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
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
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
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,
Introduction Transfer learning usually feels like a shortcut that just works. Pretrain on a large dataset, fine-tune on your task, and let scale do the heavy lifting. That approach worked well for us in vision - but audio broke that assumption in a very specific, painful way. At Hoomanely, we
Audio classification on edge devices presents a unique engineering challenge that doesn't get enough attention in the ML community. While everyone's building massive transformer models that need GPU clusters, we needed something that could run on a Raspberry Pi and accurately detect different types of dog
In the lifecycle of any machine learning project, the most painful phase is often the very first one: data curation. Before you can train a model to detect a dog bark, you need thousands of labeled examples of dog barks. But where do those labels come from? In computer vision,
AI systems are no longer passive observers. What began as chatbots, summarizers, and recommendation engines has evolved into systems that actively influence application behavior and persistent state. AI now classifies events, prioritizes alerts, triggers workflows, escalates anomalies, and shapes timelines that users and downstream systems rely on. This shift fundamentally
For a long time, retrieval lived quietly in the background. It powered search boxes, filters, and internal tooling, important, but rarely the center of attention. AI changed that. In modern AI-backed products, retrieval is no longer an implementation detail, it sits directly on the critical path between user intent and
Edge AI is transforming how devices interact with the real world, from smart speakers to pet care devices. A critical challenge in deploying these models is robustness in varied acoustic environments. While collecting massive labeled datasets can help, real-world constraints often limit data availability, especially for specialised domains like pet