Pull sample applications into its own page in docs

docs/docs/building-applications/1-sample-applications.md

@@ -0,0 +1,29 @@
+# Sample Applications
+
+Explore these GitHub repositories to see examples of Groundlight-powered applications:
+
+## Groundlight Stream Processor
+
+Repository: [https://github.com/groundlight/stream](https://github.com/groundlight/stream)
+
+The Groundlight Stream Processor is an easy-to-use Docker container for analyzing RTSP streams or common USB-based cameras. You can run it with a single Docker command, such as:
+
+```bash
+docker run stream:local --help
+```
+
+## Arduino ESP32 Camera Sample App
+
+Repository: [https://github.com/groundlight/esp32cam](https://github.com/groundlight/esp32cam)
+
+This sample application allows you to build a working AI vision detector using an inexpensive WiFi camera. With a cost of under $10, you can create a powerful and affordable AI vision system.
+
+## Raspberry Pi
+
+Repository: [https://github.com/groundlight/raspberry-pi-door-lock](https://github.com/groundlight/raspberry-pi-door-lock)
+
+This sample application demonstrates how to set up a Raspberry Pi-based door lock system. The application monitors a door and sends a notification if the door is observed to be unlocked during non-standard business hours.
+
+## Industrial and Manufacturing Applications
+
+Groundlight can be used to [apply modern natural-language-based computer vision to industrial and manufacturing applications](/docs/building-applications/industrial).

docs/docs/building-applications/building-applications.md

@@ -5,45 +5,18 @@ Groundlight provides a powerful "computer vision powered by natural language" sy
 In this page, we'll introduce you to some sample applications built using Groundlight and provide links to more detailed guides on various topics.
 
 ## Sample Applications
-
-Explore these GitHub repositories to see examples of Groundlight-powered applications:
-
-### Groundlight Stream Processor
-
-Repository: [https://github.com/groundlight/stream](https://github.com/groundlight/stream)
-
-The Groundlight Stream Processor is an easy-to-use Docker container for analyzing RTSP streams or common USB-based cameras. You can run it with a single Docker command, such as:
-
-```bash
-docker run stream:local --help
-```
-
-### Arduino ESP32 Camera Sample App
-
-Repository: [https://github.com/groundlight/esp32cam](https://github.com/groundlight/esp32cam)
-
-This sample application allows you to build a working AI vision detector using an inexpensive WiFi camera. With a cost of under $10, you can create a powerful and affordable AI vision system.
-
-### Raspberry Pi
-
-Repository: [https://github.com/groundlight/raspberry-pi-door-lock](https://github.com/groundlight/raspberry-pi-door-lock)
-
-This sample application demonstrates how to set up a Raspberry Pi-based door lock system. The application monitors a door and sends a notification if the door is observed to be unlocked during non-standard business hours.
-
-### Industrial and Manufacturing Applications
-
-Groundlight can be used to [apply modern natural-language-based computer vision to industrial and manufacturing applications](/docs/building-applications/industrial).
+- **[Sample Applications](1-sample-applications.md)**: Find repositories with examples of applications built with Groundlight
 
 ## Further Reading
 
 For more in-depth guides on various aspects of building applications with Groundlight, check out the following pages:
-- **[Grabbing images](1-grabbing-images.md)**: Understand the intricacies of how to submit images from various input sources to Groundlight.
-- **[Working with detectors](2-working-with-detectors.md)**: Learn how to create, configure, and use detectors in your Groundlight-powered applications.
-- **[Confidence levels](3-managing-confidence.md)**: Master how to control the trade-off of latency against accuracy by configuring the desired confidence level for your detectors.
-- **[Handling server errors](4-handling-errors.md)**: Understand how to handle and troubleshoot HTTP errors that may occur while using Groundlight.
-- **[Asynchronous queries](5-async-queries.md)**: Groundlight makes it easy to submit asynchronous queries. Learn how to submit queries asynchronously and retrieve the results later.
-- **[Using Groundlight on the edge](6-edge.md)**: Discover how to deploy Groundlight in edge computing environments for improved performance and reduced latency.
-- **[Industrial applications](7-industrial.md)**: Learn how to apply modern natural-language-based computer vision to your industrial and manufacturing applications.
+- **[Grabbing images](2-grabbing-images.md)**: Understand the intricacies of how to submit images from various input sources to Groundlight.
+- **[Working with detectors](3-working-with-detectors.md)**: Learn how to create, configure, and use detectors in your Groundlight-powered applications.
+- **[Confidence levels](4-managing-confidence.md)**: Master how to control the trade-off of latency against accuracy by configuring the desired confidence level for your detectors.
+- **[Handling server errors](5-handling-errors.md)**: Understand how to handle and troubleshoot HTTP errors that may occur while using Groundlight.
+- **[Asynchronous queries](6-async-queries.md)**: Groundlight makes it easy to submit asynchronous queries. Learn how to submit queries asynchronously and retrieve the results later.
+- **[Using Groundlight on the edge](7-edge.md)**: Discover how to deploy Groundlight in edge computing environments for improved performance and reduced latency.
+- **[Industrial applications](8-industrial.md)**: Learn how to apply modern natural-language-based computer vision to your industrial and manufacturing applications.
 
 By exploring these resources and sample applications, you'll be well on your way to building powerful visual applications using Groundlight's computer vision and natural language capabilities.
 

docs/docs/getting-started/getting-started.mdx

@@ -18,7 +18,7 @@ _Note: The SDK is currently in "beta" phase. Interfaces are subject to change in
 
 ### How does it work?
 
-Your images are first analyzed by machine learning (ML) models which are automatically trained on your data. If those models have high enough [confidence](docs/building-applications/3-managing-confidence.md), that's your answer. But if the models are unsure, then the images are progressively escalated to more resource-intensive analysis methods up to real-time human review. So what you get is a computer vision system that starts working right away without even needing to first gather and label a dataset. At first it will operate with high latency, because people need to review the image queries. But over time, the ML systems will learn and improve so queries come back faster with higher confidence.
+Your images are first analyzed by machine learning (ML) models which are automatically trained on your data. If those models have high enough [confidence](docs/building-applications/4-managing-confidence.md), that's your answer. But if the models are unsure, then the images are progressively escalated to more resource-intensive analysis methods up to real-time human review. So what you get is a computer vision system that starts working right away without even needing to first gather and label a dataset. At first it will operate with high latency, because people need to review the image queries. But over time, the ML systems will learn and improve so queries come back faster with higher confidence.
 
 ### Escalation Technology