[transcript]

23:18 Yeah, that's a very good question as well. Basically, pytorch kind of started as a pure Python implementation, really, like, the whole framework was written in Python, except for the, like, core kernels that to like, the math, because for those, like, if you really want to match performance of existing libraries, you really need this part to, like, live in highly optimized C and c++. So, you know, but a lot of like, all of the, let's say, logic of the, of the framework, like automatic differentiation, and so on, we started with an implementation within Python, and then, you know, throughout the history with intent of moving away from this choice, like, we've been moving more and more things, c++, mostly from two reasons. One is that some of this code is like really hot, like it's getting executed, kind of at every single line of user programs. And so overhead of this code is really, really important. And Python sometimes just can't deliver the same performance, although like as an anecdote. One downside is that when I ported part of the automatic differentiation system, which was like 30 lines of Python code, I think it expanded into like, a few hundred lines of c++ code that like work with C, Python API, and so on. So it's definitely like, not easy to convert this. But we've been seeing like measurable and significant performance gains from this. But this is mostly because like, it is really hot code that's, like, heavily exploited by the users, we don't really typically see those kinds of speed ups, if like, you were to just write your let's say, machine learning model in Python vs in c++ itself. So this is really only relevant for the library. But but at the same time, you know, we have some users which are really interested in running models in pure c++, mostly because, like, they have existing research pipelines and c++, like a lot of reinforcement learning research on games, a lot of a lot of games, kind of only have c++ API's. And so those people naturally like started their projects and c++. So they would really like also to have hybrid features in c++. And this is something that we've been trying to address as well, like with their release the with at one point or release, we also have like a beta version of our Python bindings like our Python interface, except in c++. So that also required kind of, to move a lot of things from Python, c++, although at this point, we've been so far ahead in this work, that it wasn't too hard to actually add those c++ findings. So today, really, most of Piper is just a c++ library, and then like a thin layer of Python bindings on top of this.