Bellman–Ford, explained clearly.
A practical guide to shortest paths, negative edges, negative cycles, and a Python implementation.
on computing and education.
Why I write
From data science to teaching.
My background is in data science and computational maths. I worked first in commercial analytics, then in UK education data.
I now write about computing and education while training to teach computer science.
My approach is practical: measure what matters, remove work that adds no value, then improve what remains. Models, metrics and AI can support your thinking, but none can replace your judgement or understanding.
Articles and study
Personal site writingShort articles on computing, education, and evidence — for people who teach, learn, and decide. Browse the full article list, or subscribe via RSS . This is not a publication record or portfolio of formal work.
LLM fluency is not reasoning.
What large language models are good at, where fluent answers mislead us, and how to evaluate claims about intelligence and understanding.
Computer science has two parents.
Mathematics explains the structure and limits of computation. Engineering makes those ideas work in imperfect, physical systems. Students need both.
Teaching computing beyond syntax.
What pupils need besides working code: structure, explanation, debugging habits, and the confidence to reason about programs.
PGCE Secondary Computer Science with ICT.
Current teacher training at London Metropolitan University.
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Get in touch.
For questions about computing, education, or data, connect on LinkedIn.