social

Farcaster

The good news is that doing social media algorithms is *hard*, so no one really should be faulting Merkle.

I'm sure you've seen the complaints on X (so much so they released their algorithm, never updated it, and now claim to be Grok LLM powered). You've seen the complaints here on Farcaster. 

There are complaints on Threads too!

We once had only "following", which I guess many did get used to (the Facebook timeline/news feed). We were told algorithms would be better for us, but even facebook hasn't solved this since 2006!

Maybe the only people that cracked algorithmic discovery so far has been TikTok. And no, we haven't seen an update to Monolith since they talked about it. https://github.com/bytedance/monolith

hypothesis: text/image feed algos are "bad" because of a lack of preference revealing signals the algos have to determine what you like. the only reliable signals you can get from these feeds are explicit engagement (reactions/clicks/shares)

watch time is a very strong "interestingness" signal

I kind of agree with this hypothesis - because of watch time, but you can also post longer, or use threads. There are other signals X uses, like if there's a profile visit, as part of engagement metrics.

Monolith has a batch training stage (tensorflow like), and a online training stage (live feedback). Then the Kafka pipeline is highlighted, and they increase snapshot frequencies. 

They even have an example of using the MovieLens dataset, and a criteo dataset. 

Overall a very good paper to read!

I kind of agree with this hypothesis - because of watch time, but you can also post longer, or use threads. There are other signals X uses, like if there's a profile visit, as part of engagement metrics.

Monolith has a batch training stage (tensorflow like), and a online training stage (live feedback). Then the Kafka pipeline is highlighted, and they increase snapshot frequencies. 

They even have an example of using the MovieLens dataset, and a criteo dataset. 

Overall a very good paper to read!

https://grouplens.org/datasets/movielens/

Applied AI, databases, data, vibe coding (sic.), open source, community/devrel, remote. 

itinerant businessperson. globalist.

Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia