When you ship a hardware product with sensors, you inherit a problem that pure software companies never face: the real world is messy. At Hoomanely, our smart bowl, Everbowl, uses load cells to measure food and water consumption and thermal sensors to track a dog's body temperature. In
Long before a processor boots or a sensor streams data, a user forms an opinion. That opinion is shaped not by firmware or electrical architecture, but by what they see, hold, and feel. In product engineering, the enclosure is the first interface, and often the most unforgiving one. At Hoomanely,
Flutter makes it easy to build good-looking apps fast. Early on, everything feels smooth: a few screens, some setState, and it just works. Then the app grows. You add chat screens, pagination, animations, API calls, offline storage, and suddenly the UI starts flickering, pagination behaves randomly, scroll position jumps, images
Building high-performance camera systems for edge AI means mastering the dance between hardware peripherals, memory architecture, and real-time constraints. When developing precision imaging for pet healthcare monitoring, achieving consistent capture rates with zero data loss is the whole game. The challenge: zero-copy high-speed imaging Traditional microcontroller camera implementations often struggle
Problem Modern pet‑health systems increasingly rely on thermal cameras to extract physiological signals such as eye temperature. The challenge is that thermal sensors are low‑resolution and almost never share the same optical center, field of view, or distortion model as the RGB camera doing the visual perception. In
The decision we didn't question When we started building our edge systems, the filesystem was the easiest decision we made. It was familiar, stable, and came with decades of engineering behind it. Logs went into files. State got persisted as small blobs. Recovery was something the OS already
Every machine learning project has a dirty secret: the model is usually not the problem. When a model underperforms, the instinct is to reach for a bigger architecture, more layers, or fancier training tricks. But in our experience building machine learning systems at Hoomanely, the culprit is almost never the
In embedded systems, speed and stability are often framed as opposing forces. Fast iteration is seen as something you do early, before the "real" system exists. Reliability is something you enforce later, once hardware is fixed and features are locked. In practice, this separation is artificial, and costly.
When we talk about smart home devices, the conversation inevitably drifts toward a single, uncomfortable word: privacy. At Hoomanely, we build technology that lives in the spaces of a pet parent's home. Our flagship product, Everbowl, is a smart feeding system that tracks a dog's hydration,
Filesystems in embedded systems usually get treated as an implementation detail: picked late, configured with the defaults, and rarely touched again. In practice, a filesystem becomes a long-lived contract between firmware, storage media, update mechanisms, and whatever data pipeline sits on top of it. This post looks at embedded filesystems