Comments URL: https://news.ycombinator.com/item?id=47870458

Points: 3

# Comments: 1

The For You one uses only your likes: it finds people who liked the same posts as you, and shows you what else they've liked recently.

Comments URL: https://news.ycombinator.com/item?id=44926897

Points: 3

# Comments: 1

As an example, I built a "For You" feed https://bsky.app/profile/did:plc:3guzzweuqraryl3rdkimjamk/fe... that finds the posts you liked, finds other people who liked the same posts and shows you what else they liked.

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.

The recurrent approach is more efficient when memory bandwidth is the bottleneck. They talk about it in the paper.

Comments URL: https://news.ycombinator.com/item?id=41547551

Points: 1

# Comments: 0

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.

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.

You subscribe to tens or hundreds of feeds and, boom, you have another problem - how do you prioritize which feed to read .

With https://linklonk.com I'm trying to solve both problems: discovering feeds to follow and prioritizing content from all feeds.

You start with content you liked - submit links you liked and you will get connected to all feeds that included this link.

For example, there are a bunch of feeds that included this link https://simonwillison.net/2024/Feb/21/gemini-pro-video/

Those are:

- https://simonwillison.net/atom/everything/ - the original blog

- https://kagi.com/api/v1/smallweb/feed/ - a feed of "small web" links, I didn't know it existed, but one of the users must have submitted this feed.

- https://hnrss.org/newest?points=1000&count=100 - HN links that got more than 1000 points

- https://lobste.rs/rss - submissions to Lobste.rs

- https://lobste.rs/t/ai.rss - submissions to Lobste.rs with "ai" tag.

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.

LinkLonk differs in these two aspects:

1. The algorithm is intentionally simple - like content to get more from that publisher (ie, RSS feeds) and from other users that liked it. Dislike - to get less. There is no AI so that you as a user could have control. For example, LinkLonk does not use your view history to guess what else you would like.

2. The list of sources is any RSS feed users have added. LinkLonk also automatically tracks any feed that posted content users liked. In this respect LinkLonk is more similar to RSS readers.

Passing the parameters from the handler to the template could be left to the user of the library.

I would make the templates library take a callback function for every public template: func(method, path string, template).

Then the user could add any custom logic to register the route handler and execute the template with any application specific inputs.

I liked the idea of defining the handle path in the name of the template and the auto-reloading on change functionality.

I wish this was a stand-alone library that I could use in my custom Golang server. Do you know if something like this exists?

Comments URL: https://news.ycombinator.com/item?id=36750716

Points: 452

# Comments: 170

Maybe it's a test to see how many power users would convert to verified?