The closest I've ever felt to this as a native English speaker is reading words in music scores in English. I'm a classically trained cellist, and grew up learning notation with Italian and French words for directions and expression. I've never learned either of those languages, save the words used in music notation. Seeing a score with those words in English just feels... wrong. Not in any big way, but as you said: uncanny. Definitely get the "bad psuedocode" vibe, because to me it English in music notation feels similar -- like the person who wrote it didn't know what they were doing, even though the notation makes perfect sense and the music is good. It removes some of the flair of the art of the notation itself for me.
This reminds me of an article I read that was posted on HN only a few days ago: Uncertain<T>[1]. I think that a causality graph like this necessarily needs a concept of uncertainty to preserve nuance. I don't know whether this would be practical in terms of compute, but I'd think combining traditional NLP techniques with LLM analysis may make it so?
I don't think the part about front and back channels is quite correct. GET and POST requests are both encrypted in HTTPS -- including the URL (but not the domain, as DNS resolution happens separately). Front and back channel are more to do with trust boundaries, and what information is public vs private from the client's perspective.
If your experience of tofu is only the above, I completely understand your distaste for it. But I think you owe it to yourself to try better tofu, and not as a meat alternative. Tofu on its own doesn't have much flavour, but that's the point, you need to marinate it. Google can give you some tips (squeeze the water out, then soak it in something delicious -- hell, soak it in meat juices!), but I highly recommend trying some good, low moisture smoked firm tofu. It's so good, I often just snack on it, slicing it like a sausage. But it's also great in things like burritos to add a smokey kick. Try it!