Frigate Installation Project Plan#

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UniFi Protect remains the primary NVR. Frigate will be an additional detection and classification layer. No installation or configuration should begin until the read-only validation phase is complete.

1. Project Objectives#

1.1 Functional Objectives#

The system will detect animals using Frigate motion detection, object detection, tracking, and zones.

It will distinguish coyotes from chickens, dogs, cats, deer, raccoons, foxes, and unknown animals. The initial tuning priority is avoiding missed coyote detections; false positives can be reduced afterward.

Local AI Camera Detection Documentation#

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This document describes the working local visual-classification implementation and the information required to recreate it. It is documentation, not a task list.

1. Purpose and Design Principles#

1.1 Purpose#

The system adds a second-stage visual classifier to the existing UniFi Protect and Home Assistant installation.

UniFi Protect remains responsible for recording video and detecting broad event categories such as animal, person, or vehicle. Home Assistant receives the Protect event, obtains an image, sends it to a local vision-capable Ollama model, evaluates the structured result, records the decision, and sends a notification only when the objective-specific policy accepts the result.

Local AI Coding Agent Setup#

This guide documents the steps taken to install and configure Claude Code to run 100% locally on the Mac Studio M4 (64GB) using Ollama, ensuring complete privacy for local folders and Obsidian vaults.

1. Prerequisites (Mac Studio)#

• Hardware: Mac Studio M4 (64GB Unified Memory).
• Environment: Node.js v25.8.0 installed.
• Local Brain: Ollama installed and running.

2. Install Claude Code (Native)#

Instead of using npm, we used the official native installer from Anthropic for better performance and signed binary security.

Ollama & Stable Diffusion AI Guide#

1 HP1GPU - Docker (Ollama Model Inventory)#

1.1 llama3.2:latest (2.0 GB)#

• Consuming VM: HP1Docker
• Services:
  • karakeep (Docker)
  • paperless (Docker)
  • [n8n](n8n.md) (Docker)

1.2 qwen3-vl:8b (6.1 GB)#

• Consuming VM: HP1Docker
  • Service: paperless-ai (Docker)

1.3 gemma3:12b (8.1 GB)#

• Consuming VM: HP1GPU
• Service:
  • open-webui (Docker)

1.4 llama3.2-vision:latest (7.8 GB)#

• Consuming VM: HP1Docker
• Service:
  • paperless Inactive (Commented out in compose)

1.5 qwen2.5vl:3b (3.2 GB)#

• Consuming VM: HP1Docker
• Service: paperless (Testing)
• Status: Inactive (Commented out in compose)

1.6 glm-ocr:latest (2.2 GB)#

1.7 gemma4::e4b (9.6 GB)#

1.8 nomic-embed-text:latest (274 MB)#

• Service: Internal Embeddings / RAG
• Status: Active (Implicit)

2 Ollama Installation & Network Setup#

2.1 Basic Installation#

# Open firewall port
ufw allow 11434/tcp

# Official Install Script
curl -fsSL [https://ollama.com/install.sh](https://ollama.com/install.sh) | sh

2.2 Enable Local Network Access#

To allow other devices to use the AI, you must change the bind address from 127.0.0.1 to 0.0.0.0.