Artificial Intelligence, Machine Learning and Data Science, Data Visualisation
I wrote a children’s book, then tried to illustrate it — and the same girl wouldn’t survive thirty pages. The problem wasn’t my prompts. It was that the model has no object permanence, and no amount of describing her fixes that.
A day at #Apple HQ exploring the technologies announced at #WWDC26. The biggest takeaway wasn’t a new framework, it was the conversations about turning AI into practical, production-ready software.
Solaris, a parade of Linux shells, the occasional PowerShell detour — and always, stubbornly, back to the native macOS Terminal with my hand-tuned zsh setup. Then a passing mention on Talk Python to Me got me to try Warp. Here are the honest first impressions of a terminal lifer.
Twelve years ago, I published my first book, Essential MATLAB and Octave. What began as an introduction to scientific computing for students and engineers became the starting point of a much larger journey as an author, educator and technologist.
Now, after writing three additional books, revising two of them for second editions, and spending more than a decade working in data science, machine learning and AI, I am returning to where it all began. Revisiting a first book is a curious experience: some ideas feel timeless, others reveal how much both the technology and the author have evolved.
In this post, I reflect on the lessons learned from writing technical books, how MATLAB, Octave and scientific computing have changed since 2014, and why the second edition is about much more than updating syntax and screenshots. At its heart, it remains a book about computational thinking—a skill that has become even more relevant in the age of AI.
OpenAI quietly admitted something with the Codex mobile launch: agents aren’t autonomous enough to leave alone. The phone is now the leash. Here’s what that means for governance, throughput, and the metric you should actually be tracking.
After more than a year of writing, revising and proof-checking, I’ve finally completed the proofs for the second edition of Advanced Data Science and Analytics with Python. The new edition expands coverage of transformers, generative AI, large language models, vector search, graph embeddings and modern deployment workflows — reflecting just how dramatically the AI landscape has evolved since 2020.
Ghost calls itself “the first database built for agents.” Strip the marketing varnish and what you’ve actually got is a forkable Postgres workflow that quietly redraws how developers experiment — and how easily disposable infrastructure can become disposable thinking.
A keynote at Christ Church, Oxford for the XXIII Symposium of Mexican Students and Studies in the UK. Talked about the unlikely arc from physics to AI and innovation leadership, and why the most useful conversations of the day were the ones that never made it to the microphone.
If most tech books try to explain what’s coming, The Coming Wave tries to force a more uncomfortable question: what happens when powerful capabilities stop being scarce? Mustafa Suleyman argues that AI and synthetic biology aren’t just the next innovation cycle, they’re a shift in how power itself is distributed. As the cost of doing complex, high-impact things collapses, control doesn’t scale with it. The real issue isn’t whether these technologies are good or bad, but whether any institution can meaningfully contain them once they’ve spread.
This release is about removing friction without touching the core: keeping your compressions on rhythm when it actually counts- Download here https://apps.apple.com/gb/app/easy-cpr/id1659875521