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

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.

By "not an ML problem" do you mean that the work is more on process, data lifecycle, metrics/monitoring, etc? I.e. more of an engineering problem than a science problem

I am writing my thoughts about how we can do this in a decentralised way. RecSys are not trivial to get right and it is not an ML problem per say.. just hard to measure… Built my fair share of them in adtech, streaming, and retail and each time it is product/client/user base specific

Agreed. But longer term, for decentralized apps to compete with modern web2 alternatives, they will need to handle ML workloads

can probably just start with simple rules based systems before moving to ML

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

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 👍

Technowatermelon. Elder Millenial. Building Farcaster. 

nf.td/varun

@omilos recommendation engine is built on top of OpenAI. 