Tag:explainer

AI & ML, actually explained

No sci-fi robots, no scary math — just the ideas behind the tech that already picks your playlists, your route to work, and your Tuesday night movie.

You Built the Whole Chain

Building Blocks · Part 13

Article 1 opened with a single weight, multiplying a single number. Nothing since has introduced anything more magical than that.

The Same Model, Two Very Different Jobs

Building Blocks · Part 9

The exact same matrix multiplications run whether a model is learning or answering. So what could possibly be different enough to need its own dedicated hardware, memory budget, and math?

Tokens, Revisited: how text becomes math

Building Blocks · Part 6

Before a single vector or tensor can exist, raw text has to be chopped into pieces a model can count on one hand.

Tensors: the shape data takes inside AI

Building Blocks · Part 5

Vectors were one row of numbers. Tensors are what you get when a model needs to stack rows into grids, and grids into stacks of grids.

Dimensions: why AI thinks in thousands of directions at once

Building Blocks · Part 4

Every entry in an embedding is a separate axis of meaning. Here's what that actually buys a model, and what it costs.

Embeddings: where similar ideas learn to live near each other

Building Blocks · Part 3

Vectors gave every idea an address. Embeddings are what happens when a model chooses those addresses on purpose, so that meaning has geography.

Turning meaning into a list of numbers

Building Blocks · Part 2

Part 1 gave you the dials. This one gives them something to act on.

The billions of knobs inside a model

Building Blocks · Part 1

Every model you've ever used is, underneath, a very long list of numbers. Here's why that turned out to be the winning design.