AI
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
Software
As AI systems move from experimental features to core product capabilities, the definition of “good intelligence” changes. In early stages, intelligence is judged by fluency, creativity, or raw problem-solving ability. In production systems, those qualities matter far less than consistency, predictability, and control. A production AI system does not operate
AI
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
Hardware
Introduction: Signal Integrity as a System Property In the world of hardware development, "prototype" is often synonymous with "experimental." It implies a level of instability that is acceptable for a proof-of-concept but disastrous for a product. However, in the vBus ecosystem—where CPU SoMs, Power SoMs,
Software
The DOM Performance Ceiling When architecting our IoT monitoring platform at Hoomanely, we knew we'd be dealing with device data that scales exponentially. Our device status dashboard needed to display real-time information across hundreds of connected devices—device IDs, user details, heartbeat status, timestamps—all updating constantly. Drawing
AI
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
AI
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
Software
We start simple—a few functions, clean handlers, everything works beautifully. Then our application grows, and we need to do more work than fits inside a single request. Generate AI insights. Send emails. Trigger analytics. Run follow-up jobs. And the temptation appears: "Let's just spin up a
AI
Introduction Audio is one of the hardest signals to work with in real-world ML systems. Unlike images, it is continuous, noisy, and deeply tied to the environment it is captured in. At Hoomanely, we build smart pet products that live inside homes - kitchens, living rooms, and feeding areas -
Technology, Insights and Ideas.
Hardware
Introduction: When Geometry Becomes a Mechanical Constraint Flat PCBs prefer flat worlds. Curved enclosures do not offer that luxury. In products like our bowl, industrial design demands organic forms—continuous curves that feel natural in the hand, integrate seamlessly into living spaces, and support fluid interaction. But behind that curvature
AI
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
Software
As AI systems move from experimental features to core product capabilities, the definition of “good intelligence” changes. In early stages, intelligence is judged by fluency, creativity, or raw problem-solving ability. In production systems, those qualities matter far less than consistency, predictability, and control. A production AI system does not operate
AI
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
Hardware
Introduction: Signal Integrity as a System Property In the world of hardware development, "prototype" is often synonymous with "experimental." It implies a level of instability that is acceptable for a proof-of-concept but disastrous for a product. However, in the vBus ecosystem—where CPU SoMs, Power SoMs,
Software
The DOM Performance Ceiling When architecting our IoT monitoring platform at Hoomanely, we knew we'd be dealing with device data that scales exponentially. Our device status dashboard needed to display real-time information across hundreds of connected devices—device IDs, user details, heartbeat status, timestamps—all updating constantly. Drawing
AI
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
AI
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
Software
We start simple—a few functions, clean handlers, everything works beautifully. Then our application grows, and we need to do more work than fits inside a single request. Generate AI insights. Send emails. Trigger analytics. Run follow-up jobs. And the temptation appears: "Let's just spin up a
AI
Introduction Audio is one of the hardest signals to work with in real-world ML systems. Unlike images, it is continuous, noisy, and deeply tied to the environment it is captured in. At Hoomanely, we build smart pet products that live inside homes - kitchens, living rooms, and feeding areas -