Microsoft Collaborates with NVIDIA on DirectX for Unified AI Workflow

Sandboxing AI agents for yolo-mode ("accept all permissions") seems like a swamp of ad hoc solutions from propeller heads. Let's see if this new container based solution from NVIDIA makes it smoother: https://lnkd.in/dxBSpCiJ

The biggest roadblock to actually using AI agents in a real company usually comes down to one question: "What exactly is this thing accessing, and where is our data going?" NVIDIA just dropped a pretty solid answer to that with NemoClaw. Long story short, NemoClaw takes the autonomous capabilities of tools like OpenClaw but forces them into a strictly controlled sandbox. A few things that stood out to me: 🔹 Strict Isolation: The agent only touches the specific files, apps, and networks you explicitly whitelist. No unpredictable rogue actions. 🔹 100% Local Execution: Your proprietary code and sensitive data literally never leave your infrastructure. 🔹 Open-source: The framework itself is free to use (assuming you have the RTX hardware to actually run it). For dev teams who are serious about data privacy, this is a big deal. We no longer have to blindly hand over confidential code to external APIs just to get a working autonomous agent. Has anyone had the chance to dig into the docs or spin it up locally yet? Curious to hear your thoughts. 👇 #AI #Nvidia #NemoClaw #DataPrivacy #SoftwareEngineering #TechNews #OpenSource https://lnkd.in/dd_z6t_S

LangGraph just got a big boost. NVIDIA released a production platform for it. Apparently, they’ve been working on this for a while. Seriously streamlining workflows. It’s supposed to make deploying LangGraph apps way easier. Think, faster inference, better scaling. They're using Triton Inference Server. Good choice, honestly. It's one of the few that actually *works* reliably. I tried a quick test with a simple RAG pipeline. Ran surprisingly well. Took about 10 minutes to set up. Normally, that would be a half-day affair. The setup scripts were... less than elegant, though. Felt like they copy-pasted from a tutorial from 2021. It’s mostly for larger projects. Smaller things are fine on a regular server. This feels targeted at companies actually needing to serve lots of users. Makes sense. Building our internal tool at JOYNEXT really highlighted how painful deployment can be. This addresses that directly. Less time wrestling with infrastructure, more time actually building. Did anyone else immediately jump on this to test it? #LangGraph #NVIDIA #AI

When I first deployed ClawdBot (back when it still was called like that), one of the main issues was security. NemoClaw now addresses these issues with e.g. protection from prompt injections. I highly suggest to read the blog article from Johan. Great breakdown!

💾 Custom Partition Layouts on NVIDIA Jetson? Don’t Learn This the Hard Way. Everything works… until you need: 🔹 More storage for AI data 🔹 OTA updates (A/B rootfs) 🔹 Persistent logs 🔹 NVMe or external storage 👉 That’s when the default Jetson partition layout breaks down. We created a step-by-step guide to help you customize partition layouts *the right way*—without breaking your device 👇 📚 Read the full guide: https://lnkd.in/edZ4xrAP --- ### 🔧 What you’ll learn: ✅ How Jetson partition layouts actually work ✅ How to safely modify flash configuration files ✅ Add custom partitions (data, logs, OTA, NVMe) ✅ Avoid boot and flashing failures ✅ Structure storage for production-ready systems 🚀 Building on Jetson and need help? 👉 Book a free consult: https://lnkd.in/e4yqbNSC #NVIDIAConnect #NVIDIAJetson #EmbeddedLinux #YoctoProject #EdgeAI #AIonEdge #LinuxBuildSystem #EmbeddedSystems #ProventusNova

What if you could run a full LLM locally with GPU acceleration? No API keys. No cloud costs. No data leaving your machine. Check out this video on Kubesimplify youtube by Saloni Narang where she demonstrates exactly that using Docker, Inc Model Runner running on the NVIDIA DGX Spark 𝐋𝐢𝐯𝐞 𝐝𝐞𝐦𝐨 building a lightweight Python release notes analyzer that uses the Qwen3-Coder model pulled and run locally with Docker Model Runner.

Why I generate images in 4K resolution | Half of UI academic research fails to reproduce | NotebookLM Cinematic Videos Do Well with Viewers | Midjourney Version 8 Disappoints | New protocol for AI agents to use micropayments | Microsoft releases its new MAI-Image-2 model | NVIDIA’s GTC Conference Full article 👉 https://lnkd.in/ggKb_cbM #UserExperience #ArtificialIntelligence #NVIDIA #Video Education #ImageGeneration

Better visuals and smarter AI won’t fix weak UX fundamentals. 4K images, cinematic videos, advanced models, all great. But if the underlying thinking is flawed, we are just scaling noise faster. The part about research failing to reproduce is a bigger deal than it seems. If we cannot trust insights, we cannot design with confidence. Tools are accelerating. But clarity, rigor, and critical thinking still matter more than ever.

NVIDIA NemoClaw makes deploying always-on AI agents easier and safer than ever—one command to run any coding agent anywhere! 🔗 https://nvda.ws/47XzlfU "

Excited to see how NVIDIA is making AI agent deployment more accessible and safer with NemoClaw—tools like this could really accelerate real-world adoption of always-on assistants