How this channel uses it Across the material in this channel — from conversations about how language models work to discussions of building AI products at scale — embeddings come up as the mechanism that lets software work with meaning rather than just raw text. When the channel's guests explore how language models answer questions, how knowledge tools like Notion organize information, or how AI products find and compare content, the underlying step is the same: text is converted into embeddings — numerical vectors — so that similar ideas end up close together and can be found, compared, and reasoned over programmatically.