First startup, first app: Kwixer

Hacker News

Greetings ! https://www.kwixer.com We’re two freshly graduated students and we’ve developed our first smartphone app called Kwixer with 0 funds! Kwixer enables you to share and review what you do like what movies you’ve watched, what songs you’ve listened to, what books you’ve read, what video games you’ve played and which restaurants you went to. Kwixer is unique because of its social recommendation engine, let’s say you’ve watched “Skyfall” and loved it; Kwixer will not only recommend you other James Bond movies or other action movies your friends loved but will also recommend theme songs from the movie like the song “Skyfall” from Adele. We cross reference a lot of data so you’d discover the things you’re about to love. This is our first app and first version; we’ve developed on iOS and windows phone and working on the android app, we know we need to do a lot of UX improvements to make it simpler, please give us a lot of feedback and let us know what you think! Our data comes mostly from Freebase, iTunes and Foursquare. Both our apps are native, we built our own transitions we wanted to feel different so let us know ;)...And finally our backend is developed with .net.

Visit product View on Hacker NewsJan 4, 2013

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios, songs · Missing: supports, reddit linkedin, podcasting

89%89% 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.

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

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs

56%56% predicted probability of success on TrustMRR, 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

44%44% 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

30%30% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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