Data

An overview of how to transition from simply "talking" to your local model to connecting it to your personal data and local apps.

A look at the "hidden" part of RAG: how tools like ChromaDB or lanceDB store your files as mathematical points so the AI can find them.

A look at the root cause of bias: how incomplete or historical datasets (like 50 years of male-dominated resumes) "teach" the AI to be discriminatory.

If you feed a machine data from an unfair world, it becomes an unfair machine; exploring how AI inherits human prejudices in hiring, lending, and law.

A deeper dive into "Proxies"—how an AI can discriminate against a group even if you hide their race or gender (e.g., using a "Zip Code" as a secret stand-in...

An introduction to the benefits of running models on your own machine, from total data privacy to avoiding monthly subscription fees.