At Hoomanely, our embedded platforms power an ecosystem of modular IoT products where reliability, uptime, and predictability matter more than raw throughput. Our System-on-Module architecture spans sensing, communication, and processing nodes, all running continuously under diverse workloads, so memory allocation isn't a theoretical concern, it defines real-world reliability
Every embedded Linux engineer eventually reaches the same point: the need for a fully customized, lightweight, reproducible Linux distribution. Prebuilt OS images are bloated, build-from-scratch distros are fragile, and the Yocto Project solves this by giving you a structured framework to build a Linux system exactly as big as it
Hoomanely is a deep-tech hardware startup building intelligent IoT systems designed to be reliable, predictable, and humane in how they interact with users. Our engineering philosophy centers on robust embedded design, modular hardware pipelines, and rapid prototyping without sacrificing stability. In our early hardware development cycles, the Raspberry Pi Compute
The Microchip MCP2518FD is a popular external CAN-FD controller that interfaces with the host MCU over SPI. It performs well under controlled conditions, but its field reliability comes down almost entirely to the quality of the host-side driver. This post documents technical findings, failure signatures, and recovery patterns validated through
In modern imaging pipelines, especially ones running on edge devices, every byte matters. When an embedded system captures images, those images have to travel through limited memory, constrained communication buses, and strict power budgets. Large image payloads degrade system performance, slow down recovery pipelines, and create unpredictable behavior. To keep
Monolithic firmware scales linearly in features but exponentially in complexity. When building IoT devices that integrate multiple sensors, accelerometers, GPS modules, barometers, LoRa communication, and more, a firmware project built as one giant directory quickly becomes unmanageable. It creates compilation bottlenecks (change one sensor driver, recompile everything), dependency hell (unclear
Picture debugging a critical production issue where the logs show a sensor event was processed before it was captured. Impossible? No, just unsynchronized clocks. That was our reality building distributed embedded systems, since our platforms rely on multiple Systems-on-Module working in concert, each handling different sensors and processing tasks. Without
During prototyping of our EverBowl and EverHub hardware, we used the Raspberry Pi Compute Module 4 as our embedded compute platform for early-stage development, a prototyping vehicle ahead of transitioning to Hoomanely's custom, purpose-built System on Module for production. The CM4 requires specific initialization procedures to establish reliable
At Hoomanely, we set out to build connected pet products that could genuinely understand pet behavior and health through multiple sensors working together. Trackers needed to monitor activity, detect vocalizations, track location, and communicate over long distances. Smart bowls needed to measure food consumption precisely, detect presence, capture video, and
Eliminate server upload bottlenecks with presigned URLs, enable massive file uploads via multipart, and automate storage archival for significant ongoing savings. Building a media-heavy application means handling large files, video, images, documents, efficiently without bottlenecking your application servers. Whether you're processing video, managing user-generated media, or handling document