Resources
Select resources, many of which I follow regularly. Key:
💰 Requires subscription or payment.
🪲A resource I personally use regularly.
Books
The field of AI Engineering is developing so rapidly it’s hard to recommend particular books, but here’s one which came out December 2024:
AI Engineering by Chip Huyen 🪲💰
Like all O’Reilly books, this is expensive. I’m currently on page 173 of 500. It’s good background information, but has less practical hands-on information than I had anticipated, and hands-on is what AI Engineering is all about. Good if you want to understand things in-depth and have the motivation to actually read this big book.
People to learn from
Andrej Karpathy 🪲
Andrej has a PhD on natural language processing and computer vision from Stanford University. He worked at Tesla and then was one of the co-founders of OpenAI. Unlike Sam Altman he is not focused on the money and is very concerned with ethics. He is also great at explaining how this stuff works.
Simon Willison 🪲
A prolific blogger and AI Engineer, Willison is co-creator of the Python Django Web framework. He does a lot of testing of LLMs and creates tools and methods to make them easier to use. He is also a bit of a productivity nerd.
- Willison’s website (the best place to follow him)
Jeremy Howard 🪲
The co-founder of fast.ai, a research institute dedicated to making Deep Learning more accessible.
Andrew Ng 🪲
A cofounder and head of Google Brain and was the former Chief Scientist at Baidu, the Chinese search giant. Does a lot of education stuff and I’ve done some of his Coursera courses, but he tends to be too focused on the math and I find him a bit boring and uninspiring.
Michael Pound 🪲
A researcher at the University of Nottingham who posts some good AI explainers on YouTube.
News sources
Some of these sources require subscription, but you get a few free articles each month. I subscribe to The Economist but none of the others.
- AI from The Economist 🪲💰 — in-depth and reliable industry analysis
- Hacker News 🪲 — Good quality discussion on AI news and other stuff
- TechCrunch AI 🪲(💰 for additional features)
- arsTECHNICA on AI 🪲
- The Verge on AI 🪲💰
- Wired on AI 🪲💰
- O’Reilly AI & ML radar
- AI News — A summary of AI news from lots of different source with RSS feed. 🪲
Others to view occasionally
- Jack Clark — Import AI
- Scott Aaronson blog 🪲 — understand the reality of Quantum computing
Official Documentation & Guides
Guides and manuals from major AI providers, focused on how to use AI models.
- OpenAI API Documentation — Official docs for using OpenAI’s models (like GPT-4, ChatGPT, DALL·E) via API, with code examples, prompt usage tips…
- Google Cloud Generative AI Docs — Google’s official documentation for deploying generative models on Google Cloud’s Vertex AI and MakerSuite.
- Microsoft Azure AI (OpenAI) Docs — Microsoft’s guides to using OpenAI models on Azure, covering setup, integration, and enterprise features.
- Anthropic Claude API Docs — Anthropic’s official developer documentation for using the Claude large language model, with how-tos for prompting Claude.
Major Corporate AI Blogs
Only the Microsoft Blog has an RSS feed, which sucks.
YouTube Channels
- Two Minute Papers — Short summaries of the latest AI research papers and breakthroughs.
- AI Explained — Not used this one but it looks good.
- Machine Learning Street Talk — Ditto
- All About AI — A channel dedicated to practical AI usage.
- Robert Miles – AI Safety — Focused on the ethics and safety side of AI.
- Google Cloud Tech — Flashy videos that often don’t say very much.
Communities & Forums
- OpenAI Community Forum
- Hugging Face Discussion Forum
- Reddit AI Communities – I used to use Reddit a lot, now hardly at all.
Open-Source Tools & Libraries (for Using AI Models)
If you have suggestions for additions here, please send them to me: [email protected].