# NemoClaw Published articles for NemoClaw. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Use AI Agents to Prepare 3D Scenes for Simulation DevFeed: [How to Use AI Agents to Prepare 3D Scenes for Simulation]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T23:20:33Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation](), [Physical AI](), [Simulation and Design](), [blender](), [Isaac Sim](), [Omniverse](), [Robotics Simulation](), [Robotics]() Tags: [3d](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [blender](), [gpt-6-astra](), [how-to](), [isaac-sim](), [nemoclaw](), [nvidia](), [omniverse](), [openai](), [openusd](), [physical-ai](), [robotics](), [robotics-simulation](), [simulation](), [simulation-modeling-design]() ### AI overview This tutorial describes an agentic workflow for preparing Blender 3D scenes for robotics simulation. It covers scene inspection, OpenUSD metadata, physics properties, rendering preflight views, and validation for simulation-ready handoff to NVIDIA Isaac Sim or Isaac Lab. ### Source excerpt Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in... ## Building a Memory-Driven Agent with NVIDIA NemoClaw DevFeed: [Building a Memory-Driven Agent with NVIDIA NemoClaw]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-04T18:04:55Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Architecture & Design](), [Authorization](), [Markdown]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [build-ai-agents](), [developer-tools-techniques](), [enterprise](), [llms](), [memory](), [nemoclaw](), [openshell](), [retrieval](), [retrieval-augmented-generation-rag](), [security]() ### AI overview This tutorial describes building a memory-driven AI agent with NVIDIA NemoClaw for enterprise work. It presents a structured self model, separates evidence from derived knowledge and governed execution, and emphasizes retrieval, user corrections, security, and authorization. ### Source excerpt Enterprise work spans messages, decisions, projects, and obligations that change over time. An AI agent that starts without this context must reconstruct it... ## NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents DevFeed: [NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-11T13:01:07Z Content type: release Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Language models](), [AI Bots]() Tags: [agentic-ai-generative-ai](), [agents](), [ai-agent](), [ai-agents](), [dgx-spark](), [featured](), [inference](), [low-latency](), [mixture-of-experts-moe](), [nemoclaw](), [nemotron](), [nvfp4](), [nvidia](), [open-source](), [orchestration](), [routing](), [top-stories]() ### AI overview NVIDIA introduces Nemotron 3.5 Lightning, an open 30B MoE model with 3B active parameters for fast, high-volume execution in long-running AI agents. It also presents NeMo Switchyard for routing tasks to appropriate models. ### Source excerpt Long-running AI agents spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning... ## Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA DevFeed: [Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-10T13:27:19Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Local AI](), [Large Language Model](), [long-context](), [Inference](), [Meta](), [Open Source](), [GPU](), [NVIDIA DGX](), [NVLink](), [Jetson](), [Automation]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [ai](), [automation](), [context-window](), [data-center-cloud](), [dgx-spark](), [dgx-station](), [edge-computing](), [featured](), [gpu](), [inference](), [jetson](), [local-ai](), [nemoclaw](), [nvidia](), [nvidia-dgx](), [nvlink](), [open-source](), [top-stories]() ### AI overview Meta's Muse Glimmer is a 30B open-weight dense model designed for local agentic AI workflows. With a 120K+ context window and performance of up to 20K tokens per second on a single GPU, it supports sustained, multi-step tool use and local processing of sensitive data. ### Source excerpt Meta returns to the open source ecosystem with the release of Muse Glimmer, a 30B open-weight dense model with a 120K+ context window built for local AI... ## Integrating Context-Aware Video AI Agents Into Enterprise Workflows DevFeed: [Integrating Context-Aware Video AI Agents Into Enterprise Workflows]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-16T16:03:35Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [ai-agents](), [computer-vision-video-analytics](), [developer-tools-techniques](), [enterprise](), [featured](), [integration](), [nemoclaw](), [nvidia](), [rag](), [retrieval-augmented-generation-rag](), [video](), [video-analytics](), [workflows]() ### AI