Just a shower thought on $AMD vs $NVDA Right now Nvidia undoubtedly dominates in robotics (Isaac, Omniverse, GR00T, Jetson). They own the whole development stack robots get built/trained on. Once humanoids are scaling massively (when we're out of dev. phase) and it becomes a cost competition, this is typically where $AMD outperforms Nvidia. Not to mention, its embedded/adaptive silicon (Xilinx) is crucial to real-time robotics control, which $NVDA doesn't have. Ofc this is contingent on robotics becoming progressively more mainstream. Just wanted to toss this thought out there. TLDR: wouldn't bet against $NVDA or $AMD in the long-run, but when it comes to robotics scaling, $AMD should have a higher upside.

At NVIDIA GTC, Rev Lebaredian, VP of Omniverse and Simulation Technology, called today’s agents “software robots that can do knowledge work” and says we’re at the start of a revolution in how we work, build, and create value. Get a glimpse of that future in this GTC session: Accelerate the Physical AI Era With Digital Twins and Real-Time Simulation

$NVDA and $AMZN are deepening their AI partnership with AWS set to deploy 2M more Nvidia GPUs through Q2 FY29 and another 100,000 GPUs for secure U.S. government data centers. AWS is also adopting more of Nvidia’s stack beyond GPUs from Vera and Nemotron to Omniverse, Cosmos, Isaac and Jetson across cloud and robotics.

How do you turn real-world driving footage into photorealistic, simulation-ready scenes—without the domain gap? 🚗 In this livestream, our experts share how NVIDIA Omniverse NuRec reconstructs real driving clips into 3D Gaussian splat scenes that power closed-loop AV simulation and synthetic data generation. Tune in live:

Super agents have arrived on the desktop. With local AI, designers and engineers can build, customize and run domain-specific agents using their own data and workflows. NVIDIA NemoClaw on DGX Station brings together Nemotron 3 Ultra, Omniverse libraries and OpenShell in an open agent stack. 🔗 #SIGGRAPH2026#

We have worked with @nvidia to integrate their official Agent Skills catalog into the Hermes Skills Hub. These skills teach your agent how to use CUDA-X libraries, Omniverse and Physical AI workflows, NeMo training and inference tools, and other platform components.

$NVDA & AWS EXPAND AI INFRASTRUCTURE PARTNERSHIP NVIDIA says AWS will deploy 2 million additional NVIDIA GPUs across its global infrastructure through Q2 FY29, along with Vera CPUs. AWS and NVIDIA will also build secure data centers for the U.S. government featuring 100,000 GPUs. AWS will offer NVIDIA’s Nemotron open models through Bedrock and SageMaker, while Amazon will adopt NVIDIA’s Omniverse, Cosmos, Isaac and Jetson stack for its warehouse robotics fleet.

.@bilawalsidhu reveals how the Seoul World Model generates realistic training data for robotics: "Seoul World Model is a really interesting paper, very similar to what Google's doing with Street View grounding. If you ask Genie to give you a representation of the Palace of Fine Arts, it'll give you a plausible reconstruction, but it ain't exactly it." "One way to constrain these models is, we could just do what we do in LLMs, which is RAG, retrieval-augmented generation." "Can we do a form of spatial RAG where you just pull in the nearest reference image of reality to condition the generation? That lets you do a lot of these use cases that are interesting for robotics." "If you wanna create a bunch of training data for downtown Seoul in different lighting conditions, really cool way to go about doing that. That's exactly what you see folks like NVIDIA trying to do with Omniverse as this 3D simulator and Cosmos."

Closing the sim-to-real gap for humanoid robotics starts with better simulation environments. Join us, @NianticSpatial, and @FlexionAI on Aug. 12 at 11 AM PT to see a real-to-sim workflow using 3D Gaussian splatting, Omniverse NuRec, Isaac, and OpenUSD. 📅 Add to calendar:

Weekly NVIDIA Update AI INFRASTRUCTURE EXPANSION IN INDONESIA Firmus Technologies partnered with $NVDA to develop a 360MW AI campus in Batam, Indonesia, featuring up to 170,000 Nvidia AI accelerators through 2028. Firmus expects $25B–$30B in customer offtake agreements over the partnership's first six years, highlighting strong long-term AI infrastructure demand. QUANDELA VALIDATES GPU–QPU INTEGRATION Quandela announced it experimentally validated low-latency integration between its photonic quantum processors and $NVDA accelerated computing infrastructure via NVQLink. The milestone supports tighter GPU–QPU collaboration for hybrid AI, quantum machine learning, and HPC workloads, advancing quantum accelerator deployment in data centers. GEOTHERMAL AI PARTNERSHIP $FRVO partnered with $NVDA and Pacific Northwest National Laboratory to develop EGS-Twin, an AI-powered digital twin platform that uses NVIDIA Omniverse to optimize geothermal reservoir performance. The collaboration aims to improve operational efficiency and support scalable geothermal energy production, with deployment targeted by 2029.