What do you think of Shopaholic (sale items on your new tab page)?

Hacker News

Hey guys, I just launched a demo yesterday for Shopaholic, a project I've been working on recently, and now I'm looking to validate the concept. Check it out here: http://www.getshopaholic.com It's a Chrome extension that asks you to follow your favourite clothes stores then turns your new tab page into a feed of sale items from those stores. Very simple idea but it's had a warm reception in its first 24 hours with a couple of hundred signups. I'd be very keen to hear your feedback. Thanks very much!

Visit product View on Hacker NewsApr 11, 2014

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

63%63% 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

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

41%41% 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 · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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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