Every AI system I've built has a graveyard of custom connectors — bespoke glue code written to bridge a model to a tool. Model Context Protocol is here to replace all of it. What it is, why it matters, and what it changes for how we build AI systems.

I spent months building a production RAG system. Then I watched someone dump the entire corpus into a million-token context window and get the same answer in one shot. Here's an honest look at where RAG still wins — and where it's already losing.

The race to 100B+ parameters is over for most applications. 7B quantized models running on-device are beating 70B hosted models on latency, cost, and privacy. A look at why smaller models are winning — and what that means for how we deploy ML.

RLHF teaches models to sound good. RLVR teaches them to be correct. Reinforcement learning from verifiable rewards is how DeepSeek-R1 and similar models learned to actually reason — and it's changing how we think about post-training.