Merge pull request #73 from dtaivpp/main

Adding modules pages and sublinks

Author: bowenjw

docs/docs/blade/guides/gpio.mdx

@@ -39,7 +39,8 @@ View an interactive guide to the full Raspberry Pi GPIO pinout on [Pinout.xyz](h
 <Tabs groupId='pinout'>
     <TabItem value="Expansion" label="Expansion Module Port" default>
         The Expansion Module Port is pin 1 through pin 10 of the Raspberry Pi GPIO pinout.
-        This makes the Expansion Module Port compatible with many existing products like Real Time Clock Modules or I2C Devices.
+        This makes the Expansion Module Port compatible with many existing products like [Real Time Clock Modules or I2C Devices](/modules).
+        Ensure you reference the pinout on your specific Compute Blade and the module's datasheet for correct wiring.
         ![Expansion Module Port](/img/hardware/expansion-module-port.svg)
     </TabItem>
     <TabItem value="uart" label="UART" default>

docs/docs/modules/ai.md

@@ -0,0 +1,84 @@
+---
+sidebar_position: 2
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# AI Module
+
+The AI Module is currently in development and will provide enhanced artificial intelligence capabilities for edge computing applications on the Compute Blade platform.
+
+![AI module](/img/module/ai/ai-module.webp)
+
+## Current Status
+
+The AI Module is actively being developed to bring machine learning and artificial intelligence capabilities directly to your Compute Blade setup. This module will enable:
+
+- **Edge AI processing** without cloud dependencies
+- **Real-time inference** capabilities
+- **Integration** with popular ML frameworks
+- **Optimized performance** for Raspberry Pi hardware
+
+## Planned Features
+
+<Tabs groupId="ai-capabilities">
+  <TabItem value="inference" label="Inference Engine" default>
+    **On-device Model Inference**
+    - Support for TensorFlow Lite models
+    - ONNX runtime compatibility
+    - PyTorch mobile integration
+    - Quantized model support for faster processing
+  </TabItem>
+  <TabItem value="frameworks" label="ML Frameworks">
+    **Supported Frameworks**
+    - TensorFlow/TensorFlow Lite
+    - PyTorch
+    - OpenCV for computer vision
+    - scikit-learn for classical ML
+    - Custom model deployment tools
+  </TabItem>
+  <TabItem value="hardware" label="Hardware Acceleration">
+    **Performance Optimization**
+    - GPU acceleration where available
+    - Multi-core CPU utilization
+    - Memory-efficient model loading
+    - Batch processing capabilities
+  </TabItem>
+</Tabs>
+
+## Use Cases
+
+### Computer Vision
+- **Object detection** and classification
+- **Facial recognition** systems
+- **Quality control** in manufacturing
+- **Security monitoring** applications
+
+### Natural Language Processing
+- **Text classification** and sentiment analysis
+- **Language translation** for IoT devices
+- **Voice command** processing
+- **Chatbot** deployment at the edge
+
+### Predictive Analytics
+- **Sensor data** analysis
+- **Predictive maintenance** for industrial equipment
+- **Anomaly detection** in real-time
+- **Time series forecasting**
+
+## Development Roadmap
+
+- **Phase 1**: Core inference engine development
+- **Phase 2**: Framework integration and testing
+- **Phase 3**: Hardware optimization and acceleration
+- **Phase 4**: Documentation and example projects
+- **Phase 5**: Community beta testing
+
+## Stay Updated
+
+Check back for updates on the AI Module development progress and availability. Follow our [GitHub repository](https://github.com/uptime-lab) for the latest developments and early access opportunities.
+
+:::info Coming Soon
+The AI Module is expected to be available in Q3 2025. Early access will be provided to beta testers and community contributors.
+:::

docs/docs/modules/ai.mdx

@@ -0,0 +1,38 @@
+---
+sidebar_position: 1
+---
+
+# Modules Overview
+
+The Compute Blade ecosystem supports various modules that can enhance the functionality of your Raspberry Pi projects. These modules are designed to work seamlessly with the Compute Blade hardware and provide additional capabilities for different use cases.
+
+## Available Modules
+
+### Real Time Clock (RTC)
+
+Real Time Clock modules help maintain accurate time tracking even when your Compute Blade is powered off. These modules use I2C communication and are essential for applications that require precise time synchronization.
+
+[Learn more about RTC modules →](modules/rtc)
+
+### AI Module
+
+The AI Module is currently in development and will provide enhanced artificial intelligence capabilities for edge computing applications on the Compute Blade platform.
+
+[Learn more about the AI Module →](modules/ai)
+
+### Security Modules
+
+For applications requiring additional security features, various security modules can be integrated with the Compute Blade. These modules provide hardware-based security and encryption capabilities.
+
+[Learn more about security modules →](modules/zimkey4)
+
+## Getting Started
+
+When connecting any module to your Compute Blade, be sure to:
+
+1. Consult the pinout guide on your Compute Blade
+2. Reference the module's datasheet for correct wiring
+3. Follow the specific setup instructions for each module
+4. Test the module functionality before deploying
+
+Each module page contains detailed information about compatibility, installation, and configuration specific to that module type.

