Reader – Curated Articles for You

Hacker News

Greetings, Hacker news community, Amidst the ongoing changes brought by COVID-19, short-form video content has become increasingly popular. However, many of us find ourselves watching content that doesn't add any real value to our lives. As a person who highly values quality content, I aimed to create a solution that helps people discover informative and engaging articles. Introducing Reader! When a friend of mine mentioned the struggles of finding reach for their blog, I knew I had to offer a helping hand. Reader curates articles from over a thousand sources, providing easy access to high-quality content that expands your knowledge. Moreover, Reader's personalized recommendation engine suggests articles based on users with similar interests. Give it a spin at https://reader.place and share your thoughts with us!

Visit product View on Hacker NewsFeb 23, 2023

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Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

66%66% 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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% predicted probability of success on AppSumo, 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

53%53% 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 · Strong signals: personal, video, users · Missing: mobile apps, ios, entrepreneurs

31%31% 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

12%12% 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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