Hi Lex,<br><br>I think this episode makes a great segue into what makes Julia such a phenomenal language — for instance, the type hierarchy makes it such that (as a user), you don’t have to call different functions for acting on a scalar or a vector. JIT compilation just figures out the optimal method call (see “multiple dispatch”). As such, you never have to worry about “np.sqrt vs math.sqrt” etc<br><br>As a physicist who made the switch recently, I think Julia has the potential to revolutionize scientific computing, and I don’t say this lightly. It would be incredible to hear more about it from people like Alan Edelman (@ MIT itself)<br><br>EDIT: for a more ML-oriented audience, you could also invite someone like Chris Rackauckas. He’s also largely responsible for perhaps the best differential equation solving library available (beating even Mathematica in my opinion).