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Who we want to meet at AMD Advancing AI, July 22 and 23: Embedded and SoC engineers building on Ryzen Embedded and EPYC Embedded who want a runtime that deploys agents out of the box. OEM product leaders deciding what ships on next year’s devices, who need the agent layer to be small, secure, and OS agnostic. Robotics and Physical AI teams stuck between a working pilot and a production fleet. Systems integrators designing agentic architectures for enterprises that insist on sovereignty in execution: control over where agents execute, decide, and act. Analysts covering the agent runtime layer, because this category is moving fast and we would rather show than tell. If any of that is you, comment or DM us. Coffee slots are open both days. Learn more: mimik.com/our-events/mim… #AMDAdvancingAI #AgenticAI #Embedded #Robotics @Fayarjomandi @siavashalamouti @SamArmani

oughly 90 percent of AI pilots never make it to production. The missing piece is not more inference compute. The industry has spent years solving the model problem. What is not solved is the operations problem: how autonomous agents are executed, discovered, coordinated, secured, and observed once they leave the lab. We call this the #operationalization gap. mimOE is the Agentix-Native (aka #AgenticAI) Operating Engine built to close it. One runtime, 10 to 20 MB, sitting on top of any OS on any device, powering the full agent layer across the Device-First Continuum, from the device itself to gateways, on-prem systems, and cloud. That is the conversation we are bringing to @AMD #AdvancingAI on July 22 and 23, and it is a better conversation in person. We are booking 30-minute meetings on site now. DM us or grab a slot. #AMDAdvancingAI #AgenticAI #DeviceFirstContinuum #mimOE @Fayarjomandi @siavashalamouti @SamArmani

Where does #PhysicalAI actually run? At #AMDAdvancingAI 2026, @mimiktech CEO @Fayarjomandi joins the Physical AI panel, moderated by AMD’s Amey Deosthali, to dig into exactly that question. Robotics and Physical AI are CPU plus GPU problems. Models handle perception and planning, but the agent layer, the part that discovers, coordinates, secures, and observes, runs on the CPU. Getting that layer right is the difference between a demo and a deployment. Fay will share what we have learned running agentic workloads on devices across the Device-First Continuum, and why the runtime is the multiplier on the silicon. Add the session to your agenda. And if you want to go deeper than a panel allows, our team is booking meetings for both days. Link to the panel: amd.com/en/corporate/e… Learn more about mimik at #AMDAdvancingAI: mimik.com/our-events/mim… PhysicalAI #Robotics #AgenticAI @SamArmani @siavashalamouti @AMD

mimik is coming to AMD #AdvancingAI 2026. mimik is coming to @AMD Advancing AI 2026.  On July 22 and 23 at Moscone Center in San Francisco, our team joins AMD and its partner ecosystem for two days focused on what comes next in AI. As a member of the AMD Partner Program, we are arriving with one message: inference compute is solved, operations are not, and mimOE closes that gap on AMD silicon. The highlight of our week: mimik CEO @Fayarjomandi Arjomandi joins the #PhysicalAI panel, moderated by AMD’s Amey Deosthali, on what it takes to move #agentic robotics from pilot to production. If you are attending, let’s meet. Comment below or send us a DM, and we will set up a time on-site. Learn more about mimik: mimik.com #AMDAdvancingAI #PhysicalAI #AgenticAI #mimOE #EnterpriseAI #AIAgents #AgentixNative #OnDeviceAI

The model was never the problem. The runtime is.   A recent @VentureBeat survey of 132 enterprise AI leaders confirms what mimik has argued for years: agents fail in production not because the model reasons poorly, but because the infrastructure under it cannot hold. Container restarts erase context. Token bills break the business case. A small error in step three compounds into catastrophic failure by step twelve. VentureBeat calls it the Agentic Reckoning.   Most of the market is answering it wrong. The common fix is a more durable runtime in the cloud, which keeps the same metered, centralized stack that caused the problem. There is another way. A device-native runtime, across the continuum of compute, keeps agent state local, takes inference off the meter, and keeps sovereign data in place. That is what Agentix-Native systems on mimOE are built for.   The agents are ready. The runtime is the gap. mimOE closes it. Download #mimOEStudio and start building agents that survive production. hubs.la/Q04mBGsN0   #EnterpriseAI #AgenticAI #AIAgents #AgentixNative #OnDeviceAI #mimik

