Vol. IV · 9
Embeddings: How Meaning Becomes Geometry
ML Foundations · Part 4
Of all the ideas in machine learning, this is the one that genuinely changed how I see the field: neural networks turn meaning into geometry.
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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 · 8
Scaling Laws: The Physics of Machine Learning
ML Foundations · Part 3
Most of machine learning research is messy and empirical — try something, see if the benchmark goes up. Then, in 2020, a team at OpenAI published a result with a completely different character.
Read entryVol. IV · 7
The Transformer, Explained the Way I Wish Someone Had Explained It to Me
ML Foundations · Part 2
In 2017, a paper with the almost arrogant title "Attention Is All You Need" replaced decades of accumulated architecture research with one mechanism.
Read entryVol. IV · 6
How Models Actually Learn: Gradient Descent and Backpropagation from First Principles
ML Foundations · Part 1
Every impressive thing a neural network does — writing code, recognizing faces, translating languages — comes down to one shockingly simple loop repeated billions of times: make a guess, measure how wrong it was, nudge every parameter a tiny bit in the dire…
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