Rig Feedback and Rewards Program

Thank you for your interest in Rig, an open-source Rust library for building LLM-powered applications! As Rig is still in early development, we’re eager to gather feedback from Rust developers like you.

How to Get Started

  1. Visit the Rig repo: https://github.com/0xPlaygrounds/rig
  2. Read the docs and explore the provided examples:

How to Participate

To be eligible for the $100 reward, please complete the following steps:

  1. Star the Rig repo on GitHub: Star Rig
  2. Build a sample project or use case using Rig.
  3. Share your project with your community (e.g., Twitter, Reddit, blog post).
  4. Submit your feedback using this form, including suggestions for improvement and how you plan to use Rig in future projects.
Submissions will be reviewed on a first-come, first-served basis. If your entry meets the criteria and is among the first 20 eligible submissions, we’ll contact you via email to arrange your $100 reward.

Important Details

  • Submission Deadline: August 30th, 2024.
  • Questions or Assistance: Reach out on GitHub or join our Discord.
  • Connect with Us: Follow us on Twitter and visit our website.

Thank you for participating, and happy coding! 🦀💻

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E-mailadres *
Name *
Github Username
Please describe your experience with Rust (beginner, intermediate, advanced) and provide links to any notable Rust projects you've worked on. *
Have you built any AI or LLM-powered applications before? If yes, please provide a brief description and any relevant links.
 *
Link to the repository containing your Rig sample project or use case.
 *
Please provide a concise overview of your Rig project or use case (100-200 words). Explain the problem it solves and how RIG helped you achieve your goals.
 *
Please share a link to your blog post, tweet, or other public post showcasing your Rig project.
What aspects of Rig did you find most helpful or easy to use? Please be specific and provide examples.
*
Did you encounter any challenges or limitations while using Rig? If so, please describe them in detail.
 *
How well did Rig's documentation and examples help you get started and solve problems? Is there anything you would improve? *
What types of AI or LLM-powered applications do you plan to build with Rig in the future? Please be as specific as possible. *
How likely are you to use Rig for your future Rust projects involving AI or LLMs? (Very likely, Somewhat likely, Neutral, Somewhat unlikely, Very unlikely)
 *
Verplicht
Have you used other Rust libraries for building AI or LLM-powered applications? If yes, please list them and briefly compare your experience with Rig.
 *
What improvements, new features, or integrations would make Rig more valuable for your projects? Please share any ideas or suggestions you have.
 *
Is there anything else you'd like to share about your experience with Rig or your thoughts on the library?
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