Login
Logout
Login
Logout

Search

The Alif Blog

March 30, 2026

Edge AI Meets Wearables: Inside Everysight’s Maverick Smart Glasses Powered by Alif Ensemble E7

January 26, 2026

Stuck on how to get the best value out of edge AI? Here’s some inspiration.

January 20, 2026

Growing MCU capabilities shift more AI from the cloud to the edge

Search

Categories

March 30, 2026

Edge AI Meets Wearables: Inside Everysight’s Maverick Smart Glasses Powered by Alif Ensemble E7

Everysight’s Maverick smart glasses show what happens when edge AI, optics, and system design finally come together. Powered by Alif’s Ensemble E7, the platform delivers real-time, on-device intelligence in a lightweight, all-day wearable form factor. This article explores how that balance enables practical AR and AI experiences that move smart glasses closer to everyday use.

Read More

January 26, 2026

Stuck on how to get the best value out of edge AI? Here’s some inspiration.

Explore practical examples of edge AI on microcontrollers, including voice, vision, vibration analysis, and ultra-low-power sensing, and learn how Alif Ensemble and Balletto enable intelligent embedded designs.

Read More

January 20, 2026

Growing MCU capabilities shift more AI from the cloud to the edge

Battery life (the time between charges) is a crucial figure of merit for many types of portable, wearable, mobile and remotely located devices, from smart glasses and smart watches to agricultural sensors for measuring soil conditions or animal health. We now know from research cited in an earlier blog that energy consumption can be at least five times lower when AI inference is performed locally on an ESP32-S3 system-on-chip (SoC) than when raw data is transferred wirelessly to the cloud for inference.

Read More

January 12, 2026

It’s official: local AI inference at the edge uses less power than the cloud

It is widely assumed that running AI/ML inference on-device in an edge microcontroller uses less power than transferring the raw data to the cloud, and then performing the inference on a cloud server. Intuitively, it seems obvious: an edge device such as an Ensemble or Balletto MCU has a much smaller power footprint than even a purpose-built AI inference server in the cloud. And when inference is performed locally, the system saves the power which would be used by an RF circuit to transmit data to the cloud.

Read More

loading

No results…

X

This field is for validation purposes and should be left unchanged.
(Required)