AI: Through an Architect's Lens
Exploring AI/ML concepts from a distributed systems architect's perspective — bridging theory with production-grade engineering.
Part 0A: Neural Networks & The Learning Mechanism
Building deep intuition from first principles — how neural networks actually learn, explained step by step for engineers who want to truly understand the machinery before architecting with it.
Part 0B: From Sequences to Transformers
The journey from “words in order” to “understanding meaning” — how we taught machines to process language, and why the Transformer changed everything.
Part 1A: Understanding the LLM Machine
The technical foundations that drive every architectural decision — transformers, embeddings, and tokenization- are explained through the lens of cost, performance, and trade-offs.
Part 1B: Making Decisions with LLMs
From model selection to production reliability — the decision frameworks that separate prototype AI from enterprise systems.
Part 2A: Production RAG: What Tutorials Don’t Teach You
From naive retrieval to production-grade systems — the architectural patterns, chunking strategies, and retrieval engineering that separate demo RAG from enterprise RAG.