Merge pull request #3167 from madeline-underwood/metaserver5

Further metadata description fields and enhancements

Author: pareenaverma

content/learning-paths/laptops-and-desktops/chrome-os-lxc/_index.md

@@ -1,6 +1,8 @@
 ---
 title: Install Ubuntu on ChromeOS Crostini as an LXC container
 
+description: Learn how to create and run Ubuntu containers on ChromeOS Crostini using LXC with file sharing and GUI application support on Arm-based Chromebooks.
+
 minutes_to_complete: 60
 
 who_is_this_for: This Learning Path is for software developers who want to install Ubuntu and other Linux distributions on their Arm-based Chromebook with ChromeOS file sharing and GUI support.

content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md

@@ -1,6 +1,8 @@
 ---
 title: Build Robot Simulation and Reinforcement Learning Workflows with Isaac Sim and Isaac Lab on DGX Spark
 
+description: Learn how to build and deploy high-fidelity robotic simulations and reinforcement learning pipelines using Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark with Grace-Blackwell architecture.
+
 minutes_to_complete: 90
 
 who_is_this_for: This is an advanced topic for robotics developers, simulation engineers, and AI researchers who want to run high-fidelity robotic simulations and reinforcement learning (RL) pipelines using NVIDIA Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark system powered by the Grace–Blackwell (GB10) architecture.

content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/_index.md

@@ -1,6 +1,8 @@
 ---
 title: Unlock quantized LLM performance on Arm-based NVIDIA DGX Spark
 
+description: Learn how to build and optimize quantized LLMs using llama.cpp on NVIDIA DGX Spark with Grace-Blackwell architecture, leveraging Armv9 SIMD acceleration.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for AI practitioners, performance engineers, and system architects who want to learn how to deploy and optimize quantized large language models (LLMs) on NVIDIA DGX Spark systems powered by the Grace-Blackwell (GB10) architecture.

content/learning-paths/laptops-and-desktops/dgx_spark_rag/_index.md

@@ -1,5 +1,8 @@
 ---
 title: Build a RAG pipeline on Arm-based NVIDIA DGX Spark
+
+description: Learn how to build a Retrieval-Augmented Generation (RAG) pipeline on NVIDIA DGX Spark combining Arm Grace CPU orchestration with Blackwell GPU-accelerated inference using llama.cpp.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an advanced topic for developers who want to build a Retrieval-Augmented Generation (RAG) pipeline on the NVIDIA DGX Spark platform. You'll learn how Arm-based Grace CPUs handle document retrieval and orchestration, while Blackwell GPUs speed up large language model inference using the open-source llama.cpp REST server. This is a great fit if you're interested in combining Arm CPU management with GPU-accelerated AI workloads.

content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/3_fasterwhisper.md

@@ -248,7 +248,7 @@ This demo shows the real-time transcription pipeline in action, running on an Ar
 
 Notice the clean terminal output and low latency, demonstrating how the pipeline is optimized for local, real-time voice recognition on resource-efficient hardware.
 
-![Real-time speech transcription demo with volume visualization alt-txt#center](fasterwhipser_demo1.gif "Real-time speech transcription with audio volume bar")
+![Animated terminal session demonstrating real-time speech-to-text transcription on DGX Spark. The terminal displays timestamped transcribed text as someone speaks into a USB microphone, with a volume visualization bar at the bottom showing live audio input levels.#center](fasterwhipser_demo1.gif "Real-time speech transcription with audio volume bar")
 
 The device runs audio capture and transcription in parallel. Use `threading.Thread` to collect audio without blocking, store audio frames in a `queue.Queue`, and in the main thread, poll for new data and run STT.
 

content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/6_chatbot_contextaware.md

@@ -304,7 +304,7 @@ Once both the STT and LLM services are live, you'll be able to speak naturally a
 
 ### Demo: Multi-turn voice chatbot with context memory on DGX Spark
 
-![Animated terminal session showing real-time speech-to-text transcription and AI responses in a multi-turn customer service conversation, with a volume bar at the bottom indicating live audio input levels from a microphone alt-txt#center](fasterwhipser_vllm_demo2.gif "Full function voice-to-AI with volume bar")
+![Animated terminal showing an offline voice assistant in action on DGX Spark. The session displays multi-turn customer service conversation with real-time speech transcription and AI responses. A volume bar at the bottom indicates live microphone input levels throughout the interaction.#center](fasterwhipser_vllm_demo2.gif "Full function voice-to-AI with volume bar")
 
 This demo showcases a fully offline voice assistant that combines real-time transcription (via faster-whisper) and intelligent response generation (via vLLM). Running on an Arm-based DGX Spark system, the assistant captures live audio, transcribes it, and generates context-aware replies using a local language model, all in a seamless loop.
 

content/learning-paths/laptops-and-desktops/dgx_spark_voicechatbot/_index.md

@@ -1,6 +1,8 @@
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 title: Build an offline voice chatbot with faster-whisper and vLLM on DGX Spark
 
+description: Learn how to build an offline voice assistant combining speech-to-text via faster-whisper and text generation via vLLM on Arm-based DGX Spark for privacy-focused deployments.
+
 minutes_to_complete: 60
 
 who_is_this_for: This is an advanced topic for developers and ML engineers who want to build private, offline voice assistant systems on Arm-based servers such as DGX Spark.

content/learning-paths/laptops-and-desktops/docker-models/_index.md

@@ -1,6 +1,7 @@
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 title: Run AI models with Docker Model Runner
 
+description: Learn how to run pre-trained AI models locally using Docker Model Runner and build containerized applications integrating large language models.
 
 minutes_to_complete: 45
 

content/learning-paths/laptops-and-desktops/electron/_index.md

@@ -1,6 +1,8 @@
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 title: Develop cross-platform desktop applications with Electron on Windows on Arm
 
+description: Learn how to develop and build cross-platform desktop applications using the Electron Framework on Windows on Arm devices.
+
 minutes_to_complete: 30
 
 who_is_this_for: This learning path is for developers who want to learn how to develop cross-platform desktop applications using the Electron Framework on Windows on Arm (WoA).

content/learning-paths/laptops-and-desktops/gh-arm-runners-win/_index.md

@@ -1,6 +1,8 @@
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 title: Automate Windows on Arm Builds with GitHub Arm-hosted Runners
 
+description: Learn how to automate Windows application builds on Arm architecture using GitHub Arm-hosted runners and GitHub Actions workflows.
+
 minutes_to_complete: 20
 
 who_is_this_for: This introductory tutorial is for software developers looking to automate Windows application builds on Arm architecture using GitHub Actions.