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Antacids did this to me. I was convinced it was electrolytes, would take supps etc but I believe it came down to calcium absorption. Taking a break from the antacids immediately stopped the cramping.

Funny one of the very first things I tried when I got my hands on chatgpt was to play the game "zork" with me. I was initially floored by the fact it could recreate the game on the fly, seemingly accurate, but it fell off the rails relatively quickly.

I'm totally green when it comes to nlp,transformers, LLM training, etc, but I staunchly believe you can't produce real "reasoning" or consistent logic based on the predictions of byte pair encodings.

The whole point is that a dsl syntax is constrained enough to fit in the context window and not rely on training data. LLMs are damn near built for this exact use case.

I think it all comes down to tight, context efficient, deterministic feedback loops. Pre commit hooks work well for this type of thing. Ideally I think a set of those hooks should run on every file edit, however I haven't gotten around to testing something like that yet.

Hit the nail on the head. I find it surprising people give a fuck any more. Who likes writing documentation? Us devs being horrible at and hating to write documentation was a standard trope before llms. They do a better job and dont complain.

I really like this idea and the way you mapped the concepts to unix primitives. Indeed llms are already "unix native". I've been experimenting with similar event driven workflows using k8s primitives but that's one level up the stack. This makes a whole lot of sense to me in terms of organizing a shared mental model. Will definitely check it out. Thanks for the good work.

I can't really explain it but when I am working with a system in my head it's not represented as text. It has an almost physical dimension to it similar to imagining how an engine works or what have you. This is obviously beyond the grasp of an LLM.

I can't prove it but I have strong beliefs that the logic and intuition required for abstracting for future changes is not possible in a stream of predicted tokens. Mental models aren't built from text. Something's missing that can't be measured.