In this episode of My Weird Prompts, hosts Herman and Corn take a deep dive into the often-overlooked world of AI model cards. While most users treat these documents like "terms and conditions" to be scrolled past, Herman argues that in the landscape of 2026, they have become essential forensic reports that reveal a model’s true upbringing and inherent biases. The duo explores the history of model reporting—from its origins in hardware data sheets to the landmark 2019 paper by Mitchell and Gebru—and explains why transparency is the ultimate antidote to the "black box" problem.
Listeners will learn exactly what to look for when evaluating the latest releases from labs like Google, Meta, and OpenAI. Herman breaks down the "green flags" of modern documentation, such as detailed data provenance, rigorous decontamination processes to prevent benchmark cheating, and the implementation of Process Reward Models (PRMs). Whether you are a developer looking for the right prompt template or a curious enthusiast trying to verify leaderboard scores on Hugging Face, this episode provides a masterclass in reading between the lines of technical literature to find the signal in the noise.
