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Instant Beer, Wine and Spirits Delivery

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Instant Beer, Wine and Spirits Delivery

Hello HN, My name is Freddie and I'm the creator of a little beer loving robot named Distributron. We had the pleasure of discussing our business with Geoff Ralston yesterday and let me tell you it was a VERY humbling experience. We didn't make it into YC this time around but we will next time. We're currently only operating in Central PA in our beta so you'll need to use an address from that region to see what we have to offer for delivery and preview our service. Just use: 1 S. Union Street, Middletown, PA 17057 Marketplace: https://distributron.app Let me know any constructive criticisms but keep in mind I built this instant delivery service in a little over a month. While it's not perfect we're looking to make it that way and spread our success across the globe. Thank you YC for everything!

Visit product View on Hacker NewsDec 3, 2020

Actual performance

7points

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Launch Intel predictions

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

75%75% 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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · 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.

TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal

47%47% 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

29%29% 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

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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