Engineer specializing in AI/ML systems. Discover insights, tutorials, and production-ready solutions in artificial intelligence, machine learning operations, and scalable ML platforms.
From childhood science experiments to AI engineering — this is not a coincidence, it's a vocation.
I'm an engineer (Master in Control Engineering & Robotics) specializing in AI and MLops. From childhood science experiments to building production ML platforms, vector databases, and monitoring systems — my journey combines deep technical foundations with hands-on expertise. I share practical insights from real-world ML deployments, system design, and the challenges of scaling AI in production.
An 8B reasoning model summarizes and labels documents for a retrieval index very well, at 10 seconds per document. I distilled the task into two 0.6B...
Agent memory is often measured against a weak baseline. Give a plain agent the same token budget and the advantage mostly disappears into seed varianc...
Frontier coding LLMs have converged within 0.8 points on SWE-bench Pro. The scaffold around them is now the dominant performance variable. Here is wha...
Unlock trustworthy AI: Master evaluation, monitoring, and observability. Discover why DevOps thinking fails AI and how a "helix" model elevates your s...
Building GenAI apps isn't like traditional software – most projects fail without a crucial shift. Discover how a data-first, evaluation-centric approa...
LLMs only *talk*; AI agents *act* – orchestrating tools for real-world tasks. Even seasoned AI pros misunderstand agents' inner workings – this guide...
Short visual notes on AI infrastructure and agents, first published on LinkedIn.
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