neal@nairobi ~ bash
$ whoami
Dr Neal Aggarwal — Physician/Surgeon, AI & Robotics Engineer
$ cat focus.txt
Medical AI · LLM Engineering · Agentic Systems · Deep Learning · Algorithmic Trading
$ ls posts/
32 articles — machine intelligence, AI safety, blockchain, medical science
$ _
AI & Medicine Advanced ⏱ 32 min 2026-07-04

The Compressed Career: What AI Means for Physicians and Surgeons

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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// recent_posts

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Deep Learning Intermediate attentionself-attention 2026-07-19

Reading the Room: The Idea at the Heart of Every Language Model

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 Read article
Deep Learning Beginner–Intermediate next-token predictionembeddings 2026-07-18

The Prediction Game: How Tokens Learn to Mean Something

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 Read article
Deep Learning Beginner–Intermediate tokenizationbyte pair encoding 2026-07-17

The Grain of Language: How a Machine Reads the Internet

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 Read article
AI & Science Advanced AINobel Prize 2026-07-14

The Irreducible Core: Three Nobel Laureates on AI and the Future of Science

A Nobel Prize laureate who built the most consequential scientific AI tool of the last decade, a cell biologist who won the Nobel for unpicking the machinery of the cell cycle, and the Oxford engineer who chaired the Royal Society's landmark report on AI in science sit down together at Carlton House Terrace. This is what they said — and what it means.

⏱ 22 min Read article
AI & Medicine Advanced medtechAI adoption 2026-07-13

From Point Solution to Plumbing: Inside Medtech's 2026 AI Integration and Robotics Arms Race

Two structural shifts in medtech, examined against the primary sources: AI is moving from standalone pilot tools into the clinical and device fabric itself, and surgical robotics is scaling from a one-company category into a genuinely contested, multi-platform market. What the FDA filings, earnings calls, and funding rounds actually show.

⏱ 20 min Read article
Deep Learning Advanced RAGembeddings 2026-07-12

The Capstone: Build a Grounded Assistant

Lesson 8 of Learning With Dr Neal — the capstone. Build a working retrieval assistant over a folder of your own documents: local embeddings, the twenty-line retriever, grounded answers with citations, and an honest recall@k evaluation that will teach you more about deployed clinical AI than any product demo ever will.

⏱ 40 min Read article
Deep Learning Intermediate–Advanced embeddingsvector search 2026-07-11

Meaning as Geometry: Embeddings, Retrieval, and RAG

Lesson 7 of Learning With Dr Neal. The assistant from Lesson 6 knows nothing about your hospital's guidelines, your patient's notes, or anything published since its training data was collected. Closing that gap without retraining is the job of embeddings and retrieval — turning documents into geometry, searching by meaning, and grounding answers in sources you control.

⏱ 30 min Read article