overview A tutorial on integrating context-aware video AI agents into enterprise workflows with NVIDIA NemoClaw, Video Search and Summarization, and retrieval-augmented generation blueprints. ### Source excerpt A video analytics AI agent that can perceive, reason, and act based on massive amounts of video footage must be integrated with existing workflows and... ## DigitalOcean at NVIDIA GTC 2026: Building the AI Factory for the Agentic Era DevFeed: [DigitalOcean at NVIDIA GTC 2026: Building the AI Factory for the Agentic Era]() Original publisher: [Read original article]() Author: Vinay Kumar, DigitalOcean Chief Product & Technology Officer Published: 2026-03-16T20:35:55Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Artificial Intelligence](), [Inference](), [Nvidia](), [Cloud](), [model-deployment](), [Deployment](), [NemoClaw](), [OpenClaw](), [OpenShell](), [Open Source]() Tags: [2026](), [agentic](), [ai](), [announce](), [autonomous](), [deployment](), [digitalocean](), [inference](), [infrastructure](), [models](), [nemoclaw](), [nvidia](), [open-source](), [openclaw](), [openshell](), [product-updates](), [production](), [services]() ### AI overview DigitalOcean announces an expansion of its AI inference capabilities in partnership with NVIDIA. The article describes an AI Factory for production AI workloads, including agentic workflows, OpenClaw deployment, NemoClaw, and the OpenShell runtime. ### Source excerpt A seamless path for builders: Start building on build.nvidia.com, Deploy to DigitalOcean The landscape of artificial intelligence has shifted from static models to dynamic, long-running agents. At DigitalOcean, our mission is to provide developers with a purpose-built, agentic, inference cloud for running AI in production--without the operational overhead or complex cost structures of traditional infrastructure. Today, at NVIDIA GTC 2026, we are excited to announce a massive expansion of our inference capabilities in partnership with NVIDIA. We are moving beyond basic infrastructure; we are building an AI Factory designed specifically to support AI builders and power the next generation of autonomous agents. The Proven Home for AI Agents DigitalOcean is rapidly becoming the dominant player and preferred deployment destination for agentic workflows. When the open-source agent OpenClaw (formerly Clawdbot) went viral, we recognized the market's need for frictionless deployment. In under 36 hours, we shipped a production-ready 1-Click Droplet to our Marketplace. The results demonstrate our reach: OpenClaw has driven 43,000+ total deployments on DigitalOcean with over 11,000 active OpenClaw deployments in production today. Builders aren't just deploying models; they are utilizing our ecosystem, expanding into adjacent services like Backups, Snapshots, and Gradient AITM Serverless Inference to support their agentic workloads. DigitalOcean and NVIDIA are also working together on NVIDIA NemoClaw, an open source stack that simplifies running OpenClaw always-on assistants, more safely, with a single command. The NVIDIA OpenShell runtime offers a secure environment to run autonomous agents and open source models, then deploy seamlessly to DigitalOcean. Investing in the "AI Factory": Deep Cloud & Inference Integration Why is DigitalOcean uniquely positioned to win in this new marketplace? We are investing deeply to integrate traditional cloud primitives with our state-of-the-art ## Why Checkout Trust Matters for AI-Era Conversions DevFeed: [Why Checkout Trust Matters for AI-Era Conversions]() Original publisher: [Read original article]() Author: Rishabh Goel Published: 2026-03-13T00:00:00Z Content type: opinion Language: en Sources: [Dodo Payments Blog]() Topics: [Artificial Intelligence](), [computer-use](), [API](), [OpenAI](), [Meta](), [Nvidia](), [NemoClaw]() Tags: [ai](), [checkout](), [computer-use](), [design](), [meta](), [nemoclaw](), [newsletter](), [nvidia](), [openai](), [trust]() ### AI overview This opinion article argues that payments remain a human trust moment even as AI agents browse the internet and complete tasks. It discusses checkout friction and trust signals, and highlights Dodo Payments' unified Design Hub and credit-based billing updates. ### Source excerpt Learn why checkout trust drives AI-era conversions, and explore Dodo's unified Design Hub, credit billing, and upgrades that cut abandonment.