I have a friend that uses Mathematica, and I had some symbolic math I wanted to run a solver on last week, so I thought I'd give it a try. It wasn't any better

I have a friend that uses Mathematica, and I had some symbolic math I wanted to run a solver on last week, so I thought I'd give it a try. It wasn't any better than sympy from what I could tell. I ended up taking a monte carlo approach anyway. Generating the simulations was soooo slow in Mathematica compared to numpy. After that demo, I'll have to say the Python ecosystem is far superior for all it's flaws. Sure the documentation is all over the place, there's no unified design so it all feels different, and lot of choices to do the same thing. But when you need something to work right now, at least I have options to switch things out or even fix the code. Wolfram has a lot of lofty ideas that hasn't been delivered on. 13) Try to do everything, but poorly But seriously how can you build a unified model of computation, when we're still figuring things out? I just tried jax yesterday, their approach to jit & automatic vectorization is really interesting. Wolfram, do you even have a jit? This article gives me less respect for the Wolfram Tech, they're building off the shoulders of giants trying to package it up in some nice soft serve product--it's not a good look to criticize something your building your empire off of.