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I am building https://linklonk.com https://linklonk.com and I think it adds a social proof to content discovery while preserving anonymity. Here is how it works

by lonk11 5y ago

I am building https://linklonk.com https://linklonk.com and I think it adds a social proof to content discovery while preserving anonymity. Here is how it works: - When you upvote an item, you connect stronger to users that also posted that item and to RSS feeds that posted this. For example, if you upvote "Post 43: Intentionally Making Close Friends — Neel Nanda"(https://linklonk.com/item/827619236936941568 https://linklonk.com/item/827619236936941568) then you will get connected to 6 users that upvoted that article and the RSS feed https://www.neelnanda.io/blog?format=rss https://www.neelnanda.io/blog?format=rss - The recommendation algorithm shows you other items upvoted by users and feeds you are connected to. - At the top of you recommendations you see content from users and feeds that you are most strongly connected to (ie, those who have posted more useful content for you). With this mechanism you discover users and feeds that post great content just by rating content, without having to know the users personally. Yet, when you see your recommendations you know that they are coming not from random people, but from people who found useful for you content before. I think, that is a more meaningful version of social proof than the aggregate counts of likes in the traditional social media.

FreeTrade 5y ago
I wonder how scalable this is - I seem to remember reddit started out with this initial idea, and when starting member.cash, it was in my mind, but maybe at the back of my mind. I guess at minimum it requires users*users rows, and an increasingly large number of updates for a well liked post for example. Also I guess you'd need to differentiate a user who indiscriminately likes/follows everything from a user who likes just the things I like.
- lonk11 5y ago
  LinkLonk does "differentiate a user who indiscriminately likes/follows everything from a user who likes just the things I like". Every time someone that you are connected to likes something, your connection to them becomes a little weaker. If you ignore recommendations of that user then you will not see what they liked in your recommendations. Very roughly, you can think of the connection strength from user A to B as "size(set of items both A and B liked)/size(set of item B liked)". So someone who likes everything increases the denominator a lot. You are right, to keep track of user to user connections is more expensive than only to keep track of the number of likes each item has received (Reddit). But ultimately someone has to track how trustworthy each of the users is to every other user. Either every user has to track it for themselves in their head. Or we can try to offload some of that work to the computer. LinkLonk is an experiment to do just that. So yes, this computation is expensive, but it is also the main value proposition.

by lonk11 5y ago

FreeTrade 5y ago

I wonder how scalable this is - I seem to remember reddit started out with this initial idea, and when starting member.cash, it was in my mind, but maybe at the back of my mind. I guess at minimum it requires users*users rows, and an increasingly large number of updates for a well liked post for example. Also I guess you'd need to differentiate a user who indiscriminately likes/follows everything from a user who likes just the things I like.

lonk11 5y ago
LinkLonk does "differentiate a user who indiscriminately likes/follows everything from a user who likes just the things I like". Every time someone that you are connected to likes something, your connection to them becomes a little weaker. If you ignore recommendations of that user then you will not see what they liked in your recommendations. Very roughly, you can think of the connection strength from user A to B as "size(set of items both A and B liked)/size(set of item B liked)". So someone who likes everything increases the denominator a lot. You are right, to keep track of user to user connections is more expensive than only to keep track of the number of likes each item has received (Reddit). But ultimately someone has to track how trustworthy each of the users is to every other user. Either every user has to track it for themselves in their head. Or we can try to offload some of that work to the computer. LinkLonk is an experiment to do just that. So yes, this computation is expensive, but it is also the main value proposition.