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Attention, step by step: how a transformer decides what to look at
Queries, keys and values sound abstract until you follow a few tokens through the math by hand.
Gradient descent, felt rather than memorized
Build intuition for slopes, step sizes and why the loss sometimes explodes, before touching any framework.
Why your validation loss went up while training loss kept falling
Overfitting, explained with one small dataset and three fixes you can try today.
What the first layer of a CNN learns to see
Visualize trained filters and you find edge and colour detectors, much like the early stages of the visual cortex.
Tokenizers are where your model's quirks start
Byte-pair encoding explains why language models struggle to count letters and do arithmetic on long numbers.
Reward is not the goal: notes on reward hacking
When an agent maximizes the number you gave it rather than the behaviour you meant.
Diffusion models, from pure noise to an image
Train a network to remove a little noise, then run it many times in a row.
Shipping a model is the easy part: watching for drift
How input data quietly changes after launch, and the checks that catch it before your users do.
Evaluating LLM outputs without fooling yourself
Contaminated benchmarks, position bias in model graders, and how to build an eval set you can trust.
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