Category_Theory_Machine_Learning VS geniusrise-exit-proxy

For those that don't know Bruno, he's one of the organizers for https://cats.for.ai/

He also maintains an "Awesome-$X" like Github page for ML and Category Theory: https://github.com/bgavran/Category_Theory_Machine_Learning

I have no association with him and I doubt he knows who I am. But I thought there is enough interest here in both ML and Category Theory that others might be interested in this.

There's also the cats.for.ai group and this nice github: https://github.com/bgavran/Category_Theory_Machine_Learning

## More Links

1. https://github.com/geniusrise/geniusrise - core framework

2. https://github.com/geniusrise/geniusrise-huggingface - hf modules

3. https://github.com/geniusrise/geniusrise-openai - openai modules

4. https://github.com/geniusrise/geniusrise-listeners - streaming data input

5. https://github.com/geniusrise/geniusrise-databases - database input

6. https://github.com/geniusrise/geniusrise-prompt-actions - functional integrations (RAG-able and GPT function call-able, WIP)

7. https://github.com/geniusrise/geniusrise-indexing - vectorizing for RAG usecases (WIP)

8. https://github.com/geniusrise/geniusrise-exit-proxy - cached LLM interface with MITM-auditing (WIP)

## Asides

I think the core framework can be AGPL but the modules must be MIT / Apachev2.

I really wanted to create an elaborate example in the guides but could not find time, - something like load and vectorize SNOMED-CT or UMLS and use it to NER / RAG EHR docs. Or maybe a usecase of doctor communicating to patient in another language (a major problem in India), with reverse translation verifying translated output using the KG. These kinds of stuff are soon to come. Or discourse segmentation for better chunking for RAG usecases.

I'm not sure if I should add cyberpunk-ed scientists as banner images. I tried with mathematicians like Voevodsky to Andre Joyal to John Baez, but couldn't. Actual geniuses tend to not be famous, hence SDXL fails I guess.

I plan to also write this framework in scala. The category-theorizing of neural networks is amazing!!! https://github.com/bgavran/Category_Theory_Machine_Learning. I hope Bartosz Milewski approves.

I love Alan Turing, but cuz of "The Chemical Basis of Morphogenesis". It introduced me to the wonderful world of complex systems. Hence, his image as banner.

I'm also working on a cli library called "isomorphic", wraps over argparse and provides cli, api, yaml, json interfaces.

Yes, gradio integration is also underway.

Finally, to huggingface.

Also check out the curated list of papers on the intersection of CT and ML: https://github.com/bgavran/Category_Theory_Machine_Learning

## More Links

1. https://github.com/geniusrise/geniusrise - core framework

2. https://github.com/geniusrise/geniusrise-huggingface - hf modules

3. https://github.com/geniusrise/geniusrise-openai - openai modules

4. https://github.com/geniusrise/geniusrise-listeners - streaming data input

5. https://github.com/geniusrise/geniusrise-databases - database input

6. https://github.com/geniusrise/geniusrise-prompt-actions - functional integrations (RAG-able and GPT function call-able, WIP)

7. https://github.com/geniusrise/geniusrise-indexing - vectorizing for RAG usecases (WIP)

8. https://github.com/geniusrise/geniusrise-exit-proxy - cached LLM interface with MITM-auditing (WIP)

## Asides

I think the core framework can be AGPL but the modules must be MIT / Apachev2.

I really wanted to create an elaborate example in the guides but could not find time, - something like load and vectorize SNOMED-CT or UMLS and use it to NER / RAG EHR docs. Or maybe a usecase of doctor communicating to patient in another language (a major problem in India), with reverse translation verifying translated output using the KG. These kinds of stuff are soon to come. Or discourse segmentation for better chunking for RAG usecases.

I'm not sure if I should add cyberpunk-ed scientists as banner images. I tried with mathematicians like Voevodsky to Andre Joyal to John Baez, but couldn't. Actual geniuses tend to not be famous, hence SDXL fails I guess.

I plan to also write this framework in scala. The category-theorizing of neural networks is amazing!!! https://github.com/bgavran/Category_Theory_Machine_Learning. I hope Bartosz Milewski approves.

I love Alan Turing, but cuz of "The Chemical Basis of Morphogenesis". It introduced me to the wonderful world of complex systems. Hence, his image as banner.

I'm also working on a cli library called "isomorphic", wraps over argparse and provides cli, api, yaml, json interfaces.

Yes, gradio integration is also underway.

Finally, to huggingface.