sgpt VS scrapio

I feel only a bit bad when deploying a billion dollar machine model to ask "how to rename a git a branch" every other week. Its the easiest way (https://github.com/tbckr/sgpt) compared to reading the manual, but reading the manual is the right way.

I've been saving a lot of time in the terminal recently with shell-gpt (https://github.com/tbckr/sgpt):

    $ sgpt -s "The command 'sp current' outputs

https://github.com/tbckr/sgpt

I totally agree with LLM+CLI are perfect fit.

One pattern I used recently was httrack + w3m dump + sgpt images with gpt vision to generate a 278K token specific knowledge base with a custom perl hack for a RAG that preserved the outline of the knowledge.

Which brings me to my question for you - have you seen anything unix philosophy aligned for processing inputs and doing RAG locally?

> Nah these things are all stupid as hell. Any back and forth between a human and an LLM in terms of problem solving coding tasks is an absolute disaster.

I actually agree in the general case, but for specific applications these tools can be seriously awesome. Case in point - this repo of mine, which I think it's fair to say was 80% written by GPT-4 via Aider.

https://github.com/epiccoleman/scrapio

Now of course this is a very simple project, which is obviously going to have better results. And if you read through the commit history [1], you can see that I had to have a pretty good idea of what had to be done to get useful output from the LLM. There are places where I had to figure out something that the LLM was never going to get on its own, places where I made manual changes because directing the AI to do it would have been more trouble than it was worth, etc.

But to me, the cool thing about this project was that I just wouldn't have bothered to do it if I had to do all the work myself. Realistically I just wanted to download and process a list of like 15 urls, and I don't think the time invested in writing a scraper would have made sense for the level of time I would have saved if I had to figure it all out myself. But because I knew specifically what needed to happen, and was able to provide detailed requirements, I saved a ton of time and labor and wound up with something useful.

I've tried to use these sorts of tools for tasks in bigger and more complicated repos, and I agree that in those cases they really tend to swing and miss more often than not. But if you're smart enough to use it as the tool it is and recognize the limitations, LLM-aided dev can be seriously great.

[1]: https://github.com/epiccoleman/scrapio/commits/master/?befor...