Alpha is more amenable to basic research when you don't know what the solution path looks like. But that's high-level proficiency, not something you can directly ship in a product. Alpha is also a wonderful playground to use to gain mathematical and data science proficiency in an interactive and intuitive way.

Which, for me, means I use Alpha more like a really big and powerful calculator, where I just want a useful answer more than I care about the code used to generate it.

Python lets me go from MicroPython all the way up to supercomputers, though not seamlessly, there is a delightful level of commonality. Mathematica has code export capabilities, but there are gaps moving from Alpha to Mathematica to C++, and the process is rather frustrating. In Alpha, unless you dig deep into the underlying system, your math will be solved using whatever package Alpha picks for you, which may be a great choice, but it is still done behind the scenes.Īlpha also has trouble working outside the Alpha environment, say, to construct and deploy a robotic control system. In Python I have access to several linear algebra packages, and while learning each can be a pain, being able to switch between them lets me get my job done better. I've done some horribly wonderful mashups of, say, Pandas and PyTorch with real-time signal processing and control loops.īoth Python and Alpha have 'cognitive load' and 'technical debt' associated with them, but it's presented very differently.

In Python, I'm free to bash my code together with libraries, to use them in creative ways that present some powerful advantages. For me, the greatest Alpha limitation is that I have to do things 'The Alpha Way'.

While I do appreciate and use Wolfram Alpha, I'm also aware of its limitations.

Mathematica for programmers