A practical, no-nonsense resource for understanding how modern AI systems actually behave — minus hype, anthropomorphism, or marketing abstractions.
Deep dives into how large language models work under the hood, why they hedge, and how to interpret their outputs responsibly.
A discussion on the conflation and overlap of language continution and factual accuracy.
Read More →A practical breakdown of how large language models are trained, and what that term actually refers to
Read More →The output makes it seem like a giant repo of all known knowledge is 'saved' and referenced, but this is an inaccurate representation.
Read More →Discoveries and realizations that can reshape how data from LLMs are perceived.
Practical heuristics for using LLMs responsibly.
External references for deeper exploration.