artificial-intelligence

A single agent hits three walls: context limits, attention drift, and sequential bottlenecks. Multi-agent systems solve all three — the orchestrator decomposes, subagents run in parallel, each with its own clean context.

Every AI integration used to be custom-built. MCP changes that: write a tool once as an MCP server, and every MCP-compatible AI application can use it.

A language model takes a question and returns an answer. An AI agent takes a goal, makes a plan, uses tools, and produces a result. That difference turns AI from a chatbot into a collaborator.

Fine-tuning isn't always the answer — most of the time it isn't. But when it is, nothing else comes close. Here's when to use it, when RAG is the better call, and why LoRA changed everything.

The same model, the same question, worded differently — completely different results. That's not luck. How you frame your prompt directly determines the output you get.

Before I learned about RAG, I was dumping raw context into AI prompts and getting flooded with false positives. RAG changed everything. And it turns out Cursor and Claude Code use it too — that indexing progress bar isn't just for show.

AI models don't read text word by word or character by character — they read in tokens. Understanding this one concept explains context limits, pricing, and why models 'forget' things mid-conversation.

AI models are compiled binaries. Just like a compiled C program, a trained model is an artifact that stands independent of its source — a ready-to-execute brain specialized for a task. Here's what that really means.