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The 2026 Embedded Online Conference
Quickfire Heuristics: A Fast Usability Evaluation Framework for Lean Hardware Teams

Quickfire Heuristics: A Fast Usability Evaluation Framework for Lean Hardware Teams

Emmanuel Odunlade

That device with the single LED that requires you to count blink patterns just to understand system status. The button you must hold for 8 seconds, which also performs four other actions depending on hold duration. These are not accidents of negligence; they are the predictable output of development processes that have no rigorous usability evaluation component. Usability tends to slip through the gaps of standard engineering reviews, surfacing late, when design flexibility is already gone. This article introduces a framework that adapts Jakob Nielsen's Ten Usability Heuristics, for hardware and embedded systems, translating each principle into concrete evaluation questions for physical interfaces, firmware state machines, constrained displays, and cross-layer interactions. Using a smartwatch as the running example, it also introduces a structured session format, maps the framework to key lifecycle stages, and extends it to manufacturing, test, and field service contexts.


Embedded Linux Board Farms 101: The Requirements That Actually Matter

Embedded Linux Board Farms 101: The Requirements That Actually Matter

Drew Moseley

When you keep your embedded Linux boards in a rack or remote lab, the "plug in HDMI" workflow breaks down fast. One bad kernel push and SSH never comes back. This post lays out the core requirements for a real board farm: out-of-band serial console access, remote power cycling, and scripted reimaging so you never need someone on-site who knows Linux. Once those primitives are in place, everyday smart home devices — Tasmota switches, Home Assistant, environmental sensors — become legitimate development tools that bring enterprise lab capabilities to a hobbyist budget. Includes a pre-flight checklist for transitioning from KVM-style access to a fully remote setup, and a preview of the full implementation presented at the Embedded Online Conference in May.


Small Language Models (SLMs): The Future of AI is Smaller, Faster, and Closer to the Edge

Small Language Models (SLMs): The Future of AI is Smaller, Faster, and Closer to the Edge

Rohit Gupta

AI industry is shifting from a "bigger is better" mentality to a focus on efficiency, localization, and real-world utility. The article argues that the AI industry is pivoting from massive, cloud-bound models toward Small Language Models (SLMs) designed for efficiency, speed, and edge deployment. Driven by the need to overcome cloud-centric hurdles like high latency, bandwidth costs, and privacy risks, SLMs (ranging from 100M to 14B parameters) leverage architectural innovations such as quantization, sparse attention, and high-quality synthetic data to deliver specialized intelligence on local hardware. Rather than replacing large models, SLMs represent a shift toward a hybrid intelligence future where the cloud provides depth while the edge provides real-time, sustainable action, ultimately moving the focus of AI progress from raw parameter count to practical, real-world utility.


Debug, visualize and test embedded C/C++ through instrumentation

Debug, visualize and test embedded C/C++ through instrumentation

Pier-Yves Lessard

Instrumenting a firmware is a highly effective methodology for debugging and testing an embedded softwares. In this article, I will present a way of achieving this using Scrutiny, an open-source software suite developed as a personal initiative, designed to streamline debugging, telemetry, and hardware-in-the-loop (HIL) testing for embedded devices.


Can an RTOS be really real-time?

Can an RTOS be really real-time?

Miro Samek
TimelessAdvanced

Real-Time Operating Systems are meant for real-time applications. But with conventional shared-state concurrency and blocking, can you honestly know the worst-case execution time of an RTOS thread?


Always-On Intelligence Without the Cloud: Why it matters more than you think

Always-On Intelligence Without the Cloud: Why it matters more than you think

Shivangi Agrawal
Still RelevantIntermediate

Much of the AI conversation today is still focused on scale: larger models, more data, more compute. Embedded systems live in a different reality, where constraints are unavoidable, and efficiency is the priority. What’s emerging is not a smaller version of cloud AI, but a different approach altogether, the one that values locality, predictability, resilience, and trust. Always-on intelligence without the cloud isn’t just a technical milestone. It’s a change in how we think about where intelligence belongs.


Designing for Humans: Viewing DFM and Industrialization Through the Lens of the Fitts MABA–MABA List

Designing for Humans: Viewing DFM and Industrialization Through the Lens of the Fitts MABA–MABA List

Emmanuel Odunlade
Still RelevantAdvanced

"Operator’s fault" and "Inadequate Training" are the phrases you typically hear when yield loss and stubborn manufacturing issues are discussed. While these factors may play a role, they rarely tell the whole story. This article views DFM and industrialization through the lens of a classic human factors principle; the Fitts MABA-MABA list, and highlights a critical, yet less-discussed factor: the lack of manufacturing-focused human factors considerations in product design. It explores practical examples like Proprioceptive Fatigue and Visual SNR, and shows how lots of chronic manufacturing issues are results of bad upstream design decisions, echoing the fact that in many cases, inspection exists not because it is inherently valuable, but because the design failed to encode correctness directly into the product or process. If you’ve ever wondered why "retraining" never seems to fix a recurring defect, this take on industrialization and manufacturing might explain why.


Stuck with Jira — and Stuckons

Stuck with Jira — and Stuckons

Jason Sachs
Still RelevantIntermediate


The 2026 Embedded Online Conference