Hailo

Hailo AI

Hailo AI

Hailo AI

Hailo AI

Hailo AI

Hailo AI

Hailo AI

Hailo AI

Hailo AI

Hailo AI

I built the smart speaker we always wanted

I wanted to see if Claude can handle Vibe Hardware Engineering to help me make a smart speaker. Turns out, it can! I call it boxBot. It helped select the hardware set, raspberry pi, Hailo , respeaker mic, pi camera, waveshare screen and speakers. Helped me calculate thermal loads and dissipation rates for a passive cooling setup. I made the box by hand out of walnut. The agent inside is custom as well. You could probably throw openclaw on it and call it a day but I wanted to craft something that was tightly coupled with the hardware more secured considering it’s sitting in my living room with a camera and mic. The agent is highly skills driven with only a small set of tools, everything else goes through Python scripts and a custom made boxBot sdk the agent can use to control the box and the display. The display system uses a widget framework so the agent can easily read what’s displayed without a screenshot and can effectively manipulate what’s on the screen. The agent uses json to specify how the widgets should be arranged on the screen and what data should flow into them. When building a smart speaker, there’s a lot of nuance to human conversation that voice agents really struggle with, like background noise, side conversations, barge-in, etc. I was able to simplify the logic a ton by making it agent driven, the agent can control when to mute the mic to ignore background chatter, it decides what order to work vs talk, it can choose what channel to respond in; voice or WhatsApp. Instead of complex rules, agent driven hardware plus skills can provide a much richer experience, now that boxBot manages the family calendar my wife wants a text whenever I put something on it, boxBot updated the calendar skill with that request so now when I add something, it sends her a message. Just one line in a .md file and you get the desired behavior. It’s incredibly flexible and simple. I could nerd out on the details about the memory system, struggles with woodworking, and security details but I’ll save that for the comments if people want to chat. It’s open sourced if you want to inspect. Still a work in progress but after a few months it is finally feeling like a useful assistant to the family day-to-day. Www.github.com/dv-hart/boxbot submitted by /u/FunScore645 [link] [comments]

[D] Why I abandoned YOLO for safety critical plant/fungi identification. Closed-set classification is a silent failure mode

I’ve been building an open-sourced handheld device for field identification of edible and toxic plants wild plants, and fungi, running entirely on device. Early on I trained specialist YOLO models on iNaturalist research grade data and hit 94-96% accuracy across my target species. Felt great, until I discovered a problem I don’t see discussed enough on this sub. YOLO’s closed set architecture has no concept of “I don’t know.” Feed it an out of distribution image and it will confidently classify it as one of its classes at near 100% confidence. In most CV cases this can be annoyance. In foraging, it’s potentially lethal. I tried confidence threshold fine-tuning at first, doesn’t work. The confidence scores on OOD inputs are indistinguishable from in-distribution predictions because the softmax output is normalized across a closed-set. There’s no probability mass allocated to “none of the above”. My solution was to move away from YOLO entirely (the use case is single shot image classification, not a video stream) and build a layered OOD detection pipeline. - EfficientNet B2 specialist models: Mycologist, berries, and high value foraging instead of one monolithic detector. - MobileNetV3 small domain router that directs inputs to appropriate specialist model or rejects it before classification. - Energy scoring on raw logits pre softmax to detect OOD inputs. Energy scores separate in-distribution from OOD far more cleanly than softmax confidence. - Ensemble disagreement across the three specialists as a secondary OOD signal. - K+1 “none the above” class retrained into each specialist model. The whole pipeline needs to run within the Hailo 8L’s 13 TOPS compute budget on a battery powered handheld. All architecture choices are constrained by real inference latency, not just accuracy on desktop. Curious if others have run into this closed-set confidence problem in safety-critical applications and what approaches you’ve taken? The energy scoring method (from the “Energy-based Out-of-Distribution Detection” paper by Liu et al.) has been the single biggest improvement over native confidence thresholding. submitted by /u/Adebrantes [link] [comments]