Bender vs Numba: What are the differences?

# Introduction
In this comparison, we will highlight key differences between Bender and Numba for better understanding of their functionalities.

1. **Execution Mode**: Bender operates in a deferred execution mode, where transformations are applied as needed during runtime, while Numba operates in the immediate execution mode, where transformations are applied at the time of function definition. This difference affects the overall performance and flexibility of the two tools.
   
2. **Supported Languages**: Bender primarily supports Python and Java for code transformation and optimization, whereas Numba is specifically designed for Python code optimization, making it more specialized in its functionality. This difference in supported languages can influence the choice of tool based on the user's programming language preferences.

3. **Usage in High-Performance Computing**: Numba is commonly used in high-performance computing applications due to its ability to generate efficient machine code for numerical computations directly from Python code. On the other hand, Bender's focus on declarative transformations may not be as well-suited for such computationally intensive tasks, making Numba a preferred choice in this domain.

4. **Community Support**: Numba has a larger and more active community of users and developers, leading to frequent updates, bug fixes, and additional features. In contrast, Bender may have a smaller community base, which could impact the availability of resources and support for users seeking assistance or looking to expand the tool's capabilities.

5. **Code Generation Approach**: Bender adopts a rule-based approach to code transformation, allowing users to specify transformation rules based on patterns in the code. In contrast, Numba uses just-in-time compilation to optimize Python functions by generating machine code dynamically during runtime. This difference in code generation approaches can impact the ease of use and customization options available in the two tools.

6. **Targeted Use Cases**: While Bender is more suitable for users looking to apply rule-based transformations to their codebase and enforce coding standards, Numba is better suited for users seeking to optimize numerical computations in Python for faster execution. Understanding the specific use cases and requirements can help users make an informed choice between the two tools.

In Summary, the key differences between Bender and Numba lie in their execution modes, supported languages, usage in high-performance computing, community support, code generation approach, and targeted use cases, all of which contribute to their distinct functionalities and applications.