I'm very very happy to be here amongst all of the great coffers amongst all of the developers that have the ability to change the world because as developer we have the ability to change the world the world have changed and yes this tends to keep on growing. The software gave us as human the capability to enhance the world and the current goal of the software is to predict the stuff given why I want to predict X or given X I want to predict Y and this is a typical case for machine learning. So as developer we may want to use machine learning but the ecosystem is hard, mastering machine learning is hard, TensorFlow, backpropagation, equations and this should not exclude us from this revolution. We should use the machine learning like any arbitrary library. We feed it some input and get the output regarding regardless of the wiring inside the model. Let's take an illustration. Imagine this is a neural network and a neural network is just a bunch of equations and mathematical models. With associated values the goal is to find the right equation and the right values for a given use case to get the most accurate prediction of whatever input. We see here that it is not linked to the execution engine and it should not be linked to the execution engine. The value is the runtime, not the execution. So we could use, imagine we can get an abstraction of this neural network, describe it in a language and open language, we could use a superpower of machine learning and the power of our favorite language. Such a format exists. It's called Onix. It has been implemented by some major company in the AI world. What about go? We could use go as a runtime because if we hear we all love to programming go. So let me introduce you to Onix go. Onix go is a package that facilitates the import and the usage of pre-trained Onix model into any go program. It relies on an execution back in such as Gorgonia for example. This is how it looks like. You encapsulate the model. The model is described in Onix, in the Onix syntax. It can be pre-trained in TensorFlow, PyTorch, whatever Microsoft, CNTK. The model is encapsulated into an execution engine. The execution engine is abstracted with Onix go and you can use it with your regular code. So you just write a regular code the way you used to write it and use the neural network as a capacity. How do you do that? You do that in four steps. You create an execution back in. For example, Gorgonia is a good execution back in. For now it's the only one that works actually. You create a model. You load the definition file which is a binary file into the model. You set up an input. You compute and get the output. And that's it. Does it work? Right now it's a proof of concept and it needs love. A lot of love, a fantastic. At this working I'm able to import a pre-trained MNIS model into my code, write a picture and get the result. So the concept is okay. So what now? Here is your homework. Take a look at the Onix project. Take a look at the model zoo. It takes a lot of pre-trained model and imagine what you, as a go for, you can do with Onix go. And if your imagination was far enough, get involved. We need some people to develop that. Nobody's nobody. I told this sentence from Kubernetes but I love it. And all together, all together the Go community, I hope, we may be able to make programming with neural network fun again. Thank you.