Add dev container, fix lints

docs/Developer-Guide.md

@@ -4,24 +4,29 @@
 
 The project is organized as a monorepo with these main packages:
 - `libs/core/` - Base package with telemetry support
-- `libs/pylume/` - Python bindings for Lume
-- `libs/computer/` - Core computer interaction library
+- `libs/computer/` - Computer-use interface (CUI) library
 - `libs/agent/` - AI agent library with multi-provider support
-- `libs/som/` - Computer vision and NLP processing library (formerly omniparser)
-- `libs/computer-server/` - Server implementation for computer control
-- `libs/lume/` - Swift implementation for enhanced macOS integration
+- `libs/som/` - Set-of-Mark parser
+- `libs/computer-server/` - Server component for VM
+- `libs/lume/` - Lume CLI
+- `libs/pylume/` - Python bindings for Lume
 
 Each package has its own virtual environment and dependencies, managed through PDM.
 
 ### Local Development Setup
 
-1. Clone the repository:
+1. Install Lume CLI:
+```bash
+/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"
+```
+
+2. Clone the repository:
 ```bash
 git clone https://github.com/trycua/cua.git
 cd cua
 ```
 
-2. Create a `.env.local` file in the root directory with your API keys:
+3. Create a `.env.local` file in the root directory with your API keys:
 ```bash
 # Required for Anthropic provider
 ANTHROPIC_API_KEY=your_anthropic_key_here
@@ -30,7 +35,7 @@ ANTHROPIC_API_KEY=your_anthropic_key_here
 OPENAI_API_KEY=your_openai_key_here
 ```
 
-3. Run the build script to set up all packages:
+4. Run the build script to set up all packages:
 ```bash
 ./scripts/build.sh
 ```
@@ -41,9 +46,9 @@ This will:
 - Set up the correct Python path
 - Install development tools
 
-4. Open the workspace in VSCode or Cursor:
+5. Open the workspace in VSCode or Cursor:
 ```bash
-# Using VSCode or Cursor
+# For Cua Python development
 code .vscode/py.code-workspace
 
 # For Lume (Swift) development
@@ -56,9 +61,55 @@ Using the workspace file is strongly recommended as it:
 - Enables debugging configurations
 - Maintains consistent settings across packages
 
+### Docker Development Environment
+
+As an alternative to running directly on your host machine, you can use Docker for development. This approach has several advantages:
+
+- Ensures consistent development environment across different machines
+- Isolates dependencies from your host system
+- Works well for cross-platform development
+- Avoids conflicts with existing Python installations
+
+#### Prerequisites
+
+- Docker installed on your machine
+- Lume server running on your host (port 3000): `lume serve`
+
+#### Setup and Usage
+
+1. Build the development Docker image:
+```bash
+./scripts/run-docker-dev.sh build
+```
+
+2. Run an example in the container:
+```bash
+./scripts/run-docker-dev.sh run computer_examples.py
+```
+
+3. Get an interactive shell in the container:
+```bash
+./scripts/run-docker-dev.sh run --interactive
+```
+
+4. Stop any running containers:
+```bash
+./scripts/run-docker-dev.sh stop
+```
+
+#### How it Works
+
+The Docker development environment:
+- Installs all required Python dependencies in the container
+- Mounts your source code from the host at runtime
+- Automatically configures the connection to use host.docker.internal:3000 for accessing the Lume server on your host machine
+- Preserves your code changes without requiring rebuilds (source code is mounted as a volume)
+
+> **Note**: The Docker container doesn't include the macOS-specific Lume executable. Instead, it connects to the Lume server running on your host machine via host.docker.internal:3000. Make sure to start the Lume server on your host before running examples in the container.
+
 ### Cleanup and Reset
 
-If you need to clean up the environment and start fresh:
+If you need to clean up the environment (non-docker) and start fresh:
 
 ```bash
 ./scripts/cleanup.sh