AI
The Uncomfortable Truth About ML Projects 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,
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The Unspoken Tension of In-Home AI 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
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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
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The "Cold Start" Problem in Audio AI 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
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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
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When your edge device captures thousands of audio clips in real-world conditions, the question isn't just "what's happening?" — it's "what's going wrong?" At Hoomanely, our Everbowl continuously monitors a pet to provide preventive healthcare insights for pets. But
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Machine learning promises automation at scale, but building reliable audio classification systems reveals a persistent challenge: ground truth validation. When we set out to build an edge-deployed dog sound classifier, we explored every path to automated labeling—and each one led back to the same conclusion: human perception isn'
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When your AI model performs flawlessly in testing but stumbles in production, you're likely facing one of computer vision's most persistent challenges: domain shift. At Hoomanely, we encountered this exact problem with our dog segmentation model deployed on the Everbowl, our edge device designed to monitor
Software
At Hoomanely, we're building a preventive pet healthcare platform for pet parents that has tools and features ranging from pet-parents community to a AI powered pet care chat assistant. Our mission demands rapid iteration—we need to test hypotheses quickly, deploy features fast, and scale efficiently without the
Software
Ask any pet parent about their anxious dog, and they'll give you a novel. Ask an AI assistant the same question without proper context, and you'll get generic advice that could apply to any animal, anywhere. Here's what we learned building Hoomanely's