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"Zeus Living” for short getaways from urban cities in India

Hacker News

"Zeus Living” for short getaways from urban cities in India

Hi HN, I'm Arjun and one of the co-founders of Kipstay (https://www.kipstay.com/).We design and rent homestays for short getaways that are 2-5hrs away from urban cities in India. In India, booking homestays as an alternative to hotels is frustrating on many levels - the experience is unpredictable, no transparency in pricing, inaccurate listing information and photos and trust issues. We simplify the process for homeowners to upgrade their homestays to a higher standard and rent them out by the night on our website. City dwellers/travellers get getaway homes that are affordable ($50 per night), accessible (2-3 hrs or fewer from the city), and at ease (secluded and serene).

Visit product View on Hacker NewsAug 16, 2019

Actual performance

2points

2comments

Did not reach leaderboard

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79%79% 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.

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

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

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

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

20%20% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

13%13% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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