Anybody in the farcaster community thinking about ML recommendation systems for a decentralized social network?

Lots of interesting challenges across data collection, training, model deployment/inference, asset management, etc

building /propertyforecast | data stuff, robotics, sometimes defi

I had an idea a few years back to do this sort of thing on the frontend with tensorflowjs. It was my vision for bbly.io, a firehose of news and comments (similar to Reddit) delivered over IPFS pubsub, curated clientside by models trained by users as they vote/label content.
Pure static HTML social news aggregation 👍

Definitely individually trained, in the browser.
Every post is labeled by the community with any number of seven labels (hilarious, enlightening, etc). Users start with a handful of default scoring/viewing profiles, letting them prioritize funny, or inspirational posts etc. They can then make their own. Then ML boosts:

Cool idea. Were you planning to use individually fine tuned models or some kind of federated learning approach?

Follow to get the best of Farcaster using @mbd - AI Recommendation Engine for Web3. @a16zcrypto css23. Ex-ML Lead @Chainlink. Ex-Sr Data Scientist @AWS.

The ipfs-pupsub network shat itself before any meaningful MVP could be completed, and never really came back, and I never came across a replacement. Also my day job keeps me busy enough.
One of many half baked projects camping out on my GitHub waiting for the right time to reemerge.
https://github.com/pierce403/bbly.io