A Chrome Extension That Reads Articles Out Loud

Hacker News

I read a lot every day for work, so by the time I want to read articles online (especially here on Hacker News), I'm already too tired to stare at more text. I figured: why not listen to them instead? I tried a bunch of apps, but quickly deleted each one. Either: - Everything was locked behind a ~$30/month subscription (wasn't ready for that). - Or the voices sounded robotic and painful to listen to. Since I'm a software engineer, I decided to just build my own solution, a Chrome extension that reads articles out loud in a human-like voice. That's how Qariyo.com was born. Some friends liked it too, so I thought: why not make it public? When I built it, I kept things simple: - Read the article out loud without breaking the flow, so it should work as a widget inside the page and not opening a new tab/page. - Don't make me wait for the entire article to be converted before listening. I want to start as quickly as possible, without interruptions. - No extra fluff (Snoop Dogg reading the article for you, etc. I don’t care). Just read the article out loud. - And please, no subscriptions. I want to pay for what you use. It's still an MVP, but it works well. If you want to try it, I'd love to hear your feedback!

Visit product View on Hacker NewsSep 25, 2025

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3points

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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

78%78% predicted probability of success on Product Hunt, 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

54%54% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% 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.

Incorrect prediction on native model

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