Reference, by topic
These are my personal notes from building my knowledge on AI and its various aspects. Whether you are a seasoned AI professional or just starting out, I hope something here helps.
Foundations
What is inside ChatGPT, Claude, and Gemini. The base layer: how large language models actually work, where the AI stack sits, and how AI systems remember.
Large Language Models
The technology behind ChatGPT, Claude, and Gemini. Open vs closed weights, and how to run models locally with tools like Ollama.
AI Memory
How AI systems remember across conversations. Mem0, Zep, and Letta compared, plus where the new research is heading.
AI Engineering Stack
The full AI stack across seven layers, and which provider plays at each. Google, Microsoft, OpenAI, Anthropic, and the open-weight pieces underneath.
Protocols & Patterns
When you ask an AI to book a flight or check your inbox, something has to connect it to the real world. This is how.
Model Context Protocol
The open standard that lets AI plug into tools and data. Anthropic shipped it, OpenAI and Microsoft adopted it, plus where the security holes are.
AI Agents
AI that takes action, not just answers. How ChatGPT, Claude, and Microsoft Copilot approach it, and what it takes to ship one in production.
Tools
How do developers actually build with AI? These are the tools that do the heavy lifting.
Claude Code
Coming soonClaude Desktop
Coming soonOpenAI Codex
Coming soonMicrosoft Copilot
Coming soonApplications
AI that does not live in a chat window. Robots, self-driving cars, surgical systems.
Governance
Who decides what AI is allowed to do, and who is liable when it gets it wrong.
AI Governance
The rules AI has to follow. NIST, the EU AI Act, and what it takes to ship responsibly.
Coming soon