Building AI infrastructure — from data pipelines to GPU kernels. I write about data engineering, LLMOps, GPU optimization, and the systems behind reinforcement learning.
Every LLM is built on five foundational pillars: Basics, Systems, Scaling Laws, Data, and Alignment. This post maps out what they are and why mastering them is the path to building real AI systems.
Read Full Article →Test-time training lets models update their own weights during inference. Learn how TTT layers work, their GPU implications, and why this changes AI infrastructure.
Read Full Article →Learn how to add observability to LLM applications using OpenTelemetry and data engineering practices.
Read Full Article →Learn how to orchestrate a Retrieval-Augmented Generation pipeline using Apache Airflow and data engineering best practices.
Read Full Article →Learn how to apply data engineering principles to manage LLM prompts with versioning, labels, and rollback capabilities.
Read Full Article →RAG isn't magic - it's Extract, Transform, Load with vectors. I break down how your existing pipeline skills map directly to building production AI systems.
Read Full Article →10 minutes a day. 5 days. One working AI agent. Join the waitlist — launching first week of March.
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