Racket vs Julia
Side-by-side comparison for macOS
Racket
8.0Modern programming language in the Lisp/Scheme family
Julia
8.0Programming language for technical computing
| Metric | Racket | Julia |
|---|---|---|
| Category | Developer Tools | Developer Tools |
| AI Score | 8.0 | 8.0 |
| 30-day Installs | 121 | 88 |
| 90-day Installs | 401 | 215 |
| 365-day Installs | 1.9K | 1.1K |
| Version | 9.1 | 1.12.6 |
| Auto-updates | No | No |
| Deprecated | No | No |
| GitHub Stars | 5.1K | 48.6K |
| GitHub Forks | 686 | 5.8K |
| Open Issues | 590 | 4.7K |
| License | NOASSERTION | MIT |
| Language | Racket | Julia |
| Last GitHub Commit | 2mo ago | 1mo ago |
| First Seen | Sep 25, 2012 | Jun 23, 2013 |
Reviews
Racket
Racket is a modern programming language in the Lisp/Scheme family, offering a flexible and expressive environment. It features macros, multiple dialects, and a robust ecosystem, making it ideal for developers, educators, and researchers seeking a powerful tool for their projects.
Racket provides a programming language with a focus on expressiveness and flexibility, enabling developers to create a wide range of applications and explore novel programming concepts.
Pros
- + Rich ecosystem with a variety of libraries and tools
- + Expressive and flexible language with support for macros
- + Active and engaged community contributing to its development
- + Multiple dialects catering to different programming paradigms
- + Extensive educational resources and documentation
- + Cross-platform support, including macOS, Linux, and Windows
Cons
- - No auto-update feature, requiring manual updates
- - Some contributors have reported issues with the community, leading to decreased participation
Julia
Julia is a high-performance programming language designed for technical computing, data science, and machine learning. It offers a unique blend of high-level language features and speed, making it ideal for researchers and developers who need both productivity and performance.
Julia provides a programming environment for technical computing, data analysis, and machine learning.
Pros
- + High performance for numerical and technical computing
- + High-level, user-friendly syntax
- + Strong community and ecosystem for data science and machine learning
Cons
- - No auto-update feature
- - Some syntax changes may cause breaking issues