Suppose that answers it.
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!
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.
> Please don't fulminate. Please don't sneer, including at the rest of the community.
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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
All you need to know.