But when it comes down to it everything in life is just nth dimensional tensor puzzles. What I really cherish computers for giving me is fast and clear feedback.
Instead, it is constant and accelerating destabilization. Hundreds of AI projects attempting to ride this accelerating wave were essentially just made obsolete yesterday - https://www.youtube.com/watch?v=DH-2BHDYNfk
I feel the excitement is going to very soon turn into frustration of attempting to remain relevant ahead of the accelerating technological curve. Humans need periods of stabilization to plan and reason about their lives.
That prime number example is a little bit like when you put a functools.lru_cache decorator on a function in Python. It's faster than computing the function call because it's able to recall the return value for the parameters from the cache "memory".
Of course, many skilled programmers are also mainly used a cache for common solutions to common problems organizations have in the programming domain. As humans we can derive satisfaction from being able to tackle the same task others can, as a confirmation of our own progress. We like "doing it ourselves". But globally that's not super interesting if lots of people are constantly re-developing the same solutions to the same problems for their own benefit. I guess that's the push-and-pull (and the anxiety generator).
In my opinion Stack Overflow does a fine job at that. And it's transparent in terms of solutions proposed being voted on and discussed. Turning that into sausage advice is a downgrade.
>But globally that's not super interesting if lots of people are constantly re-developing the same solutions to the same problems for their own benefit.
I'd argue this is how we train individuals thus globally quite relevant.
We're building systems capable of programing computers non-deterministically. I think this is huge. But not because ChatGPT23 will be a CEO or a politician. But because this is a paradigm shift in compute similar to moving from integrator machines to general computers. I don't think LLMs will make programmers obsolete. I think large enough models will make programming something completely different from what it is now.
The days of sequencing tokens for compilers/interpreters seem to be drawing to an end as the dominant way of specifying software products.