The #humanoid pilot phase is over. The infrastructure question is what comes next.   At Automate 2026, @NVIDIA anchored the floor with the first ever Humanoid Robot Pavilion. The numbers tell the story: Figure AI's factory hit one robot per hour, Boston Dynamics shipped commercial Atlas units to Hyundai, and Agility's Digit is running paid shifts at a Toyota plant. Production humanoids change the infrastructure question entirely. A robot on a factory floor cannot pause for a cloud round trip, send its sensor data offsite for every decision, or waste power it does not have. Battery is the binding constraint: 90 to 120 minutes of runtime against tasks that need 8 to 20 hours.   So the intelligence has to run where the robot is. Locally. Without depending on a connection that may not be there. The robots are ready. The runtime that lets them think on-device, across a continuum of compute, is the part still being built. That is what we build at mimik with #mimOE.   What breaks first when these robots scale, the hardware or the runtime?  Learn more about mimik: hubs.ly/Q04mBkwD0 #PhysicalAI #Robotics #Humanoids #Automate2026 #AIInfrastructure #OnDeviceAI #mimik

The industry has named its 2026 bottleneck, and it got the cure backwards.   A recent VentureBeat analysis lays it out: AI has hit the memory wall. The constraint is no longer GPU availability, it is context. As agentic chains stack dozens of model calls per task, the KV cache has outgrown every tier we built for it: GPU DRAM, system RAM, cloud storage. The proposed fix is a new central context tier. But that is a centralized answer to a distributed problem. Build another central tier and you relocate the wall, you do not remove it. Context does not have to live in the center. When it sits where the work happens, across devices and local compute in a continuum of compute, the bottleneck dissolves instead of moving. That is what mimOE does natively. The memory wall is real. It is not a hardware problem. It is an architecture choice. Where should context live? learn more about mimik: hubs.ly/Q04mB2950   #AIInfrastructure #AgenticAI #DistributedAI #OnDeviceAI #MemoryWall #ContinuumOfCompute #mimik

#PhysicalAI isn't just about faster #GPUs. It's about composing them across robots, devices, and the cloud, so every node can sense, decide, and act in real time.  @nvidia delivers the world's most powerful infrastructure for Physical AI. mimOE sits on top of that infrastructure as a device-first AI fabric, turning every robot, sensor, and on-device system into an intelligent, coordinated node in a continuum of compute.  Faster GPUs alone are not enough. Composed GPUs, working together across the device-to-cloud continuum, are what unlock the next generation of Physical AI. For OEMs already building on NVIDIA, mimOE is the layer that turns hardware into a mesh of intelligent agents, ready for the agentic, device-first era. Curious how a device-first AI fabric fits your roadmap? Let's start the conversation. hubs.ly/Q04mzpdg0 #PhysicalAI #NVIDIA #mimOE #DeviceFirstAI #OnDeviceAI #Robotics #Humanoids #OEM #AIInfrastructure #DistributedCompute #ContinuumOfCompute #AgenticAI #EmbeddedAI

Hot take: the CPU is the most underrated piece of the Physical AI stack. At Computex, @Intel is pushing a consistent message: as AI workloads spread from client to device to cloud, the CPU is resurging as a critical engine, complementing GPUs/accelerators and anchoring open platforms across the installed base. Whether you love that narrative or not, it points to something real in real-world Physical AI deployments: Runtime coordination lives on the CPU. Scheduling, networking, storage, security policy, and service-to-service coordination don't disappear just because you added a GPU. On-device deployments are heterogeneous by default. Real deployments mix #CPU + #GPU + #NPU + specialized I/O and must degrade gracefully when links or power budgets change. "Agentic" workloads multiply services. More agents mean more distributed processes, more identity boundaries, and more need for a robust runtime layer. The takeaway for Physical AI builders: optimize inference, yes, but treat the runtime as the product. The differentiation will come from how intelligently you compose, govern, and secure agents and microservices across the Device-First Continuum. That runtime layer is exactly what we built mimOE for. See how it runs across real device-first deployments: hubs.la/Q04mynqb0 #PhysicalAI #AgenticAI #AIInfrastructure #EdgeAI #OnDeviceAI #mimOE #Computex

Physical AI doesn’t fail because inference is slow. It fails because fleets are hard to operate. In the last 12 months, we’ve gotten used to talking about TOPS, quantization, and “AI at the endpoint device level.” But when deployments go from 10 devices to 10,000, the real problem becomes: how do you govern and update distributed intelligence without turning ops into chaos? Two signals worth paying attention to: • More vendor content is converging on multi-cluster fleet management as the unit of scale, workload placement, policy enforcement, lifecycle automation, and observability across heterogeneous environments. •“Orchestration platforms” are positioning themselves as the missing layer that unifies networking, security, and updates across distributed device fleets. ✅ Our POV: the next Physical AI winners won’t just ship models, they’ll ship choreography. Distributed agents and microservices that can be versioned, governed, and composed locally (where latency and sovereignty matter) while still being managed globally. What’s your hardest device fleet problem today: software updates, security policy, or observability? #EdgeComputing #PhysicalAI #DeviceInference #AIInference #EdgeAI #IoT #Kubernetes #MLOps #ZeroTrust #DistributedSystems

