Big Zuck Energy | Coder Radio 558

We embrace the dad bod lifestyle and find out if Apple's Vision Pro demo sold Mike, and Chris is picking up on what the Zuck is putting down.

Sponsored By:

• Trusteebook: TrusteeBook - a simple, easy-to-use workbook that helps you take control of your digital legacy. Get started today, and take $10 off. Promo Code: CODER
• Coder QA: Take $2 a month off for the lifetime of your membership and contribute to our show directly Promo Code: jarjar

Support Coder Radio

Links:

• 💥 Gets Sats Quick and Easy with Strike — Strike is a lightning-powered app that lets you quickly and cheaply grab sats in over 36 countries.
• 📻 Boost with Fountain.FM — Fountain 1.0 has a new UI, upgrades, and super simple Strike integration for easy Boosts.
• Star Trek: Borg - Remastered — Welcome to 'Star Trek: Borg - Remastered', cadet. Pay attention to this training - it might ensure your survival.
• Zuckerberg trashes Apple vision Pro - YouTube — YouTube shot, FYI.
• Apple vs. Meta Headset Wars, AI Innovations & Raising Cattle with Mark Zuckerberg - YouTube — Neal and Toby chat with Meta founder and CEO Mark Zuckerberg to talk about his notorious Apple Vision Pro review video, the future of AI, what's next for Meta, and of course, cattle raisin' & meat smokin'.
• Mark Zuckerberg: Tech layoffs in 2024 have been a natural response to pandemic-era overhiring | ITPro — Overhiring and a prevailing industry approach to becoming “leaner” is driving the latest wave of layoffs
• 1:30 PM | Meetup">🍔 Lunch at SCaLE 🍇, Sat, Mar 16, 2024, 1:30 PM | Meetup — Let's put an official time down on the calendar to get together. The Yardhouse has always been a solid go-to, so sit down and break bread with the Unplugged crew during the lunch break on Saturday!
• How Nvidia’s CUDA Monopoly In Machine Learning Is Breaking - OpenAI Triton And PyTorch 2.0 — This report will touch on topics such as why Google’s TensorFlow lost out to PyTorch, why Google hasn’t been able to capitalize publicly on its early leadership of AI, the major components of machine learning model training time, the memory capacity/bandwidth/cost wall, model optimization, why other AI hardware companies haven’t been able to make a dent in Nvidia’s dominance so far, why hardware will start to matter more, how Nvidia’s competitive advantage in CUDA is wiped away, and a major win one of Nvidia’s competitors has at a large cloud for training silicon.