For You is based on your likes. If you get an empty feed then you probably haven't liked anything yet. Try liking a couple of posts in Discover feed and get back to For You.
To help me debug the algorithm I built a simple web UI that allows you to see the feed for any user by plugging their account id: https://linklonk.com/bluesky
You can switch perspective to other users and explore how the would experience the feed.
What you are describing is similar to how https://LinkLonk.com works (my side project) - when you "like" a link you get connected to the RSS feeds that posted that link and other users that also liked it. Then you get content from feeds and users that you are connected to. The more links in common you have with a feed or a user the more weight their other links have.
My understanding is that the attention in all transformer layers is "causal" - that is the output of a transformer layer for token N depends only on tokens from 0 to N.
This means that every attention layer can use previously calculated outputs for the same prompt prefix. So it only needs to calculate from scratch starting from the first unique token in the prompt sequence.
I think the commenter was thinking about the input embedding layer, where to get an input token embedding the model does a lookup of the embedding by index, which is constant time.
And the blog post author is talking about the output layer where the model has to produce an output prediction for every possible token in the vocabulary. Each output token prediction is a dot-product between the transformer hidden state (D) and the token embedding (D) (whether shared with input or not) for all tokens in the vocabulary (V). That's where the VD comes from.
It would be great to clarify this in the blog post to make it more accessible but I understand that there is a tradeoff.
The point is, if you upvote this link on LinkLonk (https://linklonk.com/item/481037215144673280), you automatically get subscribed to all of these feeds. This is a way to discover new feeds through content you liked.
Now, being connected to hundreds or thousands of feeds might seem crazy. But we have a solution to that which also relies on what content you "liked". LinkLonk knows how often you liked content from each feed you are connected to (which is essentially the signal-to-noise ratio). So it ranks new content based on that. If you like 50% of posts from https://simonwillison.net/atom/everything/ then new posts from Simon Willison will be shown above other links from, say, https://lobste.rs/rss.
The more you like - the better the ranking of fresh content becomes.
In this world you don't have to actively manage which feeds you are subscribed to or not. You only rate content.
Yes, it requires keeping track of how much each user trusts each other user. And then when you rank content for user A, you use the trust table of user A as weights of upvotes.
This is more computationally intensive than sorting by the raw number of upvotes or weight upvotes by karma/popularity.
But I think this is a useful computation - the user can be more confident that the content they is is not astroturfed and comes from trustworthy users.
I'm building https://linklonk.com which works this way - you get content ranked based on what you upvoted. This is to make the incentives for voting aligned and help prevent abuse.
I think the problem with karma/reputation systems is that the source of karma are fungible - anyone's upvote has the same effect on the reputation. And this makes it gameable.
A personalized system can solve this by replacing global reputation with user-to-user trust. Now it matters who upvoted - a random bot or a user whose past contributions have been useful to you.
How the algorithm works: it finds people who liked the same posts as you, and shows you what else they’ve liked recently.
Launched the feed a little over a year ago and it has become the most liked feed.