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AI isn’t predictable, it adapts, making embedded engineering even more complex. A model that works in the lab might fail in the real world. So, how do successful teams deploy AI at the edge? A/B test models in the field—controlled environments aren't enough. Collect real-world performance data—observability tools are key. AI deployment isn’t a one-and-done process. It requires constant iteration and real-world validation.

Fitbit was just fined $12M after Ionic smartwatches overheated and burned users. The issue? Lithium-ion batteries—powerful, but risky without proper safeguards. The best teams know you can’t catch every failure before launch. That’s why real-time monitoring is critical: Over-temperature protection isn’t enough without tracking trends. Live monitoring helps catch small issues before they become safety risks. Think about it: What if an e-bike motor overheats mid-ride? Or a smart oven malfunctions and starts a fire? Without monitoring, you’re gambling with user safety.

AI isn’t predictable, it adapts, making embedded engineering even more complex. A model that works in the lab might fail in the real world. So, how do successful teams deploy AI at the edge? A/B test models in the field—controlled environments aren't enough. Collect real-world performance data—observability tools are key. AI deployment isn’t a one-and-done process. It requires constant iteration and real-world validation.

Pebble OS is now open-source. Former Pebble engineers François Baldassari, Chris Coleman, and Brad Murray break down its innovations and why this release matters.

Security researchers found a massive flaw in Subaru’s remote vehicle system—hackers could unlock and track cars easily. The culprit? Homemade authentication protocols. Lesson: Don’t DIY security. Use trusted, third-party solutions. What do you think Subaru should have done differently?

Compliance documentation is a tedious but necessary part of embedded engineering. AI can help by automating structured tasks, drafting reports, checking for regulatory gaps, and validating submissions so that engineers can focus on development work. Would you trust AI-generated compliance documentation? Let’s discuss.

AI at the edge is built for embedded systems. And no need for tons of compute power— most of the heavy lifting happens during training so the models run efficiently on minimal hardware. With microcontrollers like STM32N6 optimizing for AI workloads, the potential is growing fast. Is AI at the edge part of your embedded strategy this year?

On this episode of Interrupt Live, we'll meet Noah Pendleton, an MCU SDK Engineer at Memfault. He'll share why he wrote the article "Publishing the Memfault SDK as an ESP-IDF Component. They discuss the development of an ESP component for the Memfault SDK, exploring Noah's background in firmware engineering, the process of creating and publishing the component, and the importance of automation in development. They also share insights on the ESP-IDF framework and future plans for further contributions to the community. takeaways.

Is hardware innovation slowing down? The basics—Wi-Fi chips, LEDs, controls—are solved for many devices. The real innovation is in software: user interfaces, intelligence, and connectivity. Where do you see hardware evolving?

Consumers crave interactivity. The best way to deliver it is to unite software and hardware. Product design used to be about perfecting hardware specs. The software was an afterthought. Today, this story is flipped on its head. The devices users love are built differently. The best embedded engineering teams co-design hardware and software from the start. It’s not just a shift in process; it’s a shift in thinking. Are we building products or creating experiences?