#edgeai

PocketBeagle 2 Powers Offline: Clem Mayer built a cloud-free security camera on the PocketBeagle 2 that detects people locally and keeps every frame of video at home.

NVIDIA's Blackwell-powered Jetson T3000 and T2000 pack up to 865 teraflops of edge AI into modules half the size and power of the T5000.

AI today can write code, compose music, and even fly drones, but it still can't truly think. It reacts, predicts, and imitates. But it doesn't perceive. That's the...

NVIDIA's RTX Spark announcement raises the ceiling for local agents, but mobile and compact-PC value depends on lifecycle measurements.

Small models don't run on devices— they run on a stack of quantization kernels, compilers, and accelerators that quietly change their behavior.

Use a mobile measurement envelope for latency, network bytes, provider cost, and joules while keeping synthetic examples out of real-world conclusions.

A release envelope for edge tools that must survive changing power, connectivity, and permissions.

Home AI Server: Build a Private, Always-On Assistant at Home The idea of a home AI server...

ASUS UGen300 puts 40 TOPS of AI inference in a USB-C stick at 2.5W. How the Hailo-10H accelerator works, what models it runs, and what it means for on-device AI

We spent decades imagining a limb you don't just wear, but feel. Like Luke's hand. This year a lab built the part that always seemed impossible: the wince.

One redaction app ships 5.61 GB of model weights and holds nearly 2 GB of RAM. What happens when 15 apps on the same phone all do that? A practitioner's look at the storage, memory, NPU, and I/O contention I think on-device AI is heading toward.

An embedded and silicon engineer's take on why the constraints of edge AI feel familiar, and why the field needs people who understand both the model and the metal.