World models are no longer “just for simulation.” They’re becoming the post-training interface for real robots. A paper last week on Human-in-the-World-Model (Hi‑WM) reframes the world model from a planning toy into an operational loop: let policies roll out in an action-conditioned world model, then let humans intervene on failures via rollback + branching, generating dense corrective data, without burning hardware time or risking unsafe behavior. Three implications I think the industry is underestimating: • Iteration economics change. The bottleneck shifts from “robot availability” to “how fast can you correct and curate use cases.” • Safety becomes architectural. Human correction happens in a controlled virtual substrate before it ever touches production fleets. • Deployment gets a new requirement: the runtime must support policy updates as modular services (versioning, canary rollout, rollback) across heterogeneous sites. This is where the device-first continuum matters: post-training can be choreographed across devices, and policy execution + monitoring can live at the endpoint device. The winners will be the stacks that treat “robot intelligence” like distributed agents and microservices, not a monolith. If your robotics program shipped a policy update weekly, what would break first: data ops, safety sign-off, or fleet rollout? #PhysicalAI #Robotics #WorldModels #EdgeAI #FoundationModels #MLOps #DigitalTwin

Digital twins are graduating. From pretty 3D models… to the operating system of industrial decision-making. Digital twins are shifting from visualization to an operational control plane, because AI needs a continuously updated “world” to reason about. EE Times reported how #Siemens’ Digital Twin Composer, paired with #NVIDIA Omniverse libraries, is being positioned to build physics-informed twins that connect live operational data from endpoint devices to cloud-scale simulation and AI. Three implications worth calling out: 1) The twin becomes a decision loop. Not “plan once,” but simulate → decide → deploy → learn, continuously. 2) Robotics is the forcing function. When you simulate hundreds of robots (fleet-level behavior), your architecture has to handle latency, safety, and distributed execution. 3) Device-to-cloud data flow is the real moat. The value isn't only the twin, it's the ability to stream, govern, and act on operational data across sites. 4) The next platform battle is orchestration. You need a way to choreograph many services (perception, planning, safety checks, identity) across heterogeneous endpoints, without losing trust. In other words, the “industrial metaverse” is less about VR, more about turning the physical world into a computable, governable system. Do you see digital twins as a design tool or as a runtime control plane? What’s missing to make it operational? #DigitalTwin #Industry40 #Robotics #ContinuumAI #Omniverse #Manufacturing #PhysicalAI @Fayarjomandi @siavashalamouti @SamArmani

Building #AIAgents is one thing. Seeing them work, across every model, every node and endpoint device, every routing decision, every trace, that is where things usually fall apart. You jump between tools. You stitch dashboards together. You write CLI commands to figure out what a single agent just did and why it made the call it did. We've been building something different. Test your AI models on your device, then turn them into Agentix-native infrastructure. One workstation. Your agents, your models, your nodes. Every routing decision, every trace, every token. All in one view. No Prometheus to spin up. No Grafana to wire in. No Jaeger to configure. #mimOEStudio. Coming soon. #AI #AIAgents #Developers #DeveloperTools #AgenticAI #PhysicalAI #mimOE @Fayarjomandi @siavashalamouti @SamArmani

Industrial AI isn’t being “adopted.” It’s being productized into modules. A quiet shift is happening in industrial IoT: the fastest path to on-device AI isn’t custom builds, it’s pre-integrated, enterprise-grade modules paired with a turnkey ML workflow. This week’s example: @silextechnology and @EdgeImpulse (#Qualcomm) teaming up to deliver edge AI on the EP-200Q module. What to notice strategically: 1) Buying behavior is changing. Factories and healthcare systems want components that are already qualified for reliability, lifecycle, and security. 2) Toolchains are becoming the “default OS.” The winning silicon isn’t just TOPS, it’s the developer experience from data to deployment. 3) On device AI will look like fleets, not devices. Once you ship 10,000 endpoints, you need consistent rollout, observability, and policy enforcement. 4) The architecture opportunity: treat inference, monitoring, and governance as microservices that can be distributed across nodes and clouds, without rewriting apps for each environment. The next wave of industrial AI will be decided by who can run a hybrid, secure, updatable on-device cloud continuum at scale. If you’re deploying on-device AI today, what’s harder: model development, device qualification, or fleet operations? #IndustrialIoT #AIInference #Manufacturing #IoT #EmbeddedSystems #Security @Fayarjomandi @siavashalamouti @SamArmani

