
Control robot by Brain with RaspberryPI
eeg PIEEG
What it does
This project is the result of several years of work on the development of BCI. We believe that the easiest way to get started with biosignals is to use a shield Reading EEG signals as fully and clearly as possible https://github.com/Ildaron/EEGwithRaspberryPI
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BioLAb in home for Brain nd Body 2025 · ▲8PiEEG kit: Computer and Brain-computer interface all in one
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Hi HN, I recently shared this on r/BCI and wanted to see what the engineering community here thinks. A while back, I got frustrated with the state of accessible BCI hardware. Research gear was wildly unaffordable. So, I spent a ton of time designing a custom board, software and firmware to bridge that gap. I call it the Cerelog ESP-EEG. It is open-source (Firmware + Schematics), and I designed it specifically to fix the signal integrity issues found in most DIY hardware. I believe in sharing the work. You can find the Schematics, Firmware, and Software setup on the GitHub repo: GITHUB…
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Hi HN! I built AI Baby Monitor – a tiny stack (Redis + vLLM + Streamlit) that watches a video stream and a YAML list of safety rules. If the model spots a rule being broken it plays beep sound, so you can quickly glance over and check on your baby. Why? When we bought a crib for our daughter, the first thing she tried was climbing over the rail :/ I got a bit paranoid about constantly watching her over, so I thought of a helper that can *actively* watch the baby, while parents could stay *semi-actively* alert. It’s meant to be an additional set of eyes, and *not* a replacement for the…
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Source: Product Hunt launch ↗
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