My first "full-stack" project, Cinematic

Hacker News

http://cinematicapp.com Why build another movie showtimes app? Because other apps are cluttered with a lot of ads, irrelevant info (crew listings?), and take a lot of taps to actually get at the good stuff, the showtimes! I made what I wanted: a fast, simple, no-frills movie showtimes app. Additionally, this is my first completed, full-stack project. The showtimes are scraped and fed using an API delivered through a Sinatra app hosted on Heroku. The app is 100% designed and programmed by me, and uses no images in-app, except the navigation bar. Try it out and let me know what you think.

Visit product View on Hacker NewsJan 10, 2012

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3points

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

65%65% 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.

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

57%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.

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

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

36%36% 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 · 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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