From Screen to streets: Real-World Gear For The Cyberpunk Lifestyle

Netrunners in this cyberpunk world need specific gear to navigate the old net effectively. Here are some essential items:

### How to Build a Small Form Factor AI Companion/Cyber Deck

**Components Needed:**

1. **Handheld Console: Lenovo Legion Go**

- This device serves as your primary interface and computing platform. It's compact, powerful, and equipped with gaming controls, making it ideal for a portable cyber deck.

2. **External GPU Accelerator: ADLINK Pocket AI**

- **NVIDIA RTX A500 GPU** with 4GB GDDR6

- **2048 NVIDIA CUDA Cores** / 64 NVIDIA Tensor Cores / 16 NVIDIA RT Cores

- **100 TOPS DENSE INT8 Inference Performance**

- **6.54 TFLOPS Peak FP32 Performance**

- **Thunderbolt™ Interface**: Ensures high-speed data transfer (up to 40Gb/s) and compatibility with USB Type-C connections.

- **NVIDIA CUDA X, RTX Software Enhancements**: Provides advanced software support for AI and visual computing applications.

Pocket_AI_-_User_Manual_202307 (1).pdf

Download PDF • 2.30MB

### Step-by-Step Build Guide:

1. **Setup the Lenovo Legion Go:**

- Unbox and charge the device.

- Install necessary software and drivers, particularly those needed for AI development and visual computing.

- Ensure the system is updated to the latest firmware and software versions.

2. **Connect the ADLINK Pocket AI:**

- Use the Thunderbolt™ interface to connect the Pocket AI to the Legion Go.

- Ensure the connection is secure and recognized by the system.

3. **Install GPU Drivers and Software:**

- Download and install the latest NVIDIA drivers for the RTX A500 GPU.

- Install any additional NVIDIA software required for CUDA and RTX functionalities.

4. **Configure the System for AI Development:**

- Set up your development environment, including tools such as TensorFlow, PyTorch, or other AI frameworks.

- Configure your IDE (e.g., Visual Studio Code, PyCharm) for AI development.

5. **Optimize for Portability:**

- Ensure all components are securely connected and optimized for mobility.

- Configure power settings to maximize battery life without compromising performance.

below are two optional choices that will greatly enhance the usability. The top picture is a large battery bank. Followed by A USB-C, intelligent hub.

6. **Test the Setup:**

- Run benchmark tests to ensure the GPU is functioning correctly and delivering expected performance.

- Test with a simple AI model to validate the entire setup.

### Tips for Cyberpunks:

- **Portability:** The Lenovo Legion Go combined with the ADLINK Pocket AI offers a balance of portability and performance. Always have a carrying case to protect your gear.

- **Power Management:** Keep an eye on battery levels, especially when running intensive AI tasks. Carry a portable power bank if necessary.

- **Software Updates:** Regularly check for updates to drivers and software to maintain optimal performance and security.

- **Expandability:** Consider additional peripherals like a foldable keyboard, portable monitor, or VR headset for an enhanced cyberpunk experience.

Smartwatch: LilyGO T-Watch 2020 V3

• A versatile smartwatch that can be integrated into your cyberpunk setup for added functionality and monitoring.

### Final Thoughts:

Building a small form factor AI companion/cyber deck is about merging high-performance hardware with a portable, user-friendly interface. The combination of Lenovo Legion Go and ADLINK Pocket AI provides a powerful platform for AI development, gaming, and more. Embrace the cyberpunk spirit by customizing and optimizing your setup for your unique needs.

OK, let’s get started with the software side of things.

Integrating a pocket AI with a Legion Go device running Bazzite OS is an exciting project that combines hardware hacking, software development, and system optimization. Here’s a detailed narrative on how to achieve this:

### Step 1: Understand the Hardware and Software Requirements

**Pocket AI Device:**

- Identify the specifications of your pocket AI device. Most pocket AI devices come with pre-built neural processing units (NPUs) or graphical processing units (GPUs) optimized for AI tasks.

- Ensure that the pocket AI device has compatible I/O ports (USB, UART, SPI, I2C, etc.) to interface with the Legion Go device.

