Practical guides, open-source tools, and opinionated articles on using AI to transform research, teaching, and learning.
Using LLMs, coding assistants, and local models for literature review, data analysis, academic writing, and privacy-conscious research workflows.
Helping teachers and academics employ AI tools for course development, content creation, grading support, and productivity enhancement.
Helping students learn with and through AI — prompt and context engineering, critical evaluation of outputs, and building real skills, not shortcuts.
Code-centric workflows, coding assistants, agentic systems, and the philosophy that one environment replaces a dozen separate apps.
Our new article in The Conversation argues that AI is trained on the finished traces of human thinking rather than the process that produced them. That gap limits what machines can do, and it changes what happens to us when we hand them the thinking.
The ideas are still yours. So is the theorising, and most of the sentences. The writing drifts anyway. Marginalia learns how you write from your own published work, then flags where a draft stopped sounding like you.
Agentic AI won't out-predict a tuned gradient booster on large, clean data. Its value is in choosing and building the method, which sometimes means picking the booster.
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