Unclutter — A new approach to reader mode

Hacker News

Hey! This is a reader mode browser extension I built that hides noisy page elements rather than extracting and re-rendering only the page text. The idea is to not make all articles look the same [0], have them still render graphs, and ideally to work in more cases. There are a few "tricks": patching the site CSSOM to apply simpler mobile styles even at desktop width, detecting the likely main text & removing its non-text siblings, blocklists for classnames that contain words like "sidebar", and testing this on a few hundred popular sites. I got carried away and also added a dark mode, page outlines, private annotations & inline Hacker News comments. The last feature works by parsing every top-level HN comment with a quote in it (formatted with > or "") within a few minutes, and anchoring these quotes in the related article HTML. So when you click a link on HN you’ll see the parts people are talking about while reading. [1] The code is all on GitHub! [0] Screenshots comparing it to the Firefox reader mode: https://github.com/lindylearn/unclutter/blob/main/docs/compa... [1] It's fun to try this on some of the "HN classics" that got 30+ quote comments over the years. Another project I built, https://hn.lindylearn.io/best shows the number of "annotations" an article has beneath its title.

Visit product View on Hacker NewsJun 18, 2022

Share card

Actual performance

49points

14comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: new, code · Missing: mac, agents, macos

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Unclutter – Reader mode, but better73%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Unclutter – Reader mode, but better

Hacker News212

Reader Mode – A fully cuztomizable “Reader Mode” with dyslexia support63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Reader Mode – A fully cuztomizable “Reader Mode” with dyslexia support

Hacker News13

A brutalist HN reader60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A brutalist HN reader

Hacker News1

I made a reader for HN with Angular48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made a reader for HN with Angular

Hacker News6

Economist Espresso Reader57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Economist Espresso Reader

Hacker News1

RFC Reader57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

RFC Reader

Hacker News1

A Reader for Programmers50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A Reader for Programmers

Hacker News1

Dirty Reader54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Dirty Reader

Hacker News2

Reader Mode47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

All-in-one reading, bookmarking, highlighting & research app

Indie Hackers40b2c

attract mode likes56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

attract mode likes

Hacker News2

Browse all products Analyze your launch