docs/docs/modules/index.mdx

@@ -0,0 +1,126 @@
+---
+sidebar_position: 3
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Real Time Clock
+
+There are several Real Time Clock (RTC) modules available for the Raspberry Pi, which can be used to keep track of the current time even when the Pi is powered off. These modules typically use I2C communication and can be easily integrated into your projects.
+
+Be sure when connecting a RTC module to use the pinout guide on your compute blade along with the pinout from the RTC modules data sheet to ensure correct wiring.
+
+## Available RTC Modules
+
+### Popular Options
+
+<Tabs groupId="rtc-modules">
+  <TabItem value="adafruit" label="Adafruit PiRTC" default>
+    The [Adafruit PiRTC](https://www.adafruit.com/product/3386) is a reliable and well-documented RTC module.
+    
+    **Features:**
+    - Built-in battery backup
+    - Easy I2C interface
+    - Compatible with all Raspberry Pi models
+    - Excellent documentation and support
+  </TabItem>
+  <TabItem value="ds3231" label="DS3231 Module">
+    The [DS3231 Real Time Clock Module](https://www.pishop.us/product/ds3231-real-time-clock-module-for-raspberry-pi/) from PiShop.us offers high accuracy.
+    
+    **Features:**
+    - Temperature-compensated crystal oscillator
+    - ±2ppm accuracy from 0°C to +40°C
+    - Built-in 32.768kHz oscillator
+    - Battery-backed SRAM
+  </TabItem>
+  <TabItem value="ds1307" label="DS1307 Module">
+    The [DS1307 RTC for Raspberry Pi](https://wiki.seeedstudio.com/Pi_RTC-DS1307/) from Seeed Studio is a cost-effective option.
+    
+    **Features:**
+    - Low-cost solution
+    - 56-byte battery-backed RAM
+    - Programmable square-wave output
+    - Automatic leap year compensation
+  </TabItem>
+</Tabs>
+
+## Installation
+
+### Hardware Setup
+
+1. **Power down** your Compute Blade before connecting any modules
+2. **Connect the RTC module** to your Compute Blade using I2C pins:
+   - VCC → 3.3V or 5V (check your module's requirements)
+   - GND → Ground
+   - SDA → GPIO2 (Pin 3)
+   - SCL → GPIO3 (Pin 5)
+3. **Insert the backup battery** (usually CR2032) into the RTC module
+
+### Software Configuration
+
+<Tabs groupId="installation-method">
+  <TabItem value="auto" label="Automatic Setup" default>
+    ```bash
+    # Enable I2C interface
+    sudo raspi-config
+    # Navigate to: Interfacing Options > I2C > Enable
+    
+    # Reboot to apply changes
+    sudo reboot
+    
+    # Install I2C tools
+    sudo apt update
+    sudo apt install i2c-tools
+    
+    # Detect the RTC module
+    sudo i2cdetect -y 1
+    ```
+  </TabItem>
+  <TabItem value="manual" label="Manual Configuration">
+    ```bash
+    # Edit the boot config
+    sudo nano /boot/config.txt
+    
+    # Add the following line (adjust for your RTC chip):
+    dtoverlay=i2c-rtc,ds3231
+    # or for DS1307: dtoverlay=i2c-rtc,ds1307
+    
+    # Save and reboot
+    sudo reboot
+    
+    # Remove fake hardware clock
+    sudo apt-get -y remove fake-hwclock
+    sudo update-rc.d -f fake-hwclock remove
+    ```
+  </TabItem>
+</Tabs>
+
+### Testing Your RTC
+
+```bash
+# Check if RTC is detected
+sudo hwclock -D -r
+
+# Set system time to RTC
+sudo hwclock -w
+
+# Read time from RTC
+sudo hwclock -r
+```
+
+## Benefits
+
+- **Persistent timekeeping**: Maintains accurate time when the system is powered off
+- **Low power consumption**: Uses minimal power from backup battery
+- **Easy integration**: Standard I2C interface works with existing projects
+- **Essential for logging**: Critical for applications requiring accurate timestamps
+- **Network independence**: No need for NTP when network is unavailable
+
+## Troubleshooting
+
+### Common Issues
+
+- **Module not detected**: Check wiring connections and I2C enable status
+- **Time drift**: Ensure backup battery is properly installed and charged
+- **Boot issues**: Verify correct device tree overlay for your RTC chip