On-device AI
On-device AI coverage belongs in AI Trends. Fast-moving themes across research, products, and adoption.
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ModelsUnscheduled
Liquid AI's vision-language acceleration work keeps edge AI in view
Liquid AI's LFM2.5-VL acceleration work matters because vision-language models are moving into workflows where latency and device constraints are as important as benchmark scores.
AgentsSep 22, 2026
Rabbit trying to escape the R1 shows agent software matters more than gadgets
Rabbit's OS3 story is important because it shows the agent category moving beyond a single hardware bet. The Verge reports that Rabbit's new AI agent no longer needs the R1 device, which is a quiet admission that the product value has to live in the software workflow.
CompaniesSep 15, 2026
Exein's funding points to a new market for AI defending connected devices
Financial Times reporting on Exein's large funding round is a reminder that AI security is moving beyond chatbots and cloud software. The Rome-based company is building foundation-model-style defenses for connected devices, where attacks can reach cars, factories, appliances, and industrial systems.
RoboticsAug 27, 2026
Anthropic's physical-world standard shows agents need hardware rules too
Software agents already make people nervous because they can touch files, browsers, repositories, and accounts. Physical-world agents raise the stakes again. When an AI system can interact with devices, machines, sensors, or robots, failure is no longer confined to a screen.
InfrastructureAug 27, 2026
NVIDIA's Jetson push puts edge hardware back into the physical-AI race
NVIDIA’s Jetson push is a reminder that physical AI will not run entirely from distant cloud data centers. Robots, drones, cameras, and industrial systems often need decisions close to the device, where latency, bandwidth, power, and reliability matter.
InfrastructureAug 28, 2026
The AI infrastructure boom is spreading into networking, edge, and robotics
The first phase of the AI infrastructure boom was easy to describe: everyone needed GPUs. The next phase is messier and more important. AI systems now need faster networks, better inference stacks, power contracts, data-center automation, edge devices, and deployment tooling that can keep products online.