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Earn rewards and recurring income from Fan Club membership.
Get the answers and support you need.
Resources and guides to launch, grow, and monetize podcast.
Stay updated with the latest podcasting tips and trends.
Check out our newest and recently released features!
Podcast interviews, best practices, and helpful tips.
The step-by-step guide to start your own podcast.
Create the best live podcast and engage your audience.
Tips on making the decision to monetize your podcast.
The best ways to get more eyes and ears on your podcast.
Everything you need to know about podcast advertising.
The ultimate guide to recording a podcast on your phone.
Steps to set up and use group recording in the Podbean app.
Mojo Lang - Tomorrow's High Performance Python? (with Chris Lattner)
Mojo is the latest language from the creator of Swift and LLVM. It’s an attempt to take some of the best techniques from CPU/GPU-level programming and package them up in a Python-compatible syntax.
In this episode we explore why Mojo was created, and what it offers to Python programmers and non-Python programmers alike. How is it built for performance, and which performance features matter? What’s its take on functional programming and type systems? And can it marry the high-level programming of Python with the low-level programming of LLVM/MLIR?
If you’re a Python programmer who needs better performance, a C programmer who expects more from a ‘scripting language’, or just someone who’d be happier if Python had a first-class type system, Mojo might well be for you…
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Mojo: https://www.modular.com/max/mojo
Mojo’s Roadmap: https://docs.modular.com/mojo/roadmap.html
The Mojo Discord: https://discord.com/invite/modular
MLIR: https://mlir.llvm.org/
Chris’s Talks: https://nondot.org/sabre/Resume.html#talks
Chris on Twitter: https://twitter.com/clattner_llvm
Kris on Mastodon: http://mastodon.social/@krisajenkins
Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/
Kris on Twitter: https://twitter.com/krisajenkins
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#software #podcast #mojolang #ml #pythonml
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