feedparser VS hnrss

There is JSON Feed¹ already. One of the spec writers is behind micro.blog, which is the first place I saw it(and also one of the few places I've seen it). I don't think it is a bad idea, and it doesn't take all that long to implement it.

I have long hoped it would pick up with the JSON-ify everything crowd, just so I'd never see a non-Atom feed again. We perhaps wouldn't need sooo much of the magic that is wrapped up in packages like feedparser² to deal with all the brokeness of RSS in the wild then.

¹ https://www.jsonfeed.org/

² https://github.com/kurtmckee/feedparser

What sites did you try? Looked into RSS yet? Many sites have RSS feeds you can use with something like https://github.com/kurtmckee/feedparser nytimes.com feeds: https://www.nytimes.com/rss

feedparser

You can explore FeedParser too

feedparser is a python package receiving and parsing RSS/Atom newsfeeds. The maintainer is active but really need much more support.

Lo más sencillo que conozco para monitorizar canales de YouTube son los feeds RSS que tiene cada canal. El formato es https://www.youtube.com/feeds/videos.xml?channel_id=[CHANNEL_ID]. Si no conoces RSS, echa un vistazo en la wiki. Para leer RSSs en Python tienes feedparser (y seguramente muchas más).

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/