Designing an automated handoff between engineering and manufacturing In hardware design, the moment a PCB layout is declared "done" is rarely the end of engineering. It's the beginning of translation. What follows is not soldering irons or pick-and-place machines, it's data. Files. Formats. Assumptions.
When designing mission-critical edge devices, engineers often face a familiar trade-off. On one side is Linux, powerful, flexible, rich with ecosystems for networking, AI, and analytics. On the other is the deterministic world of bare-metal or RTOS-based microcontrollers, trusted for precise timing and reliability. In a recent edge AI system,
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. But audio from edge devices is messy,
The convergence of Artificial Intelligence and the Internet of Things, AIoT, represents one of the more significant shifts happening in connected devices right now. Unlike traditional IoT systems that simply collect and transmit data, AIoT devices think, learn, and make decisions at the edge, turning raw sensor data into intelligent
Modern backend systems rarely fail because of a single line of bad code. More often, instability emerges when pressure moves through the system without clear boundaries. A sudden API spike increases retries, retries consume worker capacity, workers enqueue faster than downstream services can process, and queues quietly accumulate risk. Latency
Modern cloud systems are dangerously good at hiding their limits. Auto-scaling dashboards stay green. Serverless promises "infinite scale." Latency looks fine in staging. Then one day, a feature gets shared, a reel goes viral, a push notification lands at the wrong time, and that's usually when
USB-C has become the default connector for modern electronics, but in embedded systems, it's rarely "just a connector." It's a power interface, a data interface, a mechanical stress point, an ESD entry path, and often the first thing users interact with, long before firmware
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'
Every social app eventually hits what we call the Feed Scaling Wall. It usually shows up the moment your first power user follows their 500th account, and the once-snappy SELECT * query that powered their feed starts taking 200ms. Then 500ms. Then it times out. To get ahead of this at
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: the pet should never be aware of