Frigate + Local AI Stack
Experiments with local-first surveillance infrastructure, MQTT events, and lightweight AI workflows.
Frigate + Local AI Stack
Related projects: Home Automation, Cameras & Local Monitoring and MQTT Learning & Packaging Automation Concepts
This project started as a basic self-hosted camera setup and gradually turned into a much larger infrastructure sandbox.
The overall goal is keeping everything local-first and self-hosted while experimenting with:
- event-driven automation
- lightweight AI workflows
- WebRTC streaming
- MQTT messaging
- hardware acceleration
- browser-based camera systems
- remote access
- Linux infrastructure
The stack changes constantly depending on what is being tested.
Current experiments involve:
- Frigate
- go2rtc
- Docker
- VAAPI acceleration
- MQTT notifications
- browser-based camera dashboards
- remote networking through Tailscale
One thing that became obvious very quickly is how much browser behavior impacts real-time video systems.
A lot of debugging time has gone into:
- stream stability
- buffering behavior
- WebRTC negotiation
- hardware decode paths
- browser compatibility
- low-latency streaming
There is also ongoing experimentation around event systems and machine communication.
MQTT has become especially useful for tying together:
- motion events
- notifications
- automation triggers
- lightweight device communication
- status reporting
The overall environment continues evolving into a general local AI and infrastructure experimentation platform.