Search top product recommendations from Reddit

Hacker News

I wanted to share a side project that stemmed from a personal frustration many of us may relate to - the struggle to find reliable recommendations for non-serious products like coffee beans or running shoes. Google search results are often cluttered with clickbait articles and biased reviews, making it challenging to get straightforward and trustworthy advice. To tackle this issue, I've created Reddit Product Search - a simple yet effective tool designed to cut through the noise and deliver recommendations straight from the Reddit community. The concept is straightforward: it scours relevant Reddit posts and presents recommendations that have received high votes, ensuring that you get insights from a community consensus rather than relying on algorithmic search results. I have to say currently the website is a bit slow and might be a tad flaky (currently taking around 5 minutes for results). I've cached few searches in advance. I'm reaching out to the Hacker News community to gather feedback, suggestions, and insights to improve and refine the tool. Your input is invaluable, and any feedback is highly welcomed!

Visit product View on Hacker NewsNov 17, 2023

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

80%80% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

Hacker NewsStrong engagement from HN community · Strong signals: lua, hacker news, ide · 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, google · Missing: mobile apps, ios, entrepreneurs

35%35% 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.

Incorrect prediction on native model

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