Big tech has the resources, but startups have the real advantage in AI wearables: speed, agility, and the freedom to take risks. Right now, the AI wearable market is in the wildcard phase—no dominant device, no set form factor, and no clear winner. That’s a massive opportunity for smaller teams that can move fast, test in the field, and refine in real time. Unlike big tech, startups don’t need a five-year roadmap. They can launch quickly, experiment aggressively, and pivot without worrying about shareholders.

Meta is making a massive push into AI wearables, with at least six new devices launching in 2025. But here’s the catch—this wasn’t originally about AI. Meta built its hardware for the metaverse, only to find itself at the center of the AI revolution. With over 1 million Ray-Ban smart glasses already sold (and a goal of 5 million in 2025), it’s clear there’s demand. But can Meta actually scale this initiative from within, or will they lean on brand partnerships like Oakley to expand?

How can engineering teams have a bigger impact on the bottom line? By thinking beyond code. Most engineers love to build and solve problems. But in a business, building for the sake of building isn’t enough. Even the cleanest code is just an expensive distraction if it doesn’t move the needle.

Some IoT companies are making money; others are leaking it. Margins in IoT are already tight, but many brands are losing cash in ways that are completely preventable. RMAs, bloated customer support costs, churn, and on-site technician visits all add up. Too many companies default to replacing hardware instead of fixing the code. Without OTA updates and remote diagnostics, budgets get drained by unnecessary shipping and support costs.

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.