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.
Cloud AI is convenient — until you can't use it. Student data, unpublished research, industry IP, and institutional compliance all push toward local models. Here's the case for running LLMs on your own hardware.
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.
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