> When speed mattes, people use libraries that are considerably faster than plain python.
This.
The general guideline has always been that Python is ideal for glue code and non-performance-critical code, and when performance became an issue then Python code would simply be used as glue code to invoke specialized libraries. Perhaps the most popular example of this approach is bumpy, which uses BLAS and LAPACK internally to handle linear algebra stuff.
This Nim advertisement sounds awfully desperate with the way it resorts to what feels like a poorly assembled strawman, while giving absolutely nothing in return.
This.
The general guideline has always been that Python is ideal for glue code and non-performance-critical code, and when performance became an issue then Python code would simply be used as glue code to invoke specialized libraries. Perhaps the most popular example of this approach is bumpy, which uses BLAS and LAPACK internally to handle linear algebra stuff.
This Nim advertisement sounds awfully desperate with the way it resorts to what feels like a poorly assembled strawman, while giving absolutely nothing in return.