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discussions Discussions are now open! Since the Datastar maintainers have their hands full, this is a place to keep public organized conversations about Datastar.

file docs/considerations.md is a good starting point for understanding Datastar’s production trade-offs. It’s looking for improvements - check the discussion discussion to contribute!

A collection of resources for Datastar, the hypermedia framework for building reactive web applications.

Note: This repository is not endorsed by Datastar or its authors. It is a community-driven collection of resources.

About Datastar Datastar is a lightweight hypermedia framework that enables developers to build reactive web applications using simple HTML attributes (`data-*`) and server-side logic. It eliminates the need for complex JavaScript frameworks while providing real-time capabilities, reactive frontend, and other features out of the box. **Official Links:** - 🌐 [Official Website](https://data-star.dev) - 📚 [GitHub Repository](https://github.com/starfederation/datastar/) - 🏢 [GitHub Organization](https://github.com/starfederation) - Official SDKs for Python, Java, PHP, .NET, Clojure, Rust, Kotlin, Go, Ruby, and more - 📖 [Documentation](https://data-star.dev/guide)

Organization

This repository organizes Datastar references by category. The structure and subcategories will be adapted as the collection grows. When a reference needs additional context or detailed explanation, a dedicated markdown file can be created in the /docs directory.

A specific section called Using Datastar showcases real-world production applications built with Datastar.

📚 References

AI Learning Resources

(before use, please read the official documentation at https://data-star.dev/, always double check the results, use of AI is discouraged by the datastar authors)

Libraries & Tools

Guides & How-tos

Articles & Blog Posts

Examples

Videos & Screencasts

Community Collections

Using Datastar


Contributing

Contributions are welcome! To add a reference:

  1. Add your reference to the References section in this README
  2. Include clear descriptions and links
  3. Add attribution when applicable (author, source, etc.)
  4. Hashtags are optional but encouraged for discoverability
  5. If additional context is needed, create a markdown file in /docs
  6. Version Tracking for “Using Datastar”: When submitting entries to the “Using Datastar” category, include (if possible) the Datastar version detected and the date when it was detected. This helps track framework adoption and compatibility as Datastar evolves.
  7. Submit a pull request

Community