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Intro One of the hardest problems in smart pet devices isn’t sensing - it’s understanding context. At Hoomanely, our EverBowl feeding system can reliably detect when a dog is eating using weight sensors, proximity sensing, and on‑device vision. But knowing what the dog is eating is a
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Introduction Landmark detection models often look impressive during offline evaluation - clean datasets, sharp images, balanced lighting, and tidy annotations. But the real world is rarely that polite. At Hoomanely, we deploy dog‑face landmark detection models (eyes, nose, facial keypoints) on always‑on indoor devices mounted on feeding bowls.
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Introduction Over the past decade, cloud inference has powered most large-scale machine learning systems — from recommendation engines to speech assistants. But as devices at the edge get faster, cheaper, and more privacy-aware, a hybrid pattern is emerging: compute just enough on the device to protect the user and reduce noise,
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Introduction On edge devices, every millisecond and every megabyte matters. Audio models must run reliably on low-power CPUs, handle noisy real-world environments, and deliver consistent predictions without draining battery or storage. Traditional deep audio models—large transformers or multi-million-parameter networks—struggle here. They are powerful, but far too heavy for
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Introduction Running Python applications on edge devices is fundamentally different from deploying them on servers or developer laptops. These devices - whether embedded compute modules, IoT gateways, or sensor hubs - operate under strict storage ceilings and must deploy updates reliably without exhausting disk space. At Hoomanely, our edge systems
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Introduction Most computer vision models begin with a clean, carefully curated dataset. But real-world systems rarely stay that way. They evolve. New environments appear, edge cases emerge, and model failures surface in ways that the original training data could never fully capture. This is especially true when deploying lightweight models
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Noise cancellation sounds glamorous - until you try doing it in the real world. Dog sounds in homes are messy. Fans hum. TV dialogue spills into the mic. Steel bowls clink. A dog sniffs, licks, barks, scratches, and eats… all at the same time. A clean dataset of isolated dog
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Introduction Choosing the right language model architecture for production is one of the most underrated engineering decisions teams make. Behind every AI feature - from a chatbot response to a search ranking - sits a strategic decision: Should we use an encoder-only model? A decoder-only model? Or a full encoder–
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When engineers talk about Retrieval-Augmented Generation (RAG), the conversation often starts at embeddings, chunking, or vector databases. But the uncomfortable truth is this: RAG quality is bottlenecked by OCR quality. If the extracted text is noisy, fragmented, or wrongly segmented, no embedding model - no matter how powerful - can
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Detecting when something "sounds wrong" during a pet's mealtime seems simple - until you try to engineer it. Background noise, human speech, utensils clinking, bowls moving, fans humming, and wildly different pet eating styles create a dense acoustic jungle. Within this chaos, the goal is to