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I built a bookmark to fix Amazon search

Hacker News

I built a bookmark to fix Amazon search

As a heavy Amazon buyer, very often I am frustrated by Amazon search results. For instance, with so many fake reviews on Amazon, I seldom care about items with no or only a few reviews. Hide them plz. Also, pretty often Amazon shows me stuff whose names don't even match my search, come on! Why Amazon doesn't allow me to set how many items per page? I don't wanna go through pages of stuff :( Price Low to High is a joke! So many $0.01 items with $10 shipping fee! I am writing a bookmark to fix all of these. Give it a try at heartthese.com and let me know what you think.

Visit product View on Hacker NewsDec 24, 2021

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

67%67% 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.

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

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% 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

36%36% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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