For libraries like Tensorflow. So if we use just time alone, I think that's probably not the right way to look at it. It's like how relevant is that community? How relevant is that language willing to adapt? And I think what Python did really, really well, it ... that community saw the ability to make other languages easier to use. For example, Tensorflow's a lot of C++ underneath it, so programming in such a language is probably not as user friendly as something like Python. And you could take Python and use it to generate some of the stuff that people are using for, example, Tensorflow. So now that machine learning is hot, people have brung Python into that new space, so guess what? Python continues to be relevant, and will be relevant for some time to come. And the same thing's going to be true for Go. If Go can continue to be relevant, right, it's like at the foundation of many of our infrastructure tools, many of the cloud libraries, it too will remain relevant. So I think it's all about those communities ensuring that they have a place in the future, and when the future shows up, making sure that they have a story there.