Building a streaming platform with Cursor AI & Hygraph

Imagine a world where coding is enhanced by artificial intelligence, streamlining your development process and boosting productivity. This is the reality with AI-powered code editors like Cursor AI. These tools are changing the way developers work, offering intelligent code suggestions, automated refactoring, and even generating entire code blocks on demand.

In this blog post, we'll explore the concept of AI-powered code editors and how they're transforming the software development landscape. We'll use the example of building a Netflix-like streaming platform to illustrate the potential of these tools, focusing on how they can simplify complex tasks and accelerate your workflow.

#Why Cursor AI is a Game Changer

Traditional coding methods often involve rigorous manual coding, especially when setting up the initial project structure. Cursor AI changes this by automating substantial parts of the coding process, making it not just a code editor but a code generation tool. This new technology can lead to faster development times, fewer bugs, and more time for developers to focus on complex problems rather than boilerplate code.

Productivity Boost

Imagine writing extensive boilerplate code with a few keystrokes. That’s precisely what Cursor AI aims to achieve. By integrating AI models into your development workflow, it becomes possible to speed up the coding process significantly.

Seamless Integration

Since Cursor AI mirrors your existing VS Code setup, including themes and extensions, you don't have to worry about a steep learning curve. It fits right into your existing workflow, making the transition smooth.

#Setting Up Hygraph

This demo uses Hygraph’s Movie Platform starter as it’s data set. If you’d like to follow along with this article, you can clone the data and project from Hygraph’s Marketplace.

#Writing the Initial Prompt

With Cursor open, invoke the chat prompt using Command + L or Control + L. This is where you'll input commands for the AI. Each time you generate code, the result could be slightly different, but here's an example prompt that worked well for us:

When starting with a blank project, it's crucial to be as explicit as possible in your prompt. This helps Cursor AI generate cleaner, more accurate code. If you're working with an existing project, Cursor has more context to work with, potentially improving the results. However, without that built-in context, we need to provide as much detail as possible in our prompt. Here's the prompt we used:

Make a Next.js project with Tailwind that is designed to look like Netflix

This project should have a homepage and a dynamic route pulling data from a Hygraph endpoint with a GraphQL query that looks like this:

query MyQuery {
  movies {
    title
    movieInfo: federateMovie {
      data {
        Title
        Plot
        Director
        Genre
        Runtime
        Poster
      }
    }
    imdbId
    slug
moviePoster {
      url
    }
  }
}

The homepage should have a card style for each movie

Each movie page should check for a moviePoster URL and if it doesn't exist use the movieInfo image instead

The project should be set up to use images from either *.graphassets.com or m.media-amazon.com domains 

This should use the App router and not use getStaticPaths

Once the prompt is entered, Cursor AI will begin generating the code for you.

After a little troubleshooting, we were able to run the Next.js implementation and get a working site. The AI-generated code provided a solid foundation, which we then refined to address the minor issues mentioned earlier.

#Troubleshoot Issues with Cursor AI

While Cursor AI is a powerful tool, it's important to note that it may not always produce perfect code on the first try. In our experience, we encountered a couple of issues that required additional prompting and debugging:

• The Next.js configuration had an incorrect export statement.
• The image domain configuration used the deprecated 'domains' property instead of the more stable 'remotePatterns' option.

When you encounter such issues, the key is to communicate these errors back to Cursor AI. By providing clear, specific prompts about the errors you're seeing, you can guide the AI to generate the correct code. This iterative process of generating, testing, and refining is an essential part of working with AI-assisted coding tools.

#Conclusion

This process highlights an important aspect of working with AI-powered coding tools: while they can significantly accelerate development, they still require human oversight and occasional adjustments.

Here's a brief overview of what we achieved:

• A functional Next.js project structure
• Integration with Hygraph for content management
• A Netflix-inspired UI using Tailwind CSS
• Dynamic routing for individual movie pages
• Proper handling of movie poster images from multiple domains

This experience demonstrates the potential of AI-assisted coding in rapidly prototyping complex applications. It also underscores the importance of developers' expertise in fine-tuning and optimizing the generated code.