Comment by stillsut

Encoding / decoding hidden messages in LLM output.

The traditional use-case is steganography ("hidden writing"). But I see more potential applications than just for spy stuff.

I'm using this project as a case study for writing CS-oriented codebases and keeping track of every prompt and generated code line in a markdown file: https://github.com/sutt/innocuous/blob/master/docs/dev-summa...

My favorite pattern I've found is to write encode implementations manually, and then AI pretty easily is able to follow that logic and translate it into a decode function.