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Long Take
Quiet
#9297 Radar 23

A movie recommendation service that picks one film for you each night.

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1/5
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Product memo

Long Take targets discerning film enthusiasts overwhelmed by choice. Unlike generic recommendation engines, it focuses on a single, highly personalized pick per night, leveraging AI to match taste, predict scores, and surface critics with similar preferences. This deliberate focus on quality over quantity and personalized decision-making aims to cut through the noise of streaming services.

For who

Movie lovers seeking personalized recommendations

Solves what

Eliminates decision fatigue by picking one film per night matched to user taste.

  • AI-powered film selection
  • Personalized taste matching
  • Predicted score & critic insights

In their own words

Stop choosing. Start watching.

Long Take doesn’t hand you a list. It picks one film for tonight — matched to your taste, on the services you already pay for. You just press play.

CTA: Start 7-day free trial

Commercial cues

Pricing snapshot subscription with 7-day trial

Model

subscription

Free tier

No

Trial

7d

No public pricing tiers captured.

Pricing Strategy

Key Tactics
  • Generous free tier for core functionality
  • 7-day free trial for paid tier

Operator context

Operating setup

Platform

Web app

Audience

Consumers

Tech stack

SvelteSvelteKit

Builder Strategy

Strategy Type
Niche Specialist
Stage
Pre Revenue
Effort
Solo Buildable
About Long Take Expand

Long Take is a film and show recommendation service designed to end decision paralysis. Instead of presenting a wall of tiles, it intelligently selects a single movie each night, tailored to your unique taste and available on your preferred streaming platforms like Netflix and MUBI.

The service learns your preferences through a quick rating process, providing a predicted score, a match percentage, and insights from critics who share your cinematic palate. This approach ensures you spend less time choosing and more time watching films you'll genuinely enjoy, moving beyond generic popularity metrics to offer truly personal recommendations.