AI
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'
Software
Every social application eventually hits the "Feed Scaling Wall." It usually happens when your first power user follows their 500th account, and your once-snappy SELECT * query starts taking 200ms. Then 500ms. Then timeouts. To preempt this bottleneck at Hoomanely, we architected our system for scale from Day One.
Hardware
Introduction: When End Users Have Four Legs At Hoomanely, designing technology for pets presents unique engineering challenges that consumer electronics rarely encounter. Pets interact instinctively—through touch, taste, and repetition—without understanding operational boundaries or tolerating discomfort. When we developed EverBowl, every material decision was driven by a fundamental principle:
Firmware
Most performance problems we’ve encountered in embedded systems didn’t come from weak hardware or poor algorithms. They came from decisions that felt harmless at the time extra logs during bring-up, convenient abstractions, background helpers added “just for safety.” None of these broke the system immediately. Instead, they slowly
Hardware
Introduction: Hardware Meets Software Workflow Hardware design projects generate a wide range of files — schematics, PCB layouts, component libraries, manufacturing outputs, and documentation. Over the course of development, these files go through countless revisions as designs move from prototyping to testing and finally to production. Without a structured version control
Firmware
Introduction Ever wondered what happens in those crucial milliseconds between powering on your smart device and seeing it spring to life? The journey from a cold hardware reset to a running application is one of the most fundamental processes in embedded systems—yet it remains invisible to most users. Unlike
AI
Introduction Building machine learning systems for the edge sounds exciting—until you realize that most real-world edge problems begin with a simple, uncomfortable truth: you don’t have labeled data. Not enough of it. Not the right kind. And certainly not data that reflects the messy environments your devices will
Software
Hoomanely products are no longer just mobile apps or hardware devices in isolation. They are systems—a combination of sensors, firmware, mobile applications, dashboards, and cloud intelligence. At Hoomanely, as we build connected pet devices and real-time insights for pet parents, one recurring question keeps coming up: Can we stream
AI
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
Modern backend systems rarely fail loudly. They fail quietly—through duplicated requests, partially completed workflows, retried executions, and replayed messages that look successful but slowly erode correctness. On AWS, these behaviors are not edge cases or misconfigurations. They are the default operating conditions of a resilient, distributed platform. API Gateway