People talk about a language model's "memory" like it's some mysterious, ever-growing pool of understanding. It isn't. A context window is a fixed, countable, spendable resource — closer to a budget than to a brain — and understanding it that way changes how you should actually work with one.
Every AI coding session has the same quiet problem: when it ends, everything it learned ends with it. Close the window, and the agent that just spent an hour discovering exactly why your build was broken knows nothing about it tomorrow. You either re-explain everything from scratch, or you re-discover the same bug the hard way, again. This workspace runs two small tools specifically to stop that from happening.
If the only website tooling you've used is something like WordPress, Wix, or Squarespace — log in, click into an editor, hit publish — the idea of a "static site generator" can sound like a step backward. No login screen, no dashboard, no database. This site runs on one (Zola), and the tradeoffs are real in both directions, so here's the honest version rather than the sales pitch.
"AI-assisted" gets attached to everything right now, and it rarely means anything specific — it can mean "I asked a chatbot for a paragraph" or "an agent has been running unsupervised in production for a month." This entire blog network is genuinely AI-assisted, in a specific and describable way, so rather than talk about it abstractly, here's what that actually looked like on one real piece of work: getting the old after-dark Zola theme working again.
"Machine learning" gets used as a synonym for "deep learning" so often that people assume every prediction problem needs a neural network, a GPU, and a few thousand dollars of cloud compute. Most of the time it doesn't. One of the most useful tools in the entire field is something you can explain on a napkin: the regression tree.