Introducing Code Comments
Shaping Extended Reality Through AI
Extended reality, or XR, is all about immersion. How can natural language processing (NLP) improve XR for use in product design?
About the show
Stories from technologists who have gone through it. And our guests have built some pretty epic stuff. Each episode features an experienced technologist narrating a pivotal moment in the life of a technical project. Ultimately, they’ve come through challenges, having learned lessons they can pass along to you.
Sample the podcast
Want to know if Code Comments is for you before adding it to your line-up? Here’s a sneak peek.
Meet your host
A lifelong developer advocate, community organizer, and technologist—Burr has seen it all. His roots are in Java, but he’s also an expert in cloud, Kubernetes, mobile, IoT, and more. Now, he’s sitting down with experienced technologists from across the industry to trade stories and learn from their experience.
The first code that I wrote for CockroachDB was actually an implementation of Raft. In that week, I was able to implement probably the first 80% of the Raft algorithm. Then it turns out that we'd spend upwards of the next year working on what I'd say is the last 20%.
Ben Darnell, Cockroach Labs
I'll start with trying to maybe dispel a myth. You don't need a GPU especially for running inference. And that's part of the value of OpenVINO, is that we're taking models that may have been trained on a GPU using deep learning frameworks like PyTorch or TensorFlow, and then optimizing them to run on Intel hardware.
Ryan Loney, Intel
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Presented by Red Hat
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