This repository is the collaborative workspace for the DS Discovery student team. We will co-develop introductory AI learning materials, pilot them with real learners, and iteratively refine the content based on classroom feedback.
Advanced high-school, community college, and first-year university students who already know basic Python and want a practical understanding of how large language models function and can be adapted for small projects.
- Design approachable, small-scale AI activities that demystify core concepts.
- Build supporting assets (slides, notebooks, datasets) that student instructors can remix quickly.
- Document experiments and reflection notes so future cohorts inherit a clear playbook.
- Favor rapid prototypes over extensive upfront specs; capture learnings in short write-ups.
- Use issues to track ideas, experiments, and feedback from try-outs.
- Keep the main branch classroom-ready by merging only reviewed materials.
- Author notebooks in Jupyter/JupyterLab so they run unchanged on both local machines and the shared JupyterHub instance.
- Package datasets and model weights with lightweight download helpers so students can swap between hosted APIs, open-source checkpoints, or tiny distilled models.
- Keep GPU requirements optional; every activity should have a CPU-friendly path so learners can experiment anywhere.