1 Install CLI and Desktop apps#

# OpenCode 2 CLI and destop
brew install anomalyco/tap/opencode-v2 && brew install --cask opencode-desktop

# Check MCP's
opencode mcp list

• Command palette shortcuts, press Cmd + Shift + P (or Cmd + P) and search for

• Press ⌘⇧A (Command + Shift + A): Toggle Auto-Approve to bypass terminal execution prompts for that active session

• The main shared configuration is: ~/.config/opencode/opencode.json

• CLI-specific configuration is: ~/.config/opencode/cli.json

2 Guide: Configuring a 64k Context in Ollama and OpenCode v2#

For local coding agents, a larger context window can accommodate instructions, tool results, and conversation history. Ollama recommends at least 64k tokens for agent and coding tasks, but this uses more memory; confirm that the model and hardware can handle it. Ollama’s default allocated context varies with available VRAM (currently 4k below 24 GiB, 32k at 24–48 GiB, and 256k at 48 GiB or more).

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.

Hermes#

1 Projects#

Hermes Projects are named workspaces that can include folders or repositories. Project data is per profile. In this desktop version, use the built-in terminal for project and folder commands (Ctrl + ` opens it). Quote paths containing spaces.

# Create a project
hermes project create "Project Name" --primary "/absolute/path/to/folder" --use

# Use a project
hermes project use dockdash

# Add a folder
hermes project add-folder PROJECT "/absolute/path/to/folder"

# Add a PRIMARY folder
hermes project add-folder PROJECT "/absolute/path/to/folder" --primary

# Remove a folder
hermes project remove-folder PROJECT "/absolute/path/to/folder"

# List projects
hermes project list

# Show a project's configured folders
hermes project show PROJECT

# Use a project in CLI
hermes project use dockdash

2 Project Management#

Create projects and add folders

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.

Installing Hermes Agent on Mac Studio#

1. Install Hermes Agent#

Official documentation:

https://hermes-agent.nousresearch.com

GitHub repository:

https://github.com/NousResearch/hermes-agent

1.1 Run the installer#

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

The installer handles the Hermes repository, Python environment, and required dependencies.

Expected installation paths:

~/.hermes/
~/.hermes/config.yaml
~/.hermes/.env
~/.hermes/hermes-agent/
~/.local/bin/hermes

1.2 Verify installation#

hermes --version

The MacBook Air installation used Hermes Agent v0.21.1.

The Mac Studio should use the current available release.

2. Hermes Setup Wizard#

Run:

hermes setup

Recommended selections are based on the previous successful installation.