Technical Insights
Deep dives into embedded systems, edge AI, architectural decisions, and lessons from shipping real hardware products.
Build the first prototype on a dev board, move to a custom carrier board that reuses pre-certified radio modules when one of five triggers applies, and leave chip-down production boards until volume pays for their own radio certification.
Twelve written answers, from the user scenario to who owns the code, let an embedded engineer quote and test your device instead of guessing, and each blank you leave becomes paid rework.
Getting a works-like smart device prototype made costs $275–$660 in parts and fees plus $1,400–$3,500 per engineer-week, so engineer-weeks set the total and certification waits for production.
A first works-like hardware prototype takes about four to five months and production takes longer, so a founder's first 1–2 weeks are best spent testing the one part most likely to fail.
Hardware accelerators and seed investors ask first for a rough works-like prototype that proves the core function, crowdfunding also needs photos of a real unit, and an AI feature counts as proven only when it runs on the target hardware.
Jev fits ESP32 products because it returns a typed choice from a fixed list instead of free text, which matches firmware that accepts only bounded commands.
The ESP32-S3 fits sensor models under a watt, the Hailo-8L runs real-time camera detection at about 1.5 W, and Jetson Orin Nano runs GPU model at 7–25 W.
Laya is the open-weight decision model. This article checks whether a Raspberry Pi can host it, works through what the AI HAT+ and AI HAT+ 2 do and do not accelerate and when to actually not host at all.
You've probably heard "real-time voice AI" thrown around a lot lately. Agents that answer your phone, book appointments, handle support calls — they're everywhere. And a huge chunk of them are quietly running on LiveKit under the hood. This post tears it apart, layer by layer, so you actually understand what's happening when someone calls your AI agent.
NVIDIA's NemoClaw doesn't replace OpenClaw — it wraps it inside an isolated Linux sandbox enforced by Landlock, seccomp, and network namespaces, with declarative YAML policies governing every outbound byte. If you've been waiting for autonomous AI agents to be usable in production without handing them the keys to your entire filesystem, this is that moment.
Data labeling is one of the biggest hidden bottlenecks in ML projects. In this post, I walk through how I built DataFlow — a local, AI-powered annotation platform that takes a zip of images, accepts your label classes, and uses Meta's SAM 3 to automatically generate pixel-perfect segmentation masks across your entire dataset. No cloud, no manual polygon drawing, and no labeling team required. The result is a clean COCO-format dataset ready for any training pipeline — in an afternoon.
A quick guide on reading a CNN. i.e. calculate the input and output sizes of each layers.
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