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.

Comparing with X, there is a lot of content on X that gets surfaced to me that I find relevant and I read it, but I do not physically press the like button.

And the author of that relevant content doesn't matter to me as much. If I like the content, I will just follow them.

I don't doubt it makes the feed useful and you guys will keep iterating.

Just airing a stream of thoughts.

- You liked a cast and followed an author most likely because of the content of some of their casts. Think the current algo may be missing that key signal. It  is looking at the outputs (likes/follows)...

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.