Post-GPT Computing
Last week I used ChatGPT for the first time for a real world task at work. It was a self-contained lambda function to perform some admin tasks, so it seemed like an ideal fit. Although the experience was good, it&#x27;s far from the end of programmers. This was my experience:<p>* Although ChatGPT is pretty good at generating code, it kept making simple mistakes such as calling non-existing APIs or introducing bugs. Some of them it could fix itself, some I had to fix.<p>* The code provided worked well for the &quot;happy path&quot; but failed miserably for some corner cases. I had to fix that manually.<p>* The code was working, but I wouldn&#x27;t consider it production ready. It required some cleanup, unit tests, etc. Again, some of this with ChatGPT, some without.<p>* Not to mention that I was the one with the knowledge about the domain, what problem to solve, a vague idea of how...<p>Not to pick on OP but extracting a few seconds of video from a file is a pretty straightforward task, you can essentially do it with a bash one liner [1]. My biggest question is how ChatGPT performs with a large codebase, contributed over time by different authors, with complex domain logic and layers of abstraction.<p>I also had a brief existential crisis, but I just shrugged it off and got back to work.<p>[1] <a href="https:&#x2F;&#x2F;askubuntu.com&#x2F;questions&#x2F;59383&#x2F;extract-part-of-a-video-with-a-one-line-command" rel="nofollow">https:&#x2F;&#x2F;askubuntu.com&#x2F;questions&#x2F;59383&#x2F;extract-part-of-a-vide...</a>