hallucinations are still treated like a temporary glitch. Something that will vanish…
Everyone Talks About Agents. Nobody Talks About State.
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 decisions that affect real infrastructure. The narrative suggests that we are moving from static intelligence toward systems that can operate with a certain degree of independence. The enthusiasm is understandable. What is discussed far less…
Why Chasing the Next Big Thing Is a Career Trap
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 be the next big thing?Should we scale vertical or horizontal? It is a question almost everyone asks at some point. It sounds strategic. It sounds ambitious. It sounds like you are planning ahead. But the…
OpenClaw Is Not the Autonomy Revolution You Think It Is
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 created by a single developer for other developers. Its rise to GitHub trending status says as much about narrative dynamics as it does about engineering merit. OpenClaw is not a turn-key automation platform for enterprise…
Running Code AI Locally: An Engineering Reality Check
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 already falling behind. I know that not only engineers read my blog. A fair number of managers and tech leads do as well, often just to get a polite rant about trends and what is actually happening behind the screenshots….