Optimized alt text for accessibility & SEO

Author: madeline-underwood

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/1-overview.md

@@ -24,7 +24,7 @@ The Raspberry Pi 5's Arm Cortex-A76 processor can manage high-performance comput
 
 These characteristics make the Raspberry Pi 5 well suited for workloads like smart home assistants, where responsiveness, efficiency, and local processing are important. Running LLMs locally on Arm-based devices brings several practical benefits. Privacy is preserved, since conversations and routines never leave the device. With optimized inference, the system can offer responsiveness under 100 ms, even on resource-constrained hardware. It remains fully functional in offline scenarios, continuing to operate when internet access is unavailable. Developers also gain flexibility to customize models and automations. Additionally, software updates and an active ecosystem continue to improve performance over time.
 
-## Leverage the Arm ecosystem for Raspberry Pi edge AI
+## Leverage the Arm ecosystem for Raspberry Pi Edge AI
 
 For the stack in this setup, Raspberry Pi 5 benefits from the extensive developer ecosystem:
 
@@ -46,7 +46,7 @@ The table below shows inference performance for several quantized models running
 | gemma2:2b           | 4.1        | 23,758           |
 | deepseek-r1:7b      | 1.6        | 64,797           |
 
-### LLM benchmark insights on Raspberry Pi 5
+## LLM benchmark insights on Raspberry Pi 5
 
 - Qwen 0.5B and TinyLlama 1.1B deliver fast token generation and low average latency, making them suitable for real-time interactions such as voice-controlled smart home commands
 - DeepSeek-Coder 1.3B and Gemma 2B trade some speed for improved language understanding, which can be useful for complex tasks or context-aware prompts
@@ -56,7 +56,7 @@ The table below shows inference performance for several quantized models running
 
 This Learning Path focuses on the Raspberry Pi 5, but you can adapt the concepts and code to other Arm-powered devices.
 
-### Recommended platforms
+## Recommended platforms
 
 | Platform            | CPU                              | RAM            | GPIO support                   | Model size suitability      |
 | ------------------- | -------------------------------- | -------------- | ------------------------------ | --------------------------- |

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/2-software-dependencies.md

@@ -11,7 +11,7 @@ layout: learningpathall
 In this section, you’ll prepare your Raspberry Pi 5 by installing Python, required libraries, and Ollama, so you can run large language models (LLMs) locally.
 
 {{% notice Note %}}
-This guide assumes you have set up your Raspberry Pi with Raspberry Pi OS and network connectivity. For Raspberry Pi 5 setup help, see: [Raspberry Pi Getting Started](https://www.raspberrypi.com/documentation/)
+This Learning Path assumes you have set up your Raspberry Pi with Raspberry Pi OS and network connectivity. For Raspberry Pi 5 setup support, see [Raspberry Pi Getting Started](https://www.raspberrypi.com/documentation/).
 {{% /notice %}}
 
 ## Connect to your Raspberry Pi 5
@@ -46,7 +46,7 @@ sudo apt update && sudo apt upgrade -y
 sudo apt install -y python3 python3-pip python3-venv git curl build-essential gcc python3-lgpio
 ```
 
-### Configure a virtual environment
+## Configure a virtual environment
 
 Create and activate a Python virtual environment to isolate project dependencies:
 

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/3-test-gpio.md

@@ -6,13 +6,17 @@ weight: 4
 layout: learningpathall
 ---
 
+## Overview
+
 The next step is to test the GPIO functionality. In this section, you configure an LED light to simulate a smart home device.
 
 ## Verify GPIO setup on Raspberry Pi 5
 
-Gather your electronic components. Connect the anode (long leg) of an LED in series with a 220Ω resistor to GPIO 17 (physical pin 11). Connect the cathode (short leg) to a ground (GND) pin. See the image below for the full setup:
+Gather your electronic components. Connect the anode (long leg) of an LED in series with a 220Ω resistor to GPIO 17 (physical pin 11). Connect the cathode (short leg) to a ground (GND) pin. 
+
+See the image below for the full setup:
 
-![Raspberry Pi connected to a breadboard with a green LED and jumper wires](pin_layout.jpg "Raspberry Pi connected to a breadboard with a green LED and jumper wires")
+![Raspberry Pi connected to a breadboard with a green LED and jumper wires alt-text#center](pin_layout.jpg "Raspberry Pi connected to a breadboard with a green LED and jumper wires")
 
 Create a Python script named `testgpio.py`:
 

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/4-smart-home-assistant.md

@@ -35,8 +35,9 @@ In the previous section, you configured a LED on GPIO pin 17. The smart home ass
 The code uses gpiozero with lgpio backend for Raspberry Pi 5 compatibility. You can use compatible output devices such as LEDs, relays, or small loads connected to these GPIO pins to represent actual smart home devices. All pin assignments are optimized for the Raspberry Pi 5's GPIO layout.
 {{% /notice %}}
 
-![Raspberry Pi connected to breadboard with LEDs, buttons, and a sensor module](hardware.jpeg "Setup that includes a blue LED (mapped to Living Room Light on GPIO 17), a red LED, push button, and a sensor module. This setup illustrates a simulated smart home with controllable devices.")
+![[Raspberry Pi 5 connected to a breadboard with LEDs, push button, and sensor module alt-text#center](hardware.jpeg "Setup that includes a blue LED (mapped to Living Room Light on GPIO 17), a red LED, push button, and a sensor module.")
 
+This setup illustrates a simulated smart home with controllable devices.
 
 ## Run the Smart Home Assistant
 
@@ -57,7 +58,7 @@ python3 smart_home_assistant.py --no-api
 {{< /tab >}}
 {{< /tabpane >}}
 
-### Command options
+## Command options
 
 | Option            | Description                                                                                       | Example                                    |
 |------------------|---------------------------------------------------------------------------------------------------|--------------------------------------------|
@@ -67,20 +68,20 @@ python3 smart_home_assistant.py --no-api
 
 If everything is set up correctly, you should see the following output on running the default command:
 
-![Running in Default Mode](cmd.png "Running the code in default mode")
+![Terminal running smart_home_assistant.py showing default web API and CLI output alt-text#center](cmd.png "Running the code in default mode")
 
 ## Interact with your assistant
 
 Try asking the assistant to `turn on living room light`. If you've connected additional devices, come up with prompts to test the setup.
 
-### Web interface
+## Web interface
 
 Open your browser and navigate to `http://0.0.0.0:8000`, or as printed in the terminal output.
 
-![Web Interface Interaction](UI3.png "Interacting with the LLM through the web interface")
+![Web interface of the smart home assistant showing device control through LLM commands alt-text#center](UI3.png "Interacting with the LLM through the web interface")
 
 
-### Command line interface
+## Command line interface
 
 Type commands directly in the terminal.
 
@@ -92,7 +93,7 @@ I want to watch my favorite show
 its getting late, secure the house
 ```
 
-![DeepSeek-Coder Interaction](gemma2.png "Interacting with deepseek-coder:1.3b")
+![Terminal interaction with the smart home assistant showing LLM responses to user commands alt-text#center](gemma2.png "Interacting with deepseek-coder:1.3b")
 
 {{% notice Troubleshooting %}}
 If you're running into issues with the assistant, here are some things to check: