Just shipped a tool to let hub runners generate a @farcaster training corpus for LLM tuning — zero network requests. 

If you have your hub synced, it should be 100x+ faster in pulling data out of your hub compared to RPC-based methods.

Try it out with: `pip install "farglot[cli]"`

A sufficiently decentralized social network. farcaster.xyz

I just shipped a transformer-based SocialNLP toolkit for @farcaster.  This library can possibly help construct new ML-based feed algorithms based on text classification instead of relying on manual curation, recasts/reactions, and chronological ordering.

Check it out:

I just shipped a transformer-based SocialNLP toolkit for @farcaster.  This library can possibly help construct new ML-based feed algorithms based on text classification instead of relying on manual curation, recasts/reactions, and chronological ordering.

Check it out:
https://github.com/michaelhly/FarGlot

Michael Huang

@michaelhly