ftr-site-config VS hnrss

As far as full-text caching... maybe a self-hosted instance or paid version of the FiveFilters Full-Text RSS service would work. You can integrate that into whatever aggregator you want.

However you can retrofit this onto any reader by using a service that creates a full text feed from a summary feed. Two that I have used in the past are https://morss.it/ and https://www.fivefilters.org/full-text-rss/.

RSS feeds that don't contain the full article text drive me nuts.

Here is a workaround that I've had good luck with:

https://www.fivefilters.org/full-text-rss/

In addition to improving usability, it defeats attempts to measure clickbait summary efficacy, etc., since it breaks sites' ability to pull popularity / telemetry info.

By any chance, could this be used as an alternative to the full-article RSS tool that FiveFilters offers?

Please check out FullTextRSS from Five Filters: https://www.fivefilters.org/full-text-rss/

They have an OSS version you can host yourself. It fixes the problem of sites not sharing their full text in their feed, by going and scraping the site into a full feed for you.

Full-Text RSS - FiveFilters.org

This is great!

If it's useful, I work on a project where we maintain a repository of XPath selectors for extracting article content from many different sites: https://github.com/fivefilters/ftr-site-config - they're based on the original public Instapaper rules.

We also have PDF generation, but it's not really for crawling, and wasn't created for reading on a device like the Supernote, more for printing and reading: https://pdf.fivefilters.org/simple-print/

To accomplish full-text I ended up purchasing a license for https://www.fivefilters.org/full-text-rss/, self host it and bounce it through a docker container running Tor+privproxy which generates a new circuit every 10 minutes to help avoid IP based limits on certain websites I subscribe to. I can also disable the Tor bounce per-feed if needed.

Thanks for mentioning Instant View, I hadn't come across that. We actually maintain something similar here: https://github.com/fivefilters/ftr-site-config

We use these in our own tools and also get contributions from others, including Wallabag users: https://github.com/wallabag/wallabag

Before it was sold, Instapaper used to have something similar. A public database of its site-specific extraction templates. We used that as the starting point for our repository.

If you're trying to build one yourself, have a look at the open source Readability code[1]. It was originally developed by Arc90 and is now used by Apple and Mozilla in their browser reader views. The code has been ported to a number of different languages.

I work on a service called Full-Text RSS[2] that used a PHP port of Readability, coupled with site-specific extraction rules[3] to identify and extract article content from each feed item. It then produces a full-text version of the given feed. The idea is you subscribe to the full-text version in whichever feed reader you use and it will transparently give you full-text articles where you had partial content before.

[1] https://github.com/mozilla/readability

[2] https://www.fivefilters.org/full-text-rss/

[3] https://github.com/fivefilters/ftr-site-config

That’s interesting.

I have predictive models that can predict if a headline (w/o the rest of the article and not considering the URL) will (a) get more than 10 votes and (b) if it does get more than 10 votes will the votes/comments ratio be more than 2 (which is roughly average)

The first model gets a ROC-AUC (see https://scikit-learn.org/stable/modules/generated/sklearn.me...) in the low 60’s (not good, the second model gets in the low 70’s (actually pretty good though it is a heat seeking missile for clickbait headlines) and my latest content-based recommender for RSS items gets almost 80. (I saw a paper that one system at TikTok gets about 85)

To do all that you need about 10,000 headlines and don’t get a lot of benefit from having more than 100,000. The ceilings on performance have more to do with the nature of the problem rather than my models: the same article can get submitted twice and get 0 votes one time and 200 the other time so it can never be as accurate as “is this an article about galactic astronomy?”

I had it ingest the HN comments firehose and found the amount of articles was overwhelming, my YOShInOn RSS reader now ingests the “best comments” from

https://hnrss.github.io/

together with 110 other feeds and actually I like the comments it picks out a lot. Now that the system is adding about 3000 items per day it might be able to handle a big feed like the comments firehose since now those comments are diluted with so many quality articles. For a problem like that you might want a two-score system with: (i) is it relevant? (something I like) and (ii) is it popular? (like Google’s PageRank)

I think you could make a model that compares comments in the best comments feed with other comments. I have tried formulating the problems above as regression problems where I try to predict the actual score and it does not work well because of the uncertainty problem but formulated as a classification problem for a score over a threshold it is easy to make a well-calibrated model that tells you “this article has a 20% chance of frontpaging” which is about the best anyone can do.

Subscribe via rss, so you can scratch the curiosity itch and each the FOMO, without coming to the site all the time and looking over the same things 20 times?

https://hnrss.github.io/

https://news.ycombinator.com/item?id=9491978

and this https://hnrss.github.io/

ps i’m ok with some % of false positives, but hopefully a sprinkle of OpenAI could keep that magically low?

thanks

It sounds interesting but I use https://hnrss.github.io/

Unless it had most of the features of hnrss.org I would not be able to use it.

Perhaps you could pivot your approach and submit a PR to hnrss for the feature?

Looks like there's an unmerged PR on the third-party hnrss project that would add this: https://github.com/hnrss/hnrss/pull/84

Check out below link to get a more customized, topic wise rss feeds.

https://hnrss.github.io/