SearchHN: Redefining the Hacker News Experience

Hacker News

Hello HN, Excited to share a revamped browsing experience for Hacker News enthusiasts! I've redesigned the interface for improved usability and aesthetics, optimized readability, and lightning-fast performance. Check it out on GitHub: https://github.com/hharpreetk/react-ts-hacker-news-feed and experience it live on Netlify: https://search-hacker-news-feed.netlify.app/ Your feedback and contributions are welcomed!

Visit product View on Hacker NewsFeb 28, 2024

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Actual performance

1points

4comments

Did not reach leaderboard

Launch Intel predictions

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

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

nativeThis product was originally launched on this platform.

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% 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

11%11% 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

10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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