Quick intro to Docker, Kubernetes and latest network tech

Over the past two months, we have systematically constructed a complete mental model of modern AI architectures. We laid the overarching blueprint in Deconstructing AI, zoomed into the matrix-math of the parameter file in Deconstructing the Brain, and animated that static intelligence in The Agentic Control Loop. From there, we scaled up, connecting isolated agents into collaborative swarms via the A2A Protocol, and finally gave them hands to interact with enterprise infrastr

Over the past few weeks, we’ve systematically broken down the architecture of modern enterprise AI. We established the foundational blueprint in Deconstructing AI, zoomed into the core matrix-math in Deconstructing the Brain, explored how static intelligence becomes autonomous in The Agentic Control Loop, and mapped out multi-agent collaboration in Deconstructing the Swarm. But despite all this advanced architecture, there is still one massive, glaring hole. An autonomous age

Over the past few weeks, we have been building a complete mental model of modern AI architectures. In Part 1, Deconstructing AI, we laid out the high-level orchestration of Large Language Models, Agents, and MCP servers. In Part 2, Deconstructing the Brain, we zoomed all the way in to reveal the frozen matrix-math that makes up an LLM. Then, in Part 3, The Agentic Control Loop, we evolved that passive brain into a dynamic entity, wrapping it in a continuous Sense-Think-Act lo

In my foundational piece, Deconstructing AI, I laid out the high-level architecture of how Large Language Models, Agents, and MCP servers interact. Then, last week in Deconstructing the Brain, we zoomed all the way in. We cracked open the LLM itself to reveal its structural reality: a frozen file of billions of numerical parameters processing data via pure matrix math. We saw how an LLM acts as an incredible static reasoning engine, taking a prompt and outputting a highly pre

In my last piece, Deconstructing AI, I explored how a modern autonomous AI network functions. I mapped out how an Orchestrator Agent takes a natural language request, delegates tasks using the A2A protocol, and interfaces with the physical world via MCP servers. But at the absolute center of that entire architecture sits the LLM (Large Language Model)—the reasoning core giving the instructions. This week, I am zooming in to this. I want to crack open that "brain" to look at i