Warpcast now uses an ML model to order casts. 

If you have many unread casts, the model decides which ones go at the very top of your feed. 

It's main purpose is to learn your preferences and show you the most interesting stuff first. I'll go into some more details on how it works in the thread below.

Technowatermelon. Elder Millenial. Building Farcaster. 

nf.td/varun

Relying purely on the metadata of a cast - likes, author, frequency - instead of the content of the cast somehow doesn't seem complete. 

Wouldn't that work against surfacing relevant content and new users casting relevant content?

Unlearning. Full time product manager. Part time builder/tinkerer on farcaster. Bots: @nftsoffc.

This model isn't (yet) focussed on finding new stuff. 

It's mostly reordering the stuff that is already in your feed to present it in the best possible order so that the most interesting stuff is at the top. 

That seems to make feeds a lot more useful for people, both qualitatively and quantitatively.

does this maybe only work well with more than "like" as a reaction? I find very many posts interesting that I would never throw a like on. "Like" carries a lot of weight when it's the only option.

ex: a post providing a viewpoint on a topic. I might 100% disagree so no 'like' but still vry much wanted to read.