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HotelsWith – Find Hotels by Amenities and Location
Hacker NewsHotelsWith – Find Hotels by Amenities and Location
Hey! Currently we have around 3000 hotels worldwide, and around 10 locations and amenities. We aim to increase this to make HotelsWith a worldwide hotel search platform that provides accurate and reliable information, including pros and cons and FAQ for each hotel. Feel free to give it a try! Simply select an amenity and a location to begin. Personally, I love looking at "HotelsWith Gym" because HotelsWith always highlights gym photos, making it easy to compare hotels.
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Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting80%80% 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, 000, io · Missing: https docs, excited, just released57%57% 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 · Strong signals: platform · Missing: plus, intuitive, reviews46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs37%37% 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, revenue13%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, crypto4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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