fiftyone VS gorilla-cli

My experience has been much like this. For twenty years, I’ve emphasized scientific and engineering discovery in my work as an academic researcher, publishing these findings at the top conferences in computer vision, AI, and related fields. Yet, at my company, we focus on infrastructure that enables others to unlock scientific discovery. We have built a software framework that enables its users to do better work when training models and curating datasets with large unstructured, visual data — it’s kind of like a PyTorch++ or a Snowflake for unstructured data. This software stack, called FiftyOne in its single-user open source incarnation and FiftyOne Teams in its collaborative enterprise version, has garnered millions of installations and a vibrant user community.

We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.

With all that background out of the way, let’s turn theory into practice and learn how to use clustering to structure our unstructured data. We’ll be leveraging two open-source machine learning libraries: scikit-learn, which comes pre-packaged with implementations of most common clustering algorithms, and fiftyone, which streamlines the management and visualization of unstructured data:

FiftyOne is the leading open-source toolkit for the curation and visualization of unstructured data, built on top of MongoDB. It leverages the non-relational nature of MongoDB to provide an intuitive interface for working with datasets consisting of images, videos, point clouds, PDFs, and more.

Welcome to our weekly FiftyOne tips and tricks blog where we recap interesting questions and answers that have recently popped up on Slack, GitHub, Stack Overflow, and Reddit.

If you want to perform emoji searches locally with the same visual interface, you can do so with the Emoji Search plugin for FiftyOne.

kalpit-S contributed #2354 – added help link for Mapbox configuration in App

Hello HN! I know this project is a super simple wrapper around LangChain/OpenAI but I just found myself wanting this badly myself: a super simple `pip install` package that I can use to get command suggestions within the terminal as I'm being productive doing other things.

The implementation is literally one short glue of LangChain and InquirerPy for interactive CLI.

I'm curious which ideas you all have to make this smarter/better. MIT licensed, if you're keen on contributing please feel free to do so. It's a pure hobby project for me.

Some key objectives: never automatically run shell code, I want to see what I run before I run it, present me with some alternatives, a simple path to using local models in the future (Llama 2 Code soon?).

Will add I was inspired by the great https://github.com/gorilla-llm/gorilla-cli project, but didn't like that it sent the prompt to some IP based endpoint.

Very cool product! Have you consider relying on Gorilla for integrations?

https://github.com/gorilla-llm/gorilla-cli

gorilla-cli: LLMs for your CLI (https://github.com/gorilla-llm/gorilla-cli)