gish VS transynthetical-engine

I've had good luck with both:

https://github.com/drorm/gish/blob/main/tasks/coding.txt

and

https://github.com/drorm/gish/blob/main/tasks/webapp.txt

With the second one, I reliably generated half a dozen apps with one command.

Not to say that it won't fail sometimes.

Well, if you're interested in something more lite-weight, I wrote

https://github.com/drorm/gish

which is a shell command that lets you interact with GPT with flags, pipes, etc. in a much more unixy way.

This TUI has some impressive features, like the bots and plugins, but I feel gish covers most of the use cases, specifically for software development.

GPT is significantly better at modifying code when following this "all code in, all code out" pattern. This pattern has downsides: you can quickly exhaust the context window, it's slow waiting for GPT to re-type your code (most of which it hasn't modified) and of course you're running up token costs. But the ability of GPT to understand and execute high level changes to the code is far superior with this approach.

I have tried quite a large number of alternative workflows. Outside the "all code in/out" pattern, GPT gets confused, makes mistakes, implements the requested change in different ways in different sections of the code, or just plain fails.

If you're asking for self contained modifications to a single function, that's all the code that needs to go in/out. On the other side of the spectrum, I had GPT build an entire small webapp using this pattern by repeatedly feeding it all the html/css/js along with a series of feature requests. Many feature requests required coordinated changes across html/css/js.

https://github.com/paul-gauthier/easy-chat#created-by-chatgp...

Another HN user has also released a command line tool along these lines called gish:

https://github.com/drorm/gish

Here’s an approach to return just JavaScript:

https://github.com/williamcotton/transynthetical-engine

The key is the addition of few-shot exemplars.

I think the two-layer approach is worthwhile if only for limiting tokens!

Here’s an example of what I mean:

https://github.com/williamcotton/transynthetical-engine#brow...

By keeping the main discourse between the user and the LLM from containing all of the generated code and instead just using that main “thread” to orchestrate instructions to write code it allows for more back-and-forth.

It’s a good technique in general!

I’m still too paranoid to execute instructions via email without a very limited set of abilities!

Here is an example of some prompt engineering in order to build augmentations for factual question-and-answer as well as building web applications:

https://github.com/williamcotton/transynthetical-engine

Hey Simon! I've been digging your writings on LLMs lately.

I've been having some decent luck with some of the approaches that I've discussed in the following articles and projects:

From Prompt Alchemy to Prompt Engineering: An Introduction to Analytic Augmentation: https://github.com/williamcotton/empirical-philosophy/blob/m...

https://www.williamcotton.com/articles/writing-web-applicati...

https://github.com/williamcotton/transynthetical-engine

I'd love to hear your thoughts on the matter!

Sure you can. The easiest way is to go to https://chat.openai.com/chat and paste in a Wikipedia article.

There are more involved manners like this: https://github.com/williamcotton/transynthetical-engine/blob...

Parsel : A (De-)compositional Framework for Algorithmic Reasoning with Language Models

https://arxiv.org/abs/2212.10561

Here's a notebook with an introduction:

https://github.com/ezelikman/parsel/blob/main/parsel.ipynb

And here's a GUI interface the author has been developing:

http://zelikman.me/parsel/interface.html

I've been working on an augmented large language model that given these few-shot exemplars can build the below fully-functional ToDo App: ==

https://github.com/williamcotton/transynthetical-engine/tree...

https://www.williamcotton.com/articles/junie-browser-builder...

All of this is still very rough around the edges, prone to errors of various kinds, and generally not ready for prime time, but anyone is welcome to play around with what is there!