Firmware
Real-world lessons from transitioning parallel to serial camera architectures in production embedded systems Imagine this: an industrial vision system built around a high-performance MCU and a 1 MP camera using the traditional parallel interface (DCMI). It worked—until you added thermal sensors, proximity modules, and wireless connectivity to the same
Hardware
How We Made PCB Manufacturing at Hoomanely Predictable, Repeatable, and Fast In hardware, the real deadline is not when the firmware compiles or the prototype boots. The real deadline is when your manufacturer loads your files into their pick-and-place line — because that’s when surprises cost money. At Hoomanely, we
Software
Your sensors never sleep. An IMU chirps at 100–200 Hz, a load cell hums at 10–100 Hz, a microphone races at kilohertz. The instant you do real work—FIR/IIR filtering, debouncing, feature extraction—on the main isolate, your once-silky Flutter UI starts to stutter. The jank isn’
Software
Community groups power some of the most engaging features in modern apps—from fitness communities to professional networks. But building a safe, scalable group membership system is harder than it looks. Unlike ephemeral chat groups focused on real-time messaging, community groups are persistent spaces where members build lasting relationships, share
AI
Introduction When we look at an object, our brain effortlessly merges two slightly different images from our eyes to perceive depth and distance. Replicating this ability in machines, however, is anything but simple. At Hoomanely, where we design intelligent pet wellness systems, stereo vision plays a crucial role in understanding
Firmware
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 firmware projects quickly become unmanageable. A single, monolithic codebase where everything lives in one directory creates several critical issues: Compilation bottlenecks: Change one
In modern embedded systems—whether high-speed vision sensors, multi-channel audio acquisition, or edge ML inferencing—throughput and latency matter. When you run a full-blown embedded Linux platform yet also need real-time data processing, simple threading isn’t enough. You must structure threads, pick the right scheduling policies, bind threads to
Firmware
Networks misbehave in the real world. BLE links jitter, Wi-Fi bursts collide, buffers overflow, and low-power sensors occasionally flip a bit. The worst part isn’t the packet loss—it’s the visible glitch your user sees: a frozen chart, a jumpy graph, or a dropped audio blip. This post
AI
Intro: Vision That Starts with a Square At Hoomanely, we’re building smart systems that see and understand pets - from their eyes and movements to the bowls they eat from. To make this happen, our camera pipeline needs to detect key visual cues with precision and speed - even
Software
AWS migrations fail not because the technology is inadequate, but because the approach is wrong. Console-clicking through dozens of services, manually copying configurations, and hoping nothing breaks during cutover—this is how organisations rack up unexpected costs and endure hours of downtime. We at Hoomanely followed a better way: CLI-driven,