[linklonk]

I am working on an information system (similar to HN or Reddit) with the focus on maximizing usefulness of information (ie, maximize the ROI of your attention).

You can try it out with a temporary account at https://linklonk.com with invitation code "hn".

Similar to Reddit/HN users submit links and vote on them. The difference is how the votes are used. When you upvote something that was worth your time the system connects you to other users who upvoted it. These are the people who deserve your attention since they have been able to recognize it before you did.

The stronger you are connected to someone - the more weight their future upvotes have for you (ie, their upvoted items show up higher in you list).

Instead of you figuring out what is worth your attention and who to trust, the system takes care of it for you. It keeps track of the signal-to-noise ratio of every user and every RSS feed and then ranks content for you accordingly. All you need to do is:

1. upvote stuff that was worth you time - to connect to good content curators

2. downvote stuff that wasted you time - to disconnect from bad content curators.

This creates a feedback loop that brings you content that is worth your attention. The important part is that it uses your definition of "worth your attention" - whatever you upvoted. You are in control.

Another difference is the pace updates.

Reddit/HN demote items very quickly based on the exponential time-decay component in the ranking score.

On LinkLonk you don't have to keep up with the constantly changing feed. The system shows you the top-20 recommendations and waits for you to mark them as read. Then you get your next top-20 that you have not seen yet. It works at your pace.

[Bridgeburner4]

I've always wanted to explore up/down/voting in a 'chain of trust' kind of way so your project looks interesting.

Personally, I would not use a system like this unless I could vote by category. E.g. I may not trust someone on gardening tips even when I place their taste in music very highly and I would enjoy seeing both topics equally.

[linklonk]

That's a great point! LinkLonk has "collections" for this (similar to "boards" on Pinterest). When you upvote something you put that item into a collection. Every user starts with the "default" collection.

But you can create new ones for each of you distinct areas of interest.

For example, you may want to create a collection called "music" and put music links there. When someone upvotes a link that you also upvoted in "music" then they will only connect to your "music" collection, and not your other collections. LinkLonk tracks the trust at the level of collection-to-collection (not user-to-user).

Personally, I put all general interest stuff into the "default" collection, Machine Learning related links into "ML", movies into "movies".

So far I described how organizing what you liked into collections helps others (ie, they get more focused recommendations from you). Why would you want to do this organization in the first place?

There are a couple of reasons:

- When you go to the history of your ratings (https://linklonk.com/ratings) you can filter it by the collection you put it into. This helps you find the article you liked. It is kind of a bookmarking service this way.

- Normally, the recommendations you see are for all of your collections. But you can filter your recommendations to see only items for a specific collection (e.g., music).

Finally, it is not much effort to keep your likes organized once you get started. LinkLonk knows which collection every recommendation is closest to and when you upvote something it will most likely be added to the right collection automatically. For example, if I liked a blog post about an ML topic and put it into "ML", then the future posts from that blog will go to "ML".

[tiddles]

That sounds really interesting! I'll definitely keep an eye on this.

[linklonk]

Thanks! I'm planning to make a "Show NH" post soon. Please let me know if you have any suggestions or any issues that I could address before that.

There were ~40 new accounts created from this thread. I didn't really expect that many. That's encouraging.

One thing I noticed is that all of the new users skipped the "Welcome" screen which asks users to enter three links they liked recently. So I will likely remove it before doing the "Shown NH".