readability VS hnrss

I'm working with the Mozilla's readability library https://github.com/mozilla/readability to get the "readable" text from articles and now I want to style the extracted text in a readable way.

Do you remember the Firefox Reader View? It's a feature that removes all unnecessary components like buttons, menus, images, and so on, from a website, focusing on the readable content of the page. The library powering this feature is called Readability.js, which is open source.

I wonder if Firefox "reader mode as a utility" might be a viable alternative for Pinboard like "content oriented" archiving?

https://github.com/mozilla/readability

Depending upon the type of content, one might want to look into using the Readability (Browder's reader view) to parse the webpage. It will give you all the useful info without the junk. Then you can put it in the DB as needed.

https://github.com/mozilla/readability

Btw, readability, is also available in few other languages like Kotlin:

https://github.com/dankito/Readability4J

Use Mozilla's Readability to extract that sweet, sweet text content from webpages.

Same as above but the ability to grab the entire article text (you can use the Readability library for that: https://github.com/mozilla/readability)

// Original script // https://gist.github.com/kepano/90c05f162c37cf730abb8ff027987ca3 // Bookmarklet Converter // https://caiorss.github.io/bookmarklet-maker/ // Libraries // https://github.com/mixmark-io/turndown // https://github.com/mozilla/readability javascript: Promise.all([import('https://unpkg.com/[email protected]?module'), import('https://unpkg.com/@tehshrike/[email protected]'), ]).then(async ([{ default: Turndown }, { default: Readability }]) => { /* Optional vault name */ const vault = ""; /* Optional folder name such as "Clippings/" */ const folder = "Clippings/"; /* Optional tags */ const tags = ""; function getSelectionHtml() { var html = ""; if (typeof window.getSelection != "undefined") { var sel = window.getSelection(); if (sel.rangeCount) { var container = document.createElement("div"); for (var i = 0, len = sel.rangeCount; i < len; ++i) { container.appendChild(sel.getRangeAt(i).cloneContents()); } html = container.innerHTML; } } else if (typeof document.selection != "undefined") { if (document.selection.type == "Text") { html = document.selection.createRange().htmlText; } } return html; } const selection = getSelectionHtml(); const { title, byline, content } = new Readability(document.cloneNode(true)).parse(); function getFileName(fileName) { var userAgent = window.navigator.userAgent, platform = window.navigator.platform, windowsPlatforms = ['Win32', 'Win64', 'Windows', 'WinCE']; if (windowsPlatforms.indexOf(platform) !== -1) { fileName = fileName.replace(':', '').replace(/[/\\?%*|"<>]/g, '-'); } else { fileName = fileName.replace(':', '').replace(/\//g, '-').replace(/\\/g, '-'); } return fileName; } const fileName = getFileName(title); if (selection) { var markdownify = selection; } else { var markdownify = content; } if (vault) { var vaultName = '&vault=' + encodeURIComponent(`${vault}`); } else { var vaultName = ''; } const markdownBody = new Turndown({ headingStyle: 'atx', hr: '---', bulletListMarker: '-', codeBlockStyle: 'fenced', emDelimiter: '*', }).turndown(markdownify); var date = new Date(); function convertDate(date) { var yyyy = date.getFullYear().toString(); var mm = (date.getMonth()+1).toString(); var dd = date.getDate().toString(); var mmChars = mm.split(''); var ddChars = dd.split(''); return yyyy + '-' + (mmChars[1]?mm:"0"+mmChars[0]) + '-' + (ddChars[1]?dd:"0"+ddChars[0]); } const today = convertDate(date); // This is the output template // It is similar to an Obsidian core template // except to insert a value we use: ${value} instead of {{value}} const fileContent =`--- type: clipping date_added: ${today} aliases: [] tags: [${tags}] --- author:: ${byline.toString().split('\n')[0].trim()} source:: [${title}](${document.URL}) ${markdownBody} `; // This copies your text to the clipboard navigator.clipboard.writeText(fileContent); // This creates a new document in Obsidian containing your clipping // I commented it out as this isn't what you asked for /* document.location.href = "obsidian://new?" + "file=" + encodeURIComponent(folder + fileName) + "&content=" + encodeURIComponent(fileContent) + vaultName; */ })

So first part of the task is to "extract the text from URL", and that is achieved by using descendant of https://github.com/mozilla/readability library which can extract text of any URL.

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/