Made a text-only article reader extension to reduce eye strain

Hacker News

Many articles today are difficult to read, cluttered with ads, pop-ups, and distracting layouts that make focused reading exhausting. I built a Chrome extension that turns articles into text-only reading views using Mozilla's Readability library with custom CSS informed by accessibility and readability research. It follows four design choices based on W3C WCAG 2.2 standards: a sepia background to reduce eye strain, 1.5× line spacing for better readability, 50–75 character line length to reduce eye fatigue, and sans-serif fonts that work well on screens. Shortcut key: Alt+Shift+Y or Command+Shift+Y Limitations: it's designed for reading plain text articles. Because it hides images, tables, and formulas, some pages may lose important context or meaning. Github (Feel free to contribute! ) : https://github.com/uscne/Yumi-Reader

Visit product View on Hacker NewsOct 22, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, using, plain · 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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

20%20% 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

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