Hi HN,

I'm part of the AI Engineering team at Vector Institute, and we created these AI Pocket References to serve as quick refreshers or starting points for AI professionals.

Each pocket reference is designed to be read in under 7 minutes while providing:

- Clear explanations of AI concepts and terminology - Helpful visualizations where applicable - Links to deeper resources for those who want to explore further

We've initially focused on LLMs and Federated Learning, and have started to scope out a collection for Responsible AI, as well as High Performance Computing topics.

Some highlights include:

KV-Cache for efficient LLM inference (https://vectorinstitute.github.io/ai-pocket-reference/nlp/ll...) Federated Learning (https://vectorinstitute.github.io/ai-pocket-reference/fl/cor...) Carbon footprint tracking for AI workloads (https://vectorinstitute.github.io/ai-pocket-reference/respon...)

The project is entirely open source, and we welcome contributions from the community—whether that's suggesting new topics, improving existing references, or creating new ones. Our goal is to build a comprehensive, community-maintained resource that makes AI knowledge more accessible to practitioners at all levels.

GitHub repo: https://github.com/VectorInstitute/ai-pocket-reference Full collection: https://vectorinstitute.github.io/ai-pocket-reference/

I'd love to hear your feedback or answer any questions about the project!