A quick overview of how @akshaan built Trending Topics on Farcaster. 

1. Casts are selected and preprocessed daily. 
2. Model categorizes thousands of casts with a prompt. 
3. Topics are post-processed, merged and winners are picked

It costs about $1/day and runs in < 1 hour.

Technowatermelon. Elder Millenial. Building Farcaster. 

nf.td/varun

Each day, a few thousand of the best performing casts are selected and pre-processed. 

All of the text content is turned into structured JSON that can be fed into the model as a prompt. 

Right now, we're classifying 1-5% of all casts.

Casts are fed into a model with a prompt to provide topics. A condensed version of our prompt is below. 

We do about 2 million input tokens a day processing casts, and it usually takes ~30 minutes to fetch all the related topics for each cast. 

Models are very good at figuring out subtle context. They can tell that a post is about basketball even with an oblique reference to a team.

This is great info because I’m planning to build a “farcaster wrapped” mini app this weekend