A heuristic for movie influences

Hacker News

I've posted here before with no considerable traction, something I ascribe to my lacking effort (and interest) in frontend. But here goes: I am a film nerd, and I like to understand the context of art. So I developed an algorithm to find aesthetic and thematic reference points for any given film, based on reviews by critics. It performs reasonably well where expected to (mid-budget, dramas, stuff with artistic ambitions) and less well for very obscure stuff or blockbuster fare. This is obviously not a commercial service, I built it simply to develop my own film literacy. Some examples: http://cinetrii.com?i=tt2278388 (The Grand Budapest Hotel) http://cinetrii.com?i=tt2872718 (Nightcrawler) http://cinetrii.com?i=tt4062536 (Green Room) If you're on desktop you can click the nodes for quotes by critics. Mobile version displays quotes directly.

Visit product View on Hacker NewsJan 4, 2017

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

60%60% 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.

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

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

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

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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37%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, revenue

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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