Some examples of code or programs that demonstrate the benefits of coding in APL include:

Matrix operations: APL is particularly well-suited for performing matrix operations, which are common in scientific and mathematical computing. The concise syntax of APL allows programmers to write complex matrix operations in just a few lines of code.

Statistical analysis: APL is also well-suited for performing statistical analysis, which is another common task in scientific computing. APL has built-in functions for performing a wide range of statistical operations, such as mean, variance, and standard deviation.

Algorithm development: APL's concise syntax and built-in functions make it well-suited for developing complex algorithms. APL allows programmers to express complex algorithms in a way that is both concise and easy to understand.

Matlab is another popular programming language that is used for scientific and mathematical computing. Matlab is similar to APL in many ways, but it has a larger user base and a wider range of tools and libraries available. Matlab is generally considered to be easier to learn than APL, and it is a good choice for someone who has little coding experience.

In general, the choice between APL and Matlab depends on the specific needs of the programmer. If the programmer is working on a project that requires complex matrix operations or algorithm development, then APL may be the better choice. However, if the programmer is looking for a more user-friendly language with a larger community and more available resources, then Matlab may be a better choice.
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