I completely agree that there are many scientific libraries in python which scale up. I was addressing the article which showed a more advanced way to use python with the purpose of making it applicable to large datasets. If you were to implement a method from scratch or scale up to a larger dataset then you'll end up with using numba, numpy and dask. This is completely from a lower level programming perspective to implement and integrate methods rather than pipelining methods from higher level scientific libraries.
The content in the article is a bit dated. Jupyter + Numba + Dask is the direction scientific computing (in Python) is taking. Ipyparallel is not really scalable in my experience.
Designing a good gRNA is quite straightforward if you compare it to any other bioinformatics tasks. Even designing a degenerate primer is more complex than this.
Synthego custom crispr kit seems like a very useless service. Guide RNA design for knockout is extremely trivial. You just need a couple of 20bp+NGG sequence which is unique to the gene.. Who would pay to do that?..
Just for some context: https://www.scipy.org/about.html https://www.scipy.org/topical-software.html