Vol. IV · 44
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.
Read entryFig. I — The ledger, updated monthly
Meditation
Hidden Layers / Fast Machines
Notes on engineering, frontier AI, editorial design, and the quiet mechanics of running an independent studio.
The archive
Vol. IV · 43
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.
Read entryVol. IV · 42
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.
Read entryVol. IV · 41
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.
Read entryVol. IV · 40
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.
Read entryVol. IV · 39
Twelve Pieces, One Working Model
Building Blocks · Part 0
Somewhere between "it's just predicting the next word" and a hundred-billion-dollar training run sits an actual mechanism.
Read entryVol. IV · 38
Are we close to AGI, or is it all hype?
Foundations · Part 9
The question that started this whole series, revisited now that you actually know how the current stuff works.
Read entryVol. IV · 37
Who owns intelligence?
Foundations · Part 8
One camp locks the recipe in a vault. The other publishes it for anyone to copy, tweak, and rebuild.
Read entryVol. IV · 36
Why "just the data" isn't neutral
Foundations · Part 7
AI doesn't invent its opinions from nowhere. It learns them — including the ones we'd rather it didn't — from us.
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