I’ve used spicy auto-complete, as well as agents running in my IDE, in my CLI, or on GitHub’s server-side. I’ve been experimenting enough with LLM/AI-driven programming to have an opinion on it. And it kind of sucks.
Because it’s not worth inventing a whole tool for a one-time use. Maybe you’re the kind of person who has to spin up 20 similar Django projects a year and it would be valuable to you.
But for the average person, it’s far more efficient to just have an LLM kick out the first 90% of the boilerplate and code up the last 10% themself.
“Not worth inventing”? Do you have any idea how insanely expensive LLMs are to run? All for a problem whose solution is basically static text with a few replacements?
You’re focused too much on the “inventing” and not enough on the “one time”. A flexible solution can find value even if it’s otherwise inferior to a rigid one.
If it’s 90% boilerplate like you were saying above, how flexible does it need to be, really? If it only needs to get 90% there, surely a general-purpose scaffolding tool could do the job just as well.
I’d rather use some tool bundled with the framework that outputs code that is up to the current standards and patterns than a tool that will pull defunct patterns from it’s training data, make shit up, and make mistakes that easily missed by a reviewer glazing over it
I honestly don’t think such a generic tool is possible, at least in a Django context. The boilerplate is about as minimal as is possible while still maintaining the flexibility to build anything.
Almost all my projects have the same kind of setup nowadays. But thats just work. For personal projects, I use a subset-ish. Theres a custom Admin module that I use to make ALL classes into Django admin models and it takes one import, boom done.
Because it’s not worth inventing a whole tool for a one-time use. Maybe you’re the kind of person who has to spin up 20 similar Django projects a year and it would be valuable to you.
But for the average person, it’s far more efficient to just have an LLM kick out the first 90% of the boilerplate and code up the last 10% themself.
“Not worth inventing”? Do you have any idea how insanely expensive LLMs are to run? All for a problem whose solution is basically static text with a few replacements?
You’re focused too much on the “inventing” and not enough on the “one time”. A flexible solution can find value even if it’s otherwise inferior to a rigid one.
If it’s 90% boilerplate like you were saying above, how flexible does it need to be, really? If it only needs to get 90% there, surely a general-purpose scaffolding tool could do the job just as well.
I’d rather use some tool bundled with the framework that outputs code that is up to the current standards and patterns than a tool that will pull defunct patterns from it’s training data, make shit up, and make mistakes that easily missed by a reviewer glazing over it
I honestly don’t think such a generic tool is possible, at least in a Django context. The boilerplate is about as minimal as is possible while still maintaining the flexibility to build anything.
I just use https://github.com/cookiecutter/cookiecutter and call it a day. No AI required. Probably saves me a good 4 hours in the beginning of each project.
Almost all my projects have the same kind of setup nowadays. But thats just work. For personal projects, I use a subset-ish. Theres a custom Admin module that I use to make ALL classes into Django admin models and it takes one import, boom done.