AI & Medicine
Advanced
⏱ 32 min
2026-07-04
What happens to me — the physician, the surgeon, the person who has spent forty years inside the system — while AI compresses a century of medical progress into a decade? A terrain map, in the spirit of Dario Amodei's 'Machines of Loving Grace' and drawing on the DeepMind documentary 'The Thinking Game.
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Deep Learning
Intermediate
AI agentstools
2026-07-24
Chapter Eight — the finale — of a ground-up account of how large language models work. Everything so far built a mind that answers. This chapter builds a system that acts: give the model tools and a loop, let it decide for itself which to use and when, and the oracle you consult becomes an agent that works on your behalf. We assemble the last piece, weigh the new care it demands, and stand back at last to see the whole machine — from a single split token to a system that reaches into the world.
⏱ 45 min
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Deep Learning
Intermediate
embeddingsretrieval
2026-07-23
Chapter Seven of a ground-up account of how large language models work. The assistant we finished building is sealed inside the moment its training ended — it knows nothing of your documents, and it bluffs when it does not know. This chapter gives it a way to reach outside itself: to turn text into geometry, search a body of knowledge by meaning rather than by keyword, and ground its answers in sources a human can open and check. It is the machine's first honest connection to the living world.
⏱ 40 min
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Deep Learning
Intermediate
post-traininginstruction tuning
2026-07-22
Chapter Six of a ground-up account of how large language models work. The last chapter left us with a base model — vast, fluent, knowledgeable, and useless: a mind without a manner, that answers your question with three more questions. This chapter is about the second, smaller, stranger training that gives it a manner — and about why the seams of that final shaping are exactly where a deployed model's most important behaviours, and its most dangerous failures, are quietly decided.
⏱ 40 min
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Deep Learning
Intermediate
traininggradient descent
2026-07-21
Chapter Five of a ground-up account of how large language models work. We built the whole engine in the last chapter — and admitted it was empty, a magnificent tower full of random numbers that would output pure gibberish. This chapter fills it. It is the story of how a single measure of one wrong guess can reach back through a hundred layers and correct every weight that caused it — and how, repeated across a large fraction of everything humans have written, that one procedure turns noise into something that knows the world.
⏱ 45 min
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Deep Learning
Intermediate
transformermulti-head attention
2026-07-20
Chapter Four of a ground-up account of how large language models work. We have the single beating part — attention, a token's glance across its neighbours. Now we build the whole body around it: many glances at once, the private thinking step that turns gathered context into inference, and the deep tower that refines meaning layer upon layer until a prediction can be read off the top. By the end, the full transformer — the architecture that has intimidated readers for years — will be a machine you understand from the inside.
⏱ 45 min
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Deep Learning
Intermediate
attentionself-attention
2026-07-19
Chapter Three of a ground-up account of how large language models work. In the last chapter we left every token stranded — rich with meaning but frozen, wearing the same face in every sentence. This chapter builds the single mechanism that lets a token turn its head, look at the words around it, and become a different thing in every context. It is called attention, it is the beating heart of every modern language model, and we are going to derive it from nothing.
⏱ 40 min
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Deep Learning
Beginner–Intermediate
next-token predictionembeddings
2026-07-18
Chapter Two of a ground-up account of how large language models work. A model is handed a stream of tokens that mean nothing — arbitrary ID numbers — and a single, almost insultingly simple task: guess the next one. This is the story of why that one task is enough to summon everything an LLM can do, and of the quiet trick that turns a meaningless number into something that behaves like understanding: giving every token a place in space.
⏱ 40 min
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Deep Learning
Beginner–Intermediate
tokenizationbyte pair encoding
2026-07-17
The opening chapter of a ground-up account of how large language models actually work. Before a model can think, it must read — and reading, for a machine, means something stranger and more consequential than most people imagine. This is the story of how the raw text of the internet becomes the tokens a model sees, why the model builds its own alphabet to do it, and why that single design choice explains so many of an LLM's strangest habits.
⏱ 40 min
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