AI Baby Monitor – local Video-LLM that beeps when safety rules break
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…
In plain words
AI Baby Monitor is a local video monitoring system that watches a live video stream against customizable safety rules defined in YAML. When the system detects a rule violation, it emits an audio alert to notify parents to check on their child. Built on Redis, vLLM, and Streamlit, it runs a vision language model locally to analyze video frames. The tool is designed for parents of infants and toddlers as an additional layer of supervision, not a replacement for active parental oversight. It alerts through sound only, keeping parents semi-attentive rather than glued to screens.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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 adult. Thus, just a beep sound and not phone notifications. How it works * *stream_to_redis.py* captures video stream frames → Redis streams * *run_watcher.py* pulls the latest N frames, injects them + the rules into a prompt and hits a local *vLLM* server running *Qwen 2.5 VL* * Model returns structured JSON (`should_alert`, `reasoning`, `awareness_level`) * If `should_alert=True` → `playsound` beep * Streamlit page displays both camera and LLM logs
Does the same job
all alternatives →
- AYAyeWatch – Your Personal AI Internet Monitor – To Monitor the Situation2025 · ayewatch.ai · ▲5
Monitor topics, sources, and pages with AI. Get instant alerts only when your rules match or something new happens , less noise, more signal. Most monitoring tools are glorified Google Alerts. They dump everything on you. “Here’s 200 mentions of your keyword today. Good luck.” The internet moves too fast. By the time you check Twitter, Reddit, HackerNews, industry blogs, career pages, research papers etc…, something important already happened three hours ago. Or three days ago. For example: Every morning: check 15 tabs. Every lunch: scan social feeds. Every evening: catch up on industry…
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
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…
AI · 27d ago · cactuscompute.com
Launched alongside, May 2025
the whole month →
- C9
Life & fun · 2025 · felixrieseberg.github.io
