Mobile testing is often seen as verifying layouts across different devices, but this task turned out to involve much more than UI validation. While testing the application, I also worked on implementing UXCam analytics events, which highlighted how closely testing and user behavior tracking are connected. One focused on making
Firmware
Replacing the operating system on a computer usually means having your hands on it: a cable, a USB stick, a recovery button, someone in the room. Now imagine that computer is inside a pet's feeding bowl, in a stranger's kitchen, behind their home router, three time
AI
The last post ended on an open problem: we could not make phone-photo galleries work well enough to matter. Adding more enrollment images made things worse, not better. This post is about what we changed and what finally moved the numbers. The setup Before getting into results, it helps to
Hardware
Most of the interesting hardware work at Hoomanely isn't greenfield. The connected feeding bowl already had a working carrier board: a compute module doing the thinking, a precision load-cell front end doing the weighing, connectivity, power, the usual. The board did its job. So when two new product
Most SEO conversations start and end with content - better keywords, longer articles, more backlinks. That's a real part of the picture, but it quietly assumes something worth actually verifying: that search engines, and increasingly AI answer engines, can find your pages, read them correctly, and trust what
Software
One message stopped testing cold: "my bowl is not getting connected to wifi." We have an FAQ that answers exactly this. The bot escalated anyway. No answer sent, just a ticket sitting in Slack for someone to pick up six hours later. Nothing was broken, technically. Retrieval ran,
AI
In our previous post we hit around 92% identification accuracy on detected frames. That result came with a catch we had quietly set aside: the gallery was built from bowl camera images, evaluated against bowl camera images. What happens when that changes? The Problem With the Current Setup A quick
When I first started working with Grafana, I treated it like a collection of graphs. I could see request counts, latency, error rates, and dozens of other metrics, but I didn't really understand what they were telling me. Every panel looked useful on its own, yet I found
Hardware
Filter placement, return-path proximity, connector-side filtering, trace length control, and shield-boundary discipline EMI problems are often discovered late. The schematic looks correct. The filter components are selected from the datasheet recommendations. The simulation shows attenuation. The PCB passes functional testing. Then the product enters certification testing, and suddenly a few
Technology, Insights and Ideas.
Product
How real-world fabrication variability shaped the design of Everbowl, and why engineering for consistency matters more than designing for perfection. Introduction Every CAD model starts out in a world of perfect geometry. Holes sit exactly where they're supposed to, surfaces are flat, and every dimension matches the drawing
Mobile testing is often seen as verifying layouts across different devices, but this task turned out to involve much more than UI validation. While testing the application, I also worked on implementing UXCam analytics events, which highlighted how closely testing and user behavior tracking are connected. One focused on making
Firmware
Replacing the operating system on a computer usually means having your hands on it: a cable, a USB stick, a recovery button, someone in the room. Now imagine that computer is inside a pet's feeding bowl, in a stranger's kitchen, behind their home router, three time
AI
The last post ended on an open problem: we could not make phone-photo galleries work well enough to matter. Adding more enrollment images made things worse, not better. This post is about what we changed and what finally moved the numbers. The setup Before getting into results, it helps to
Hardware
Most of the interesting hardware work at Hoomanely isn't greenfield. The connected feeding bowl already had a working carrier board: a compute module doing the thinking, a precision load-cell front end doing the weighing, connectivity, power, the usual. The board did its job. So when two new product
Most SEO conversations start and end with content - better keywords, longer articles, more backlinks. That's a real part of the picture, but it quietly assumes something worth actually verifying: that search engines, and increasingly AI answer engines, can find your pages, read them correctly, and trust what
Software
One message stopped testing cold: "my bowl is not getting connected to wifi." We have an FAQ that answers exactly this. The bot escalated anyway. No answer sent, just a ticket sitting in Slack for someone to pick up six hours later. Nothing was broken, technically. Retrieval ran,
AI
In our previous post we hit around 92% identification accuracy on detected frames. That result came with a catch we had quietly set aside: the gallery was built from bowl camera images, evaluated against bowl camera images. What happens when that changes? The Problem With the Current Setup A quick
When I first started working with Grafana, I treated it like a collection of graphs. I could see request counts, latency, error rates, and dozens of other metrics, but I didn't really understand what they were telling me. Every panel looked useful on its own, yet I found
Hardware
Filter placement, return-path proximity, connector-side filtering, trace length control, and shield-boundary discipline EMI problems are often discovered late. The schematic looks correct. The filter components are selected from the datasheet recommendations. The simulation shows attenuation. The PCB passes functional testing. Then the product enters certification testing, and suddenly a few