Your prototype works. Production doesn't. The gap between "it runs on my laptop" and "it runs where the business needs it" is where most AI projects stall. We've been closing it. 💫 #mimOEStudio. Coming soon... #AI #DistributedSystems #PlatformEngineering #mimOEStudio #AI #DeveloperTools #AgenticAl #AIEngineering #AIDeployment #ModelDeployment #GGUF #LLMOps #GenA #OnDeviceAl #LocalLLM #DevEx

One device running an AI model is easy. Multiple devices, scattered across locations, some online, some not, all running different models? That's a whiteboard exercise no one wants to do. What if the nodes could find each other, coordinate on their own, and show up on the same screen? 💫 We've been working on exactly that. #AI #DistributedSystems #PlatformEngineering #mimOEStudio #AI #DeveloperTools #AgenticAl #AIEngineering #AIDeployment #ModelDeployment #GGUF #LLMOps #GenAI #OnDeviceAl #LocalLLM #DevEx

What if there was a better way to see what's happening inside your AI runtime? And an effortless way to talk to it? What if one simple interface let you build, execute, operate, and scale, all from the same place, with a click? At mimik, we've been thinking about the same problems you are. And we've been working on something for it. Stay tuned. Something exciting is coming. #mimOEStudio #AI #DeveloperTools #AgenticAI #AIEngineering #AIDeployment #ModelDeployment #GGUF #LLMOps #GenAI #OnDeviceAI #LocalLLM #DevEx

One brain handles mechanics; the other mingles with the environment. Hardware meets social skills. Position #mimOE + #ROS as the dual-brain approach for intelligent robots that discover, collaborate, and share knowledge across fleets. #mimik #ARM #PhysicalAI #DFCAI #Nvidia #AMD #AgenticAI #AgentixNative #EdgeAI #DeviceFirst #AIArchitecture #DistributedSystems #Microservices #OnDeviceAI #AIInfrastructure #IndustrialAI #Agentix

If your AI setup comes with a 12-step installation guide, three #config files, and a cloud account you never asked for... there’s a better way. This quick walkthrough shows how to get #mimOE running on Mac and Linux in literally 2 steps. One command. That’s it. The script handles everything. No hashtag#cloud. No #API keys. Just your machine doing exactly what you tell it to. If you’re a developer tired of the gap between “it works in the notebook” and “it runs in production,” this is worth 2 minutes of your time. 🔘 developer.mimik.com 🔘 github.com/mimik-mimOE/mi… #PhysicalAI #AgenticAI #AgentixNative #DeviceFirst #EdgeComputing #ContinuumAI #EdgeAI #AgenticSystems #AI #Innovation #IndustrialIoT #Partnerships #SOAFEE #Automotive #MCP #OnDeviceAI #LocalAI #AIInference #AIEngineering #MLOps #DevTools #DeveloperCommunity @nvidia @NVIDIAAI @TechCrunch @ollama @huggingface @linuxfoundation @LangChain @github @Fayarjomandi @siavashalamouti

Last chance to experience # PhysicalAI that you can touch, test, and deploy. embedded world Exhibition&Conference 2026 ends today, but the systems we've demonstrated this week will define industrial automation for the next decade. You've heard the vision: machines that sense, reason, act, and learn continuously. Systems that adapt in real time. Intelligence that lives at the edge and operates when everything else fails. Now you can see it working. If you're architecting the future of manufacturing, logistics, security, or smart infrastructure, then this is the conversation that matters. → Hall 4, Booth 4-504. Don't leave Nuremberg without stopping by. Learn more about mimik at: mimik.com #ew26 #embeddedworld #PhysicalAl #AgenticAl #AgentixNative #DeviceFirst #EdgeComputing #ContinuumAl #EdgeAl #AgenticSystems #AI #Innovation #IndustrialloT #Partnerships #SOAFEE #Automotive #mimOE #MCPNews #MCP @nvidia @NVIDIAAI @NVIDIADRIVE @AdvantechEurope @Advantech_eIoT @linuxfoundation @Edge_IR @bosh @TechCrunch @WIRED @IEEEorg @Fayarjomandi @siavashalamouti @SamArmani