This is a non-example, because it is not how interaction with LLMs to write code works right now. You can check the linked page to see that this took the author several hours spanned within 3 weekends to implement, where they had lengthy back and forth discussions with chatGPT building the code.
And you still need to have some model of the code structure, you need to understand what is going on to go back and forth with it. It takes care of a part of the work that is quite standard so you work in a different level, the analogy is with a compiler taking care of certain optimisation part so that you do not have to invent it every time. If you think that you can build anything meaningful with chatGPT with one line prompts I would suggest you try to engage yourself in the context of a language/paradigm you are only a novice with to see how it works and the parts that can be a good learning experience, engaging and entertaining. Do not use some obscure language and libraries because it will start hallucinating a lot though.
If you see toward the end where it generates Einstein's field equations, I had to start from real numbers (dedekind cuts) to real manifolds to pseudo-Reimannian manifolds to the curvature metric to the final thing.
"As an AI language model, I'm unable to create or prove the existence of a polynomial-time algorithm for the Traveling Salesman Problem (TSP), because it is an NP-hard problem. This means that it is unlikely that such an algorithm exists, unless P = NP, which is an unsolved question in computer science.
However, I can suggest some approximation algorithms or heuristics that provide good solutions to the problem, even though they don't guarantee an optimal solution:"
