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

Today's model uses 4 main features to "rank" a piece of content: 

1. How often you like casts from the author 
2. How often the author casts
3. How many interactions the cast has 
4. How old the cast is

For #2, are higher or lower cast counts preferred by the model? Or is it a ratio

If you cast a lot, the predictive power begins to diminish. 

A high prediction score means that the model will bump the cast up in the feeds of people who like it and down in the feeds of people who wont.

cooking @askgina.eth | prev: blockchain research @coinbase | sidshekhar.com | askgina.ai