A better Reddit search engine to find menswear recommendations

Hacker News

Hey HN Community! We built this simple community search tool that basically allows people to search through thousands of past Reddit threads and tens of thousands of recommendations using tags. It mostly covers a few bigger subreddits like r/BIFL, r/MFA, r/AskMen, r/SkincareAddiction but along the way, I expanded it to include other smaller subreddits too. In addition to filtering by category, power users can also search by -Body Fit (big thigh, long torso) -Occupation (work in retail, teacher, doctor) -Age (18-24, 25-35), Size (height, weight) -Brand comparison (similar to: X) -Use (suitable for wedding, everyday wear) -Location (available in UK, use in Northeast US) -Mood/Style (minimalist, vintage, retro) Check it out. Buy fewer, buy better. Happy Earth Day!

Visit product View on Hacker NewsApr 23, 2022

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Actual performance

21points

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Launch Intel predictions

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

74%74% 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.

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

67%67% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

56%56% 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: users, way · Missing: mobile apps, ios, personal

41%41% 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

17%17% 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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