 I also got stuck here with no way forward. (until reading here)The explanatory text "Note that the matrix first element-wise multiplied by 3." is very unclear.Also, looking at it from a Python novice, it is not clear we were supposed to know that the .prod() operator can act on something that is not purely a named variable, but can work on an already manipulated quantity within parentheses.Also, and this is not your fault but of Python's, but it is not clear (or merits some explanation) why some operators take the form "operator(x)" while others behave like "x.operator" or even "x.operator()" with no argument. Thanks for the feedback. I think I was being overly clever by trying to make the product example more complicated than just `m.prod()` similar to the `m.sum()` example, but as you mentioned I was fitting two concepts in one question — operating on an already manipulated variable in addition to the new operator.I've simplified question 9 to just `m.prod()` from the previous solution of `(m * 3).prod()` for now.Re: Methods vs. functions — I completely agree! I actually originally implemented Math to Code using Tensorflow.js (which was simpler than using a Python interpreter) where the syntax was:`````` m.pow(2).sum().sqrt() `````` Vs.`````` np.sqrt((m ** 2).sum()) `````` I decided to go with NumPy + Python because I felt it would be more immediately applicable vs. just learning the Tensorflow.js syntax. Also Python allows operator overloading so you can just do `2 * m` vs. `m.mul(2)`.PyTorch has a similarly clean syntax to Tensorflow.js and includes operator overloading, but there doesn't seem to be a PyTorch shim for Skulpt, but that would be a fun project. also, there are functions which need to be prefixed with np - the module name, but this isn't described. So for someone who isn't quite familiar with python, this doesn't make sense. Search: