Notes
Random updates and thoughts: what I am building, learning, and thinking about. For longer, specific blogs, read me on WordPress and Medium.
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- Building AI Stack Advisor, a build-vs-buy calculator for AI
Should a business buy a SaaS tool, build on APIs, or run its own model? A free browser tool that answers with costs, TCO, break-even, and ROI.
- Building TickerBrief, an equity research agent
Type a ticker, get a full equity research report with charts, filings, news sentiment, and an LLM-written narrative. All local and free.
- Building a model drift alert agent
A local MLOps stack that notices when a model starts going wrong and explains why, in plain English, on Slack.
- Building a fully local RAG agent
Asking questions about my own documents with n8n, Ollama, and ChromaDB, with nothing leaving my machine.
- What is RAG, and why does it matter so much?
Retrieval-augmented generation lets an AI answer from your own documents instead of its memory. Here is how it works, why it has become so important, and where it goes wrong.
- Creating FocusPulse
A Chrome extension where you track your own focus in four colours. Ideas to make it more addictive are welcome.
- How AI benchmarks are actually designed, from first principles
Benchmarks look like simple tables of percentages. Underneath, each one is a measuring instrument with design choices about tasks, scoring, statistics, and leakage. Here is how they work.
- Creating EasyAutoFill
A local, open-source browser extension that fills any form from your own profile file.
- Starting a statistics series on Medium
Stats_with_FirstPrinciples: rebuilding statistical thinking from the ground up, one intuitive post at a time.
- Automating my morning arXiv routine
Checking arXiv every day and feeding papers to an LLM ate my mornings, so I built an n8n agent to do it.
- How machine learning and deep learning are different
Not "deep learning is ML with neural networks," but why. A first-principles look at what each one is really doing, and why the difference comes down to who builds the representation.
- How AI labs actually train their models
From raw web text to a helpful assistant: the stages every big lab goes through, and how OpenAI, Anthropic, Google, Meta, and DeepSeek each do it differently.
- Karpathy in 2017: “Gradient descent can write better code than you”
In 2017, Andrej Karpathy joked that gradient descent writes better code than us. Seven years later, it doesn't feel like much of a joke.
- How Claude overtook GPT (where it matters)
ChatGPT is still the household name, but in coding and enterprise use, Anthropic's Claude has caught up and pulled ahead this year. Here is how that happened.
- The maths and physics hiding inside LLMs
Like many others, I was surprised the physics Nobel went to Geoffrey Hinton. So I went looking for the physics and maths inside large language models.
- My quickest paper, and an unexpected collaboration
How a comment in a research meeting turned into a paper with Mustapha Azreg-Aïnou in about six weeks.
- GPT: the model that started it all
With GPT-4 out, I went back to where the GPT series began. The surprising part is how simple the core idea is, and how far scaling it went.
- ChatGPT: the interface that changed everything
ChatGPT is two weeks old and everyone around me is talking to it. The model behind it is not brand new. The way we get to use it is.
- Where machine learning meets black hole energy extraction
Machine learning doesn't calculate the Penrose process for us, but it is quietly becoming essential to everything around it.
- My first paper: stealing energy from spinning black holes
The Penrose process in plain words, and what my first paper asked about black hole look-alikes.
- My MSc thesis: following light and matter around black holes
Five months of learning how light and particles move around black holes, naked singularities, and wormholes.