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
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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
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Intro Imagine spending weeks training a model, only to realize that deploying it efficiently across devices - from cloud GPUs to Raspberry Pi boards - feels like a whole new challenge. That’s where ONNX Runtime comes in. Built by Microsoft and backed by an open community, ONNX Runtime (ORT)
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Intro When you look at a photograph, your brain instantly infers which objects are near or far - even though the image itself is flat. Machines don’t have this innate ability. Monocular depth estimation teaches computers to infer depth using just one camera, without stereo vision or LiDAR. In
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Intro Devices in the field continuously send motion, temperature, and power data. In connected pet‑tech products like those at Hoomanely, spotting unusual patterns right on the device can prevent bigger issues - like a dog’s collar overheating or a tracker battery draining too quickly. Local anomaly detection lets
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How AI models are shrinking without losing their minds. Many of our real-time models - from detecting dog motion to analyzing eating patterns - run directly on embedded systems. At Hoomanely, we combine lightweight CNNs (like MobileNet) with quantization and pruning to achieve low-latency inference without sacrificing reliability. The ability
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Introduction We’re building technology that helps us understand pets better - not just through visuals, but through sound. Dogs communicate through subtle cues: the rhythm of a chew, a quiet gulp, or a bark that changes tone when they’re anxious. At Hoomanely, our smart bowls and monitoring systems
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Introduction In specialized fields, smarter systems are the goal. While scaling large language models (LLMs) is popular, real improvements often come from optimizing the backend. Retrieval-Augmented Generation (RAG) pipelines, particularly chunking, are crucial for performance. At Hoomanely, we faced issues with irrelevant responses due to poor chunking. Instead of using