open-interpreter VS gorilla

I think openinterpreter [1] were one of the first teams in this space along with shroominic code interpreter api and afaik they started with selenium but have expanded to do a lot more os level work but wonder if having a more narrow specialization could help these newer projects be better at the one thing they are focused on.

[1] https://openinterpreter.com/

I taught https://github.com/KillianLucas/open-interpreter how to use https://github.com/ferrislucas/promptr

Then I asked it to add a test suite to a rails side project. It created missing factories, corrected a broken test database configuration, and wrote tests for the classes and controllers that I asked it to.

I didn't have to get involved with mundane details. I did have to intervene here and there, but not much. The tests aren't the best in the world, but IMO they're adding value by at least covering the happy path. They're not as good as an experienced person would write.

I did spend a non-trivial amount of time fiddling with the prompts I used to teach OI about Promptr as well as the prompts I used to get it to successfully create the test suite.

The total cost was around $11 using GPT4 turbo.

I think in this case it was a fun experiment. I think in the future, this type of tooling will be ubiquitous.

My understanding is that ShellGPT aims to be a complete OS assistant. It's similar to Open Interpreter (https://github.com/KillianLucas/open-interpreter).

Shelly is a mini tool at the moment that only generates and executes commands for you.

https://github.com/KillianLucas/open-interpreter Both OpenAI and Local

Yeah there's a program called openinterpreter, It works beautifully. https://openinterpreter.com/

Do you leverage https://gorilla.cs.berkeley.edu/ at all? If not, perhaps consider if it would solve some pain for you.

* Gorilla [https://github.com/ShishirPatil/gorilla]

Could be interesting to try some of these exercises with these models.

gorilla

Nice this made me go back and check up on the Gorilla LLM project [1] to see whats they are doing with API and if they have applied their fine tuning to any of the newer foundation models but looks like things have slowed down since they launched (?) or maybe development is happening elsewhere on some invisible discord channel but I hope the intersection of API calling and LLM as a logic processing function keep getting focus it's an important direction for interop across the web.

[1] https://github.com/ShishirPatil/gorilla

"Gorilla: Large Language Model Connected with Massive APIs" (2023) https://gorilla.cs.berkeley.edu/ :

> Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them!

eval/:

Humans of AI Podcast #7

An amazing conversation with Shishir Patil the creator of the Gorilla LLM, a large language model specifically trained to use APIs!

Shishir is currently a 5th year PhD student at the University of California, Berkeley whose work broadly covers ML-Systems, LLMs, Edge-ML, and Sky computing.

Definitely give the episode a listen to hear Shishir's story.

And to read more about #GorillaLLM, check out the project page!

https://gorilla.cs.berkeley.edu