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Readfast – AI-Powered Summaries for Faster Web Reading

Hacker News

Readfast – AI-Powered Summaries for Faster Web Reading

Hi everyone, I’m excited to share Readfast, a Chrome extension I built to help tackle information overload. With one click, Readfast uses AI to quickly summarize articles from any website, helping you focus on what matters most—whether you’re a student, researcher, or just trying to keep up with the news. Key Features: - One-Click Summaries: Instant AI-powered condensation of long-form content. - Versatile: Works across news sites, blogs, academic papers, and more. - Customizable Experience: Choose your preferred summary length, language, and format (like bullet points). - Efficiency: Save time and decide in seconds if you want to dive deeper into an article. I’m in the early stages, so I’d really appreciate any feedback, feature suggestions, or bug reports you might have. Check out [my landing page/GitHub link] for more details and to try it out. Thanks for your time and happy reading! — Leng

Visit product View on Hacker NewsMar 29, 2025

Actual performance

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

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best fitHighest predicted score across all platforms for this description.

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

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

25%25% 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

17%17% 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

13%13% 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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