This One – Discover Things You Truly Love

Hacker News

Hey HN! My name’s Priya and I’m helping to build a company with the mission of helping people discover things they truly love, starting with movies. The problem: On average, it takes someone 19mins to find the right film for the mood, and most of the time that film is not the right one, with platforms optimising for other things. Our solution: We’ve built an iPhone app that asks you what you love, identifies the "vibes" within your taste, and recommends movies that align with those vibes. To download: Download the 'Testflight' app then click this link: http://www.this.one/hackernews 1m product demo: https://youtu.be/jHCbNvmzW6A Our team previously helped build music-discovery at Spotify, and we’re now creating this app to help movie-lovers discover and discuss awesome films. We’re still super-early, and we’re psyched to be sharing with HN! We'd be very grateful for any feedback and questions are massively appreciated - either on HN, or you can reach me via email: priya@this.one. Thanks in advance!

Visit product View on Hacker NewsApr 12, 2023

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1points

Did not reach leaderboard

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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.

best fitHighest predicted score across all platforms for this description.

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

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

nativeThis product was originally launched on this platform.

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

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% 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, 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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