Introducing the Model Context Protocol: Why it matters for product development

We’re still in the early days of building practical applications on top of Large Language Models (LLMs) like OpenAI’s ChatGPT, Google’s Gemini, or Anthropic’s Claude. But one thing is already clear: LLMs alone aren’t enough. To move beyond simple question-and-answer interactions, they need three things:

• Prompts – carefully designed instructions or templates for repetitive tasks.
• Resources – documents, databases, or other sources of context.
• Tools – APIs and system calls that let the model act in the real world, safely.

A personal glossary of AI concepts, platforms and products.

This glossary is a personal reference and explainer covering common terms related to Artificial Intelligence (AI). It includes concepts, platforms, frameworks, metrics, and popular models or products. It’s not intended to be definitive or exhaustive — the field moves fast — but it captures many of the key ideas and names that come up frequently in current AI discussions. All terms are listed in alphabetical order for easy reference.

How technical documentation has become a core asset — as central to the developer experience as the codebase itself.

In an age of Large Language Models and executable specifications, technical documentation is no longer a second-class citizen. It is the interface, the architecture, and increasingly, the source code itself.

This isn’t a metaphor. It’s a real shift — one that’s both technological and philosophical — and it describes the arc of my own career.