In April 2026, Andrej Karpathy introduced the LLM Wiki that transformed personal knowledge management. He proposed a shift away from messy, unstructured notes toward a compiled system where an AI m...
This article documents a technical experiment applying Andrej Karpathy’s Autoresearch methodology, originally designed for ML model optimization, to data engineering pipeline. The project explores ...
Last weekend, I decided to spend some time inside the autoresearch framework shared by Andrej Karpathy. The idea: give an AI agent a small but real LLM training setup and let it experiment auto...
As enterprises transition from generative AI experimentation to autonomous agent production, observability and governance have emerged as the primary bottlenecks to scale. Unlike deterministic soft...
In the generative AI era, the core data engineering challenges persist: ensuring data integrity and availability. For the veteran architect building a data fabric, from designing complex ETL/ELT pa...
In our upcoming series Two Many Chefs in the Kitchen, we will explore some of the challenges putting AI to production. We don’t have a simple solution to address all of these problems, and we are n...
I spend most of my professional time working on Data & AI systems with layered complexities: distributed data and applications, production constraints, observability, safety, and scale. This p...
Take a look at the following two architectural diagrams of a data engineering agent responding to the same revenue KPI anomaly. Both have access to the same underlying data. Yet one may quickly con...
Agentic programming is an emerging approach to building software where developers leverages an AI agent than can plan, take actions, use tools, and self-correct within defined constraints. Instead ...
As large language models (LLMs) move into production, effective prompting looks and feels like system design: constraints, modularity, self-checks, and rigorous evaluation. This guide introduces ei...