Very interesting, gained a follower if not just for the intriguing topical discussion but more for the the prompt interaction regarding LLM and patturn recognition, (likely self biased due to lack of foundational and topical knowledge). Noticed in particular certain prompt/response input and output type spacing regarding ML interaction, also in the author's responses and prompts but also in the return outputs from the ML model examples. Appears to me that certain "functional" prompts/responses returns odd (non-numerical in this instance) type spacing. I suppose this is more of a prompt question of my own.
How do these apparent word spacing returns/entries effect the ML training overall or effect user prompts, is it a functional return from input or output? Much of the differential type space inconsistencies appear to be located around linguistic prompt and return (string delimiters?) Examples such as double, triple space or something in between (derrived from my visual perspective without actual analysis of the site code) around functional language linked prompts. Words which denote a functional inference or a linking inference. Seems to be more concentrated around functional code language (definitive phrasing, ex. is, for, while, if etc.) as well as words which give or request more context linguistically, adjective and individual objective based functional language especially in the prompts to the ML systems. Not well versed with the topic(s) as of yet.