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.
Universities panicked when ChatGPT arrived. Bans, detection tools, fear of cheating. But the real risk was never the tools — it was failing to teach students how to use them well. Here's how we went from cautious observers to building an entire course around AI-assisted learning.
Many of my colleagues and students freeze the moment I open a terminal. But the command line isn't hard — it's unfamiliar. And in a world where AI assistants are text-native, that unfamiliarity is costing you more than you think.
I stopped using PowerPoint, Keynote, and Google Slides entirely this semester. Here's why coding lecture slides in HTML with AI assistants is faster, better, and more enjoyable — and how you can make the switch.
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