Develop on Windows, Deploy on Red Hat Enterprise Linux

Microsoft and Red Hat recently simplified the ability to run Red Hat Enterprise Linux (RHEL) on Microsoft Windows systems using Windows Services for Linux version 2 (WSL2). In this demo-filled session, we’ll show you how to install RHEL on WSL2 and use it to develop a Linux-based Python application. We’ll also have a look at the capabilities of RHEL image builder and demonstrate how you can build a WSL2-ready developer image that runs in WSL2 and lets you use a suite of your favorite Linux and Windows tools in the development process.. This way of working has been getting harder as systems become more and more distributed. Now, with the advent of AI-driven development, it has become even more challenging than ever before. Many of us use remote generative AI services to help us with scaffolding, writing and testing code, and with many other tasks.

Wouldn’t it be great if we could still run everything on our local machine? That’s what this session is all about!

In this session we’ll take a look at various solutions to continue to do local, network-optional development: Run AI models locally using different tools such as Ollama, Podman AI Lab, RamaLama, Docker, etc. Evaluate different code assistants that can work with these locally running models Explore how to infuse AI capabilities from local models into our code Compare models from different vendors; and with different sizes to find the right balance between performance and accuracy Weigh pros and cons of using local vs remote models

Come to this session to learn more about local development while leveraging AI to optimize our development flow, code and our functionality itself!