**Legion Go Device:**

- Confirm that the Legion Go device has the necessary hardware capabilities to support the pocket AI device. This includes sufficient CPU power, memory, and I/O ports.

- Ensure that the Bazzite OS is installed and updated on the Legion Go device. Bazzite OS is a lightweight, customizable Linux distribution optimized for performance.

### Step 2: Prepare the Development Environment

1. **Update and Upgrade Bazzite OS:**

```bash

sudo apt update

sudo apt upgrade

```

2. **Install Necessary Libraries and Tools:**

- Install Python and relevant libraries:

```bash

sudo apt install python3 python3-pip

pip3 install numpy scipy scikit-learn tensorflow pytorch

```

- Install other development tools:

```bash

sudo apt install git build-essential cmake

```

### Step 3: Interface the Pocket AI with Legion Go

1. **Connect the Pocket AI Device:**

- Physically connect the pocket AI device to the Legion Go via an appropriate interface (USB, UART, etc.). Ensure the connection is secure and recognized by the system.

2. **Verify Connection:**

- Check if the device is recognized:

```bash

lsusb

dmesg | grep tty

```

- If the device is connected via USB, it should show up in the `lsusb` output. For UART connections, `dmesg` will display the corresponding tty device.

### Step 4: Install Drivers and SDKs

1. **Download and Install Drivers:**

- Visit the manufacturer’s website to download the latest drivers for your pocket AI device. Follow the instructions to install these drivers on Bazzite OS.

- Example for USB drivers:

```bash

sudo dpkg -i pocket-ai-driver.deb

```

2. **Install SDKs:**

- Download and install the Software Development Kit (SDK) for your pocket AI device. The SDK will provide necessary libraries and tools for development.

- Example:

```bash

git clone https://github.com/pocket-ai/sdk.git

cd sdk

./install.sh

```

### Step 5: Configure the Development Environment

1. **Set Up Environment Variables:**

- Configure environment variables to include paths to the SDK and libraries:

```bash

export POCKET_AI_HOME=/path/to/pocket-ai/sdk

export LD_LIBRARY_PATH=$POCKET_AI_HOME/lib:$LD_LIBRARY_PATH

export PATH=$POCKET_AI_HOME/bin:$PATH

```

2. **Verify Installation:**

- Run a sample code to verify the installation and functionality of the pocket AI device:

```bash

cd $POCKET_AI_HOME/samples

python3 sample_code.py

```

### Step 6: Develop and Deploy AI Applications

1. **Develop AI Models:**

- Use frameworks like TensorFlow or PyTorch to develop your AI models. Ensure that these models are optimized for the hardware capabilities of the pocket AI device.

- Example model development script:

```python

import tensorflow as tf

from tensorflow.keras import layers, models

model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Flatten())

model.add(layers.Dense(64, activation='relu'))

model.add(layers.Dense(10, activation='softmax'))

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Load and preprocess data

# train_images, train_labels, test_images, test_labels = ...

model.fit(train_images, train_labels, epochs=5)

model.evaluate(test_images, test_labels)

```

2. **Deploy AI Models:**

- Deploy the trained models to the pocket AI device. Ensure that the models are in a format compatible with the device.

- Example deployment script:

```python

import pocket_ai_sdk as pai

model_path = "path/to/trained_model.h5"

ai_device = pai.AIDevice()

ai_device.load_model(model_path)

ai_device.run_inference(input_data)

```

### Step 7: Optimize Performance and Monitor

1. **Optimize AI Models:**

- Use model optimization techniques such as quantization, pruning, and hardware-specific optimizations to enhance the performance of your AI models on the pocket AI device.

2. **Monitor and Debug:**

- Use monitoring tools and logs to track the performance and identify any issues.

- Example monitoring script:

```python

import pocket_ai_sdk as pai

ai_device = pai.AIDevice()

performance_metrics = ai_device.get_performance_metrics()

print(performance_metrics)

```

By following these detailed steps, you can successfully integrate a pocket AI device with a Legion Go running Bazzite OS, enabling advanced AI applications on a portable platform.