Agreed that no one wants to watch shotput when the ball is launched out of a cannon, but people might be interested when the robots competing are anthropomorphs.
Not OP, but a naive guess is it would mean that you'd have your schema defined in an ORM (for example, Prisma). The advantage here is that the LLM gets context on both the schema and how the schema is used throughout the application.
It is possible, but not by default. Your app must request additional permissions for indefinite background execution. It's quite difficult to get your app approved with said permission.
I think it's a hasty jump to make assumptions about the biology of people based on their job description. Plenty of engineers I work with exercise regularly. I'm curious, do you have any links to research correlating sedentary jobs and testosterone levels?
> They tend to be more nerdy, introverted, and less masculine(lower testosterone, less aggressive).
Not a fan of you making this up based on perceived stereotypes and then using it to justify an argument. I do agree that these numbers are complicated and require analysis on data that we probably don't have access to.
It really depends on how many constraints and variables you're optimizing for. Because this problem is NP-hard, I'd bet you could make a more naive version that solves some easier ILP problems, but scaling it up probably requires quite a bit of optimization/good heuristics. Definitely seems like a good learning experience if you're interested in algorithms though!
Yes, ILP is NP-hard[1]. I don't know how many constraints Jet's problems require, but I believe there are approximation algorithms that can do quite decently on most problems (perhaps even Simplex might do decently?). Would be interested to hear about this from someone with a strong algorithms background though.
But... why would they waste time doing that when they COULD just do it in a visual format they're already familiar with? IMO writing excel formulas and having the constant visual feedback is pretty useful and I much prefer it to SQL even though I am very familiar with SQL and not so much with Excel.
No need to make an assertion about intelligence and learning programming languages in your comment here either. Everyone has their own workflow.
The memoization occurs in the fact that we are "remembering" the previous value (pre_sum). It's a little different in that we aren't memoizing/caching all previous values, but we are still caching the last computed values aka the "tail".
Wonder if you might be saying the same thing when you're 65. Meaningfulness can come from places besides jobs, house plants, or Facebook I think. I could also be totally wrong, but I think it's worth attempting to find out.