> This tool has been generated entirely with ChatGPT and hence it is also a demonstration of how useful products can be developed with collaborative reiteration with GPTs.
If it works, I don't see anything wrong with it. It looks good. Hating on projects that disclose their GPT usage will only incentivize them to not disclose in the future.
Haha yeah, I wanted to frame this as a single file tool, and hence all the quick wins. But later moved the react app into a separate file because I was not getting the syntax highlights and all.
Definitely this can be made better with proper packaging and tooling! As I mentioned, more than the idea, implementation was an experiment on the ability of collaborative iterations with GPTs- it was fun!
As much as I love to hate on chatgpt engineer, the code is definitely okay; not much difference from a project you would write quickly in a single weekend.
I'm not saying that humans never come up with this kind of crap, but it feels like it's going to be a LOT more common the more we resort to AI. I wonder if "... and make the code easy-to-read so that it's maintainable in future" is ever included in prompts for LLM-generated code.
I'm a clean code guy and tailwind made my code look terrible. But I gave it a chance because I heared so many positive things about it. Now I'm happy with it. I don't have to jump around from HTML to CSS and back to HTML.
That is just tailwind CSS, one the most common CSS libs out there I would think. Your comment is actually a lot more telling of something as opposed to this AI written project though.
> For those of us not in the know, what does that mean?
I reckon that some people are either critical of LLMs due to the data that they're trained on (concerns about the legality of using the outputs, or the ethics of taking open source projects with a variety of licenses and how transformative or not the output might be), or question the quality of the code that they might output. Oh, also there's hype cycles and right now people at large might overestimate the capabilities of what LLMs can or cannot do well, which can be tiring, as seeing constant posts about crypto was (where we had a solution in search of a problem).
In my eyes, they can be used in conjunction with something like IntelliSense and IDEs to solve select problems (ones with solutions that already exist out there) more quickly, as well as save time on some boilerplate, as long as you still validate the outputs and actually check if everything works. Nowadays I'm using GitHub Copilot and the user experience is mostly okay, I'd say it actually lets me write code faster, at the expense of occasionally getting things pretty wrong, but is still a net positive, other considerations aside.
Web-Based Log Viewer: Get a cleaner and more organized view of command outputs.
Real-Time Updates: Logs are displayed in real-time as the command executes.
Interactive Interface: Search, filter, and navigate through logs easily. // TODO