Edge Impulse and Arduino are a central part of this embedded world 2026 chat with Linda Watkins,...
AI with Edge Impulse and Arduino is the focus of an upcoming Elektor Engineering Insights show on April...
At embedded world 2026, Brian Tristam Williams spoke with Manny Singh from Qualcomm about how...
Day 2 of embedded world 2026 focused on rapid prototyping, modular systems, and...
It's Day 1 at embedded world 2026 in Nuremberg. Here we report on new tech and industry trends...
Smart devices respond to wake words like “Alexa,” “Hey, Siri,” or “OK, Google” using a machine-learning...
Machine learning enables real-time PCB defect detection using a FOMO model on a Raspberry Pi. Learn...
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The complete Edge Impulse Guest-Edited Bonus Edition is now available. To enjoy all the projects and...
The third installment of the Edge Impulse Guest-Edited Bonus Edition is ready. Visit Elektor's Edge...
The Thundercomm Rubik Pi 3 combines Raspberry Pi-style usability with Qualcomm’s AI performance. Explore...
More edge AI content awaits you. Head over to Elektor's Edge Impulse page and download the second installment...
Modern Vision Language Models can now run on low-cost edge hardware like the RUBIK Pi 3. With its...
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From hands-on prototyping and lab experimentation to magazine highlights and special collaborations,...
Discover how Advantech is driving the shift from cloud to Edge AI with tools that boost performance,...
Historically, creating an ML model and deploying it to an embedded device required considerable manual...
Designing energy-efficient embedded systems is challenging, and Edge AI adds to the challenge. Balancing...
Elektor welcomes Edge Impulse as its 2025 guest editor for an in-depth edition dedicated to the...
Join us live on December 11 for an exclusive Elektor webinar with Daniel Situnayake from Edge...
Edge Impulse is a powerful, streamlined tool for creating machine learning models for embedded devices....
The Thundercomm Rubik Pi 3 blends Raspberry Pi familiarity with Qualcomm AI muscle. Discover how...
This project uses machine learning to automatically adjust windows and louvers for comfort and energy...
In today’s world, voice commands are becoming a popular user input method for various devices, including smart...
As edge devices grow more powerful, AI is shifting away from the cloud and closer to where data is generated....
From early ML experiments at a hackathon to running models on billions of devices, Jan Jongboom has...
HP’s new headsets come with a unique feature made possible with an Edge Impulse collaboration. The voice-control function...
Arduino enters the AI era with the acquisition by Qualcomm Technologies, merging maker simplicity with...
Vision Language Models are generative AI models that take in images and text prompts. Some of the latest...
Designing energy-efficient embedded systems is challenging, and Edge AI adds to the challenge. Balancing...
Ready to add AI to your ESP32 Energy Meter? In this project, you’ll learn how to use Home Assistant data...
Edge AI is here — faster, smarter, and closer to the data. In Elektor’s upcoming guest-edited edition...
Long before today’s AI boom, Elektor was documenting the early foundations of artificial intelligence through...
OpenMV, the "Arduino of machine vision," combines MicroPython firmware, easy-to-use libraries, a custom...
In our interview with Sergi Mansilla from Edge Impulse at embedded world 2025, we got an update on...
Qualcomm is acquiring Edge Impulse to expand its AI and IoT capabilities, enhancing on-device AI performance...
Discover how the Arduino Portenta X8 can be used with Edge Impulse for your Edge AI projects in this...
The news around AI mainly reports on the achievements of tools like ChatGPT and Midjourney —...
We’re used to interacting with devices using a keyboard, mouse, or touch screen, but sometimes we seek...
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The team tackled the pressing issue of rising mosquito populations and their role in disease transmission,...
With TensorFlow Lite for Microcontrollers, you can run machine learning models on resource-constrained...
TensorFlow Lite for Microcontrollers enables running machine learning models on small, resource-constrained...
Discover how you can transform a Raspberry Pi and a camera into a smart object-counting tool using the...
Want to transform a Raspberry Pi and a camera into a smart object-counting tool using Edge Impulse?...
Many embedded systems challenges can't be resolved with rules-based programming. Increasingly, developers...
Machine learning can enable positive change in society, says Adam Benzion, Chief Experience Officer...
[Partner Content] Join the Edge ML Series, exclusive invite-only events for key leaders on the journey...
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Integrating App Lab and Edge Impulse with Alessandro Grande and Marta Barbero — EEI #58
Alessandro Grande, Director of Edge AI Developer Growth at Edge Impulse, and Marta Barbero, Lead Product Manager at Arduino, will join Elektor for a practical Elektor Engineering Insights session, Integrating App Lab and Edge Impulse, on Wednesday, April 8, 2026, at 16:00 CEST. They will discuss how the tighter connection between Arduino App Lab and Edge Impulse could make the path from data collection to on-device inference less fiddly for embedded developers.
The session will look at how engineers can move more smoothly from captured data to trained and deployable models, what that means for work involving vision, audio, sensor fusion, and robotics, and where smaller task-specific models can make more sense than larger generic ones. The discussion will also touch on constrained hardware, cascades of models, and what platforms such as UNO Q and VENTUNO Q make more practical.
What You’ll Learn in This Elektor Engineering Insights Episode
- How Arduino App Lab and Edge Impulse fit together in a practical edge AI workflow
- How developers can move from data capture to deployable models with less friction
- Where smaller task-specific models can outperform more general ones
- Why cascades of models can be useful on constrained hardware
- What kinds of projects become more practical on UNO Q and VENTUNO Q
- How vision, audio, sensor fusion, and robotics fit into this emerging workflow
Register now!
Webinar: The Future of Physical AI with Edge Impulse with Daniel Situnayake (Edge Impulse)
Daniel explained how engineers can take advantage of modern edge hardware, what Qualcomm’s acquisition of Edge Impulse means for developers, and how to build reliable AI systems that run locally — faster, safer, and without cloud dependency. He also shared insights from his books TinyML and AI at the Edge, and expanded on themes from his article in Elektor’s Edge Impulse Guest Edition.
What You’ll Learn in the Webinar
- How edge AI differs from cloud AI, and why it’s rapidly becoming the default
- How foundation models enable tiny datasets and unlock long-tail applications
- Practical hardware considerations for running ML on MCUs, NPUs and hybrid processors
- New directions in robotics and “physical AI”
- Real-world examples of products already shipping with on-device intelligence
- How to get started with Edge Impulse and build deployable ML models quickly
- A look into the future of intelligent devices and local decision-making
Watch now!
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