geppetto VS sgpt

I'm heavily using https://github.com/go-go-golems/geppetto for my work, which has a CLI mode and TUI chat mode. It exposes prompt templates as command line verbs, which it can load from multiple "repositories".

I maintain a set of prompts for each repository I am working in (alongside custom "prompto" https://github.com/go-go-golems/prompto scripts that generate dynamic prompting context, i made quite a few for thirdparty libraries for example: https://github.com/go-go-golems/promptos ).

Here's some of the public prompts I use: https://github.com/go-go-golems/geppetto/tree/main/cmd/pinoc...

I am currently working on a declarative agent framework.

(author here): I am currently writing a book about programming with LLMs, I have absolutely put my money where my mouth is over the last year, and there is not doubt in my mind that we will see incredible tools in 2024.

Already the emergent tools and frameworks are impressive, and the fact that you can make them yours by adding a couple of prompting lines and really tailor them to your codebase is the killer factor.

My tooling ( https://github.com/go-go-golems/geppetto ) sucks ass UI wise, yet I get an incredible value out of it. It's hard to quantify as a 10X, because my code architecture has changed to accomodate the models.

In some ways, the trick to coding with LLMs is to... not have them produce code, but intermediate DSL representations. There's much more to it, thus the book.

All of these examples were built in a couple of hours altogether. By the end of the article, you will be able to build them too, with no code involved.

I don't bother manually writing any of this data munching / API wrapping / result validating code anymore. I had to build a server-to-server integration with Google Tag Manager recently. I literally copy pasted the webpage into a simple 3 line prompt and can now generate PHP classes, typescript interfaces, event log parsers, SQL serialization with a simple shell command.

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?