A tool to find AirPods

Hacker News

http://fruitbasket.io I made the original version in December of 2016. For the past few months, I have gotten plenty of great feedback from people who were able to snag a pair. I was even mentioned on Business Insider. The URL used to be fruit-basket.surge.sh (which will still work!), but I figured fruitbasket.io was a bit easier to remember. If you are looking for AirPods (especially for Mother's Day!), go ahead and give it a shot. It's completely free to use! But if I do help you out, all I ask is that you consider donating :) This is done completely in my free time and I really only want to help people grab some awesome headphones. (Pro tip: it should work with any product sold in Apple Stores if you grab the right "item ID"!)

Visit product View on Hacker NewsMay 10, 2017

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Actual performance

2points

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Did not reach leaderboard

Launch Intel predictions

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

87%87% 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

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

67%67% 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: month · Missing: mobile apps, ios, personal

33%33% 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

26%26% 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.

Incorrect prediction on native model

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