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What is WatchThis.dev?

WatchThis.dev is an AI-powered recommendation tool that suggests films and television shows based on your viewing preferences. It uses OpenAI's language models to understand what you like and generate personalised suggestions. The platform is built on Vercel Edge Functions for fast performance. Beyond just getting recommendations, you can explore how the system works under the hood. The source code is available on GitHub, so developers can deploy their own version or study the implementation. It's useful if you're tired of endless browsing and want smart suggestions tailored to your taste.

Key Features

AI-powered recommendations

Uses OpenAI to generate personalised show and film suggestions based on your preferences

Edge-based performance

Built on Vercel Edge Functions for quick response times

Open source

Full source code available on GitHub for review and self-deployment

Preference learning

Understands your viewing habits to improve recommendation quality

Transparency

See how the recommendation engine works and what data it considers

Pros & Cons

Advantages

  • Free access to core recommendation features without requiring payment
  • Source code is publicly available, so you can run it yourself or contribute improvements
  • Uses modern, reliable infrastructure from Vercel and OpenAI
  • Simple interface focused on the core task of finding something to watch

Limitations

  • Limited information available about customisation options or depth of preference learning
  • Free tier may have usage limits or feature restrictions not clearly documented
  • Relies on external APIs, so service availability depends on OpenAI and Vercel

Use Cases

Finding your next film or series when you're unsure what to watch

Discovering new shows in genres you enjoy but haven't explored deeply

Learning how AI recommendation systems work by studying the open source code

Running your own recommendation service by deploying the code to your infrastructure

Gathering data about how AI-powered suggestions differ from traditional recommendation algorithms