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.
Urika is an open-source multi-agent platform that automates exploratory data analysis. You bring a dataset and a question — a team of 12 AI agents does the rest.
Voice-to-text isn't new — developers have used it for years and academics know about Whisper for transcription. But most people still aren't using it to speed up their daily work. Local, private, and practically free — here's what's worth trying.
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 Student data, unpublished research, industry IP, and institutional compliance all push toward local models.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.
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