I'm José David Baena - a passionate Software Engineer inBay Area, USA

The AI industry is reaching an inflection point where safety and reliability determine production deployment, not raw capabilities. Companies burning through compute for marginal benchmark improvements are losing to those building trustworthy, efficient systems. Your architectural decisions today will determine whether you can actually ship AI features or face production disasters.

Deep dive into modern transformer architecture decisions - RoPE position encoding, QK normalization, RMSNorm, ReLU² activation, pre-norm architecture, and more. Learn why each choice matters for training stability and performance.

Deep dive into KV caching for transformer inference - from O(T³) to O(T²) complexity. Explore cache lifecycle, prefill vs decode phases, dynamic growth, batch replication, and Multi-Query Attention optimization.