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A lot of this article and many of the comments here don't really comport with my experience programming and designing programs. Many or all of the examples in t

A lot of this article and many of the comments here don't really comport with my experience programming and designing programs. Many or all of the examples in the article seem like toy programs meant to do something relatively straightforward/simple with loose constraints, but in a domain or using tools unfamiliar to the programmer. However, many programmers find themselves in the opposite situation: using tools they're familiar with to solve complex problems with hard constraints. It's in that kind of situation where I've found that GPTs aren't much of a game changer. Many of the top comments voice a hated of programming--that programming gets in the way of creating. For a certain set of problems I completely agree (like why is making a website so damn complex), but for many of the problems I work on there are a wealth of good, helpful tools and APIs such that the writing of the code melts away and I'm left with the intellectual challenge of organizing my thoughts and thinking creatively. To me, the article and some of the top comments tell me more about the problems they choose to tackle than the act of programming itself.

teaearlgraycold 3y ago
Also many of these programs described in the article have been built many times before. This is effectively like saying programming will die because you can download programs off of GitHub. The "making flappy bird with GPT-4 in 30 minutes" YouTube videos are fun to watch, but I'll do more than raise an eyebrow when someone releases a novel video game on Steam written primarily by GPT-4 in a fraction of the time a human alone could have.
- specialist 3y ago
  "Computers are useless. They can only give you answers." -- Pablo Picasso
oivey 3y ago
I agree. I think there will be lots of hard problems where an LLM is unlikely to be successful for quite a while. For high surety software like encryption or numerical software I think this is especially the case. Sure, you can have an LLM create a draft still, but you’ll need a lot of skill to refine it into something correct and trustworthy.
armada651 3y ago
I feel ChatGPT is primarily good at dealing with problems that are straight-forward to solve, but have a needless amount of complexity attached to them that is easy to delve through for an AI but takes a considerable amount of time to grasp for a human. It is essentially dealing with the parts of software development that we have failed to make human-friendly. The compiler is already fulfilling a similar role in software development. We write in high-level languages so that we no longer need to deal with the intricacies of the hardware the program will be running on and can focus on high-level logic in a human-friendly programming language. I suspect that AI tools will fulfill a similar role, relieving us of much of the need to read API documentation, like compilers have relieved us of the need to read instruction set manuals.
deleted 3y ago
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teaearlgraycold 3y ago

Also many of these programs described in the article have been built many times before. This is effectively like saying programming will die because you can download programs off of GitHub. The "making flappy bird with GPT-4 in 30 minutes" YouTube videos are fun to watch, but I'll do more than raise an eyebrow when someone releases a novel video game on Steam written primarily by GPT-4 in a fraction of the time a human alone could have.

specialist 3y ago
"Computers are useless. They can only give you answers." -- Pablo Picasso

oivey 3y ago

I agree. I think there will be lots of hard problems where an LLM is unlikely to be successful for quite a while. For high surety software like encryption or numerical software I think this is especially the case. Sure, you can have an LLM create a draft still, but you’ll need a lot of skill to refine it into something correct and trustworthy.

armada651 3y ago

I feel ChatGPT is primarily good at dealing with problems that are straight-forward to solve, but have a needless amount of complexity attached to them that is easy to delve through for an AI but takes a considerable amount of time to grasp for a human. It is essentially dealing with the parts of software development that we have failed to make human-friendly. The compiler is already fulfilling a similar role in software development. We write in high-level languages so that we no longer need to deal with the intricacies of the hardware the program will be running on and can focus on high-level logic in a human-friendly programming language. I suspect that AI tools will fulfill a similar role, relieving us of much of the need to read API documentation, like compilers have relieved us of the need to read instruction set manuals.

deleted 3y ago

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