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Grablo Vision
No-code AI automation for the cameras you already own
Grablo Vision turns cameras you already own into no-code AI automations. Add person, face, license-plate, or fire detection, then build rules: when something is detected, run actions in sequence like alerts, text-to-speech, logging with snapshots, or Zigbee/relay control. Works with RTSP, ONVIF, and USB cameras. It all runs locally on your own hardware (Raspberry Pi, PC, Docker, or a Home Assistant add-on) and keeps working even if your internet drops. Free for personal, non-commercial use.
Hey Product Hunt,
I'm the developer of Grablo Vision. It turns cameras you already own into no-code AI automations: point detection at a camera, then build rules like "when this is detected, do that."
Why I built it: adding smart alerts to cameras today usually means either a cloud subscription that watches your footage, or wiring up Frigate + Home Assistant + YAML. I wanted something in between. Local-first like the DIY route, but simple enough that you don't have to be a developer to set it up.
What it does:
- Detection: person/object, face (known vs unknown), license plates, and fire
- Cameras: works with your existing RTSP, ONVIF, or USB cameras
- Rule builder: pick a trigger, add conditions, then chain actions in sequence
- Conditions: a schedule, a "sustained for X seconds" filter, or an "ask an LLM" check in plain language
- Actions: push/Telegram/email alerts, logging with snapshots, text-to-speech, audio/video playback, MQTT/HTTP, and Zigbee or relay control
- Scales: multiple cameras, multiple AI analyzers per camera, and multiple rules per analyzer
How it works:
- Configure everything in the web dashboard, then hit Start
- Your whole setup is pushed to your own controller (a Raspberry Pi, a PC, Docker, or a Home Assistant add-on)
- From then on, the AI and your rules run locally on your hardware, and keep working even if your internet drops
- Live viewing is a browser-to-device P2P connection, so your footage isn't proxied through our servers
A quick example:
- Get a photo alert only when an unknown face shows up at the door, while known faces are logged silently
It's free for personal, non-commercial use, and I'm actively building it. I'd genuinely love your feedback, and I'll be around all day to answer questions. What would you point it at first?
About Grablo Vision on Product Hunt
“No-code AI automation for the cameras you already own”
Grablo Vision was submitted on Product Hunt and earned 0 upvotes and 2 comments, placing #120 on the daily leaderboard. Grablo Vision turns cameras you already own into no-code AI automations. Add person, face, license-plate, or fire detection, then build rules: when something is detected, run actions in sequence like alerts, text-to-speech, logging with snapshots, or Zigbee/relay control. Works with RTSP, ONVIF, and USB cameras. It all runs locally on your own hardware (Raspberry Pi, PC, Docker, or a Home Assistant add-on) and keeps working even if your internet drops. Free for personal, non-commercial use.
On the analytics side, Grablo Vision competes within Artificial Intelligence, Home Automation and Video cameras — topics that collectively have 481.6k followers on Product Hunt. The dashboard above tracks how Grablo Vision performed against the three products that launched closest to it on the same day.
Who hunted Grablo Vision?
Grablo Vision was hunted by YoungMin Kim. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Grablo Vision including community comment highlights and product details, visit the product overview.
YoungMin Kim