

Vibe coding, in the sense of telling the model to make codebase changes, then directly using the output produced, is 100% marketing bullshit that does not scale beyond toy examples.
Here’s the rub: Claude is extremely useful as an advanced autocomplete, if and only if you’re guiding it architecturally through every task it runs, and you vet + revise the output yourself between iterations. You cannot effectively pilot entirely from chat in a mature codebase, and you must compile robust documentation and instructions for Claude to know how to work with your codebase.
You also must aggressively manage information in the context window yourself and keep it clean. You mentioned going in circles trying to get the robot to correct itself: huge mistake. Rewind to before the error, and give it better instructions to steer it away from the pitfall it fell into. Same vein, you also need to reset ASAP after pushing into the >100k token mark, because the models start melting into putty soon after (yes, even the “extended” 1M-window ones).
I’m someone who has massively benefited from using modern LLMs in my work, but I’m also a massive hater at the same time: They’re just a tool, not magic, and have to be used with great care and attention to get reasonable results. You absolutely cannot delegate your thinking to them, because it will bite you, hard and fast.
For your use case (3D math), what I recommend is decomposing your end goal into a series of pure functions that you’ll string together. Once you have that list, that’s where Claude comes in. Have it stub those functions for you, then have it implement them one at a time, reviewing the output of every one before proceeding.







Where on earth are you getting this from?
Galton was a eugenicist who thought intelligence was baked into one’s bloodline, Spearman’s entire career was that the g-factor was a relatively immutable cross-domain constant, Binet was measuring skulls phrenology style, etc.