Show HN: LinkLonk – information discovery with aligned incentives
linklonk.com
linklonk.com
I signed up and will try to find some links I like, thanks!
Though I don't know any details about Last.fm's algorithm. Most likely it is based on neural nets, which are opaque to the user. I think this is totally fine for music recommendations. In that domain the are few adversaries and the cost of a bad recommendation is just a skipped song.
For information content I feel the stakes are higher and there are more bad actors who would love to grab our attention. That's why I think it is important to have a transparent algorithm.
And yes, LinkLonk does find your "neighbors" - people that like the same content as you did. What is more, LinkLonk connects you to those people, that liked that content before you did. And they are likely to be more informed than people who upvote the same content after you upvoted it.
Since we still have few users I would suggest to start using LinkLonk as a tool for bookmarking interesting content and following RSS feeds. Hopefully this would make it useful enough before the number of users reaches a critical mass for the network effects to kick in.
2. We follow groups of people (HN/Reddit/forums) and it helps us connect to people that we wouldn’t otherwise be able to discover. But you get a mix of high signal-to-noise members and the more-noise-than-signal members. The bigger a group becomes the worse this mix usually gets.
3. We use algorithmic systems to bring us useful information. But these systems, powered by deep neural net models, do not truly understand what is useful to us. Instead they are optimizing for "time-spent" (ie, ads shown) by showing you more shallow content that will keep you clicking and yet never satisfied. They are opaque by design and are not something that we can control with our actions. Instead of offering explicit control to the users they are focusing on implicit signals such as what we clicked on before - a poor signal.
Can we get help discovering sources to follow?
Can we take only the good parts of a group and not the bad parts?
Can we have agency in an algorithmic system?
With LinkLonk I am building a system that combines the best parts of the three systems and addresses their weak points.
To start, LinkLonk is an algorithmic system, but the algorithm is transparent to the user and the algorithm’s output directly depends on what content you upvote and downvote.
When you upvote a piece of content, LinkLonk strengthens your connection to other users that upvoted that same item.
When you downvote something, LinkLonk weakens your connection to users who upvoted it.
How strongly you are connected to a user determines how high their next upvoted items will rank in your list of recommendations. It means that at the top of your recommendations you will see content from users who have been good at finding useful content for you in the past.
This creates a feedback loop with you in control.
Initially, when you have not rated any content yet, LinkLonk connects you to all users with a very weak connection. As a result, your initial set of recommendations is based on popularity. It behaves like a group-based system at the start. But as you rate content you get connected to specific users - the ones that have the highest signal-to-noise ratio for you. This makes it more similar to the “following individual sources'' type of system yet it solves the problem of discovering the sources to follow.
When you downvote something, not only does LinkLonk weakens your connection to those who upvoted that content, LinkLonk also strengthens your connection to the users who also downvoted that content. It means that their other downvotes will have more weight for you. The idea is that if they were able to recognize bad content in the past then they could be trusted to flag bad content in the future.
LinkLonk creates a new system of incentives. In order to get your attention, other users need to prove to be good curators of content. And to hide content, they need to prove to be good moderators. It’s these new incentives that excite me about this project.
Now we need a few users to test out this system. Give it a try, submit a link you found useful.
To create an account you don’t have to submit your email address. Use "Continue as guest" to create a temporary guest account. It will use a cookie on your local browser. You can later convert it into a permanent account that you can access across devices. If you don’t use your guest account for >30 days it will be completely deleted from the server.
P.S. This is my hobby project that I am building in my spare time. My stack is: PostgreSQL + Golang server (sqlc, gorilla/mux) + Angular client + Firebase for auth. It runs on a small VPS instance on OVH that costs ~$12/month. I’m intending to run it for years. That’s the nice part about being a hobby project - there is no time/financial pressure to "succeed".
If you read every issue of the Money Stuff newsletter and upvote 5 out of 10 that are most informative and then if I upvote 2 of those 5 that you upvoted then I would get connected to you and to the feed of the newsletter (if such feed existed, I don't seem to find an RSS feed for it). But because your signal to noise ratio is "2 out of 5" and the feeds ratio is "2 out of 10", then my connection to you will be stronger. And so I will see your recommendations first. That is, I will get a prioritized view of Money Stuff issues and I will benefit from you work as a curator.
To get a sense of what recommendations you would get if you upvote a given item, you can go to the items detail page (three vertical dot button in bottom right of every item -> Item page). There you will see recommendations from the users and feeds that upvoted/posted that content. For example, if you go to https://linklonk.com/item/7470250776832704512 (a page for https://arstechnica.com/cars/2021/08/please-stop-adding-more...) then you will see what else was recommended by 6 users and 2 feeds that upvoted/posted that article from Ars Technica.
Here is an example of a Substack newsletter issue: https://linklonk.com/item/2348076191098765312 which has this feed url: https://semianalysis.substack.com/feed and if you scroll down on the item detail page you will see other issues of that newsletter in the item-based recommendations
If there is feed url in the metadata, but you know the feed url, then you can submit (linklonk.com/submit) the feed url directly and LinkLonk will connect you to it.