Imagine a system where each HN vote is weighted according to your similarity to that voter. That way a vote by people with whom I have very little in common would also be worth very little to me.
I'd love to view a HN where I don't see the highly-voted Gruber/Apple/Facebook posts but I still see the stuff about Clojure, Steve Blank, and patio11.
What I'm talking about here is uncovering "latent" communities, if you will. As in, make a giant matrix with the users being the columns and the posts being the rows and then use the eigenvectors to make recommendations (see SVD: http://en.wikipedia.org/wiki/Singular_value_decomposition)
The benefit of this approach is that I no longer have to be conscious of the topics I am filtering in or out. Even keyword based filtering is, again, a coarse estimation of relevance. I may be very interested in clojure, but I'm certainly not interested in every article that contains 'clojure' in the title.
An SVD (or similar) approach would filter my interests loosely on the co-occurrence of votes. That is, a vote from someone with whom I have high overlap is worth more to me than a vote from someone with whom I have never voted the same direction on the same post.
In any case, co-voting data is not scrape-able from the public HN site, so I think using keywords and urls is really the only realistic filtering option at this point.
Here's a screenshot of one of mine http://imgur.com/NLOkM.png
I wish more sites had that kind of filtering.
Think of it like email spam. You can setup manual filters to filter out email spam, but that is a constant and never ending stream of work for you. A simple bayesian filter like pg has described will require far less work and give far better results.
In this case, a machine learning approach is even better because it can bring up stories that a user will be very interested in even though the story would never make it to the current homepage.
Example : http://toadjaw.com/article?url=http://steveblank.com/2010/07...
Hacker News reader for mobiles, created by toadjaw, is using a scrapping script which will extract nicely the article from the linked webpage.
Still this is a very good alternative, which I'll probably end up trying :)
If a single person replies to this comment, I'll implement it.
As promised, but without the sorting or date threshold. Sorry.
Change the parameters to meet your needs. Supports min/max comments/points. Sorts by points DESC. Go play with the pipe if you want :)
Apologies that it doesn't tweet you.
I use pipes for a number of my applications, great stuff yahoo.
However, is anyone else concerned that the driver seat of HN is essentially being handed over to new users as more advanced users switch to services like this or "HN daily" or even the "/best" page? It seems like the people that don't know anything exists beyond the front page will become the only ones left to do the work of curating content.
(just speculating, nothing against new users I haven't been here that long myself...)
Part of what makes HN good is that most people hit the site and help with the voting. The more people who move away to reading HN remotely or through feeds.. the worse the voting situation gets.
Though that doesn't say much to what percentage of the 60,000 are even users or logged in.. I suspect it's not a high percentage.
Say we snap a cache frequently either centrally or locally. It would be so much easier to just diff the HN cache throughout each session with the last central cache or local cache to see what has changed significantly by highlighting or something.
Sold! Not having links to the comments when following @newsycombinator always perturbed me.