A technical article explaining how large language models can encode arbitrary binary data into seemingly natural text by leveraging entropy and token probability distributions, similar to steganography. The author demonstrates a working implementation that converts files into stories by strategically selecting alternative tokens at each generation step based on their probability rankings.