Update links

docs/data-ai/ai/advanced/upload-external-data.md

@@ -96,7 +96,7 @@ You can also turn off **Syncing** unless you have other directories you'd like t
 
 ## Upload data with Python
 
-You can use the Python data client API [`file_upload_from_path`](/appendix/apis/data-client/#fileuploadfrompath) method to upload one or more files from your computer to the Viam Cloud.
+You can use the Python data client API [`file_upload_from_path`](/dev/reference/apis/data-client/#fileuploadfrompath) method to upload one or more files from your computer to the Viam Cloud.
 
 {{% alert title="Note" color="note" %}}
 
@@ -122,21 +122,21 @@ pip install viam-sdk
 ### Instructions
 
 {{< table >}}
-{{% tablestep link="/appendix/apis/data-client/#establish-a-connection" %}}
+{{% tablestep link="/dev/reference/apis/data-client/#establish-a-connection" %}}
 **1. Get API key**
 
 Go to your organization's setting page and create an API key for your individual {{< glossary_tooltip term_id="part" text="machine part" >}}, {{< glossary_tooltip term_id="part" text="machine" >}}, {{< glossary_tooltip term_id="location" text="location" >}}, or {{< glossary_tooltip term_id="organization" text="organization" >}}.
 
 {{% /tablestep %}}
-{{% tablestep link="/appendix/apis/data-client/" %}}
+{{% tablestep link="/dev/reference/apis/data-client/" %}}
 **2. Add a `file_upload_from_path` API call**
 
 Create a Python script and use the `file_upload_from_path` method to upload your data, depending on whether you are uploading one or multiple files:
 
 {{< tabs >}}
 {{< tab name="Upload a single file" >}}
 
-To upload just one file, make a call to [`file_upload_from_path`](/appendix/apis/data-client/#fileuploadfrompath).
+To upload just one file, make a call to [`file_upload_from_path`](/dev/reference/apis/data-client/#fileuploadfrompath).
 
 {{< expand "Click this to see example code" >}}
 
@@ -186,7 +186,7 @@ if __name__ == "__main__":
 {{% /tab %}}
 {{< tab name="Upload all files in a directory" >}}
 
-To upload all the files in a directory, you can use the [`file_upload_from_path`](/appendix/apis/data-client/#fileuploadfrompath) method inside a `for` loop.
+To upload all the files in a directory, you can use the [`file_upload_from_path`](/dev/reference/apis/data-client/#fileuploadfrompath) method inside a `for` loop.
 
 {{< expand "Click this to see example code" >}}
 
@@ -252,7 +252,7 @@ View your uploaded data in your [**DATA** page in the Viam app](https://app.viam
 
 ## Upload images with the Viam mobile app
 
-Upload images as machine data straight from your phone, skipping the normal data capture and cloud synchronization process, through the [Viam mobile app](/fleet/control/#control-interface-in-the-viam-mobile-app).
+Upload images as machine data straight from your phone, skipping the normal data capture and cloud synchronization process, through the [Viam mobile app](/manage/troubleshoot/teleoperate/default-interface/#viam-mobile-app).
 This is useful if you want to capture images for training machine learning models on the go.
 
 ### Prerequisites

docs/data-ai/ai/create-dataset.md

@@ -239,7 +239,7 @@ If you have 25 images in your dataset, at least 20 of those must be labelled.
 
 {{< expand "Want to add images to a dataset programmatically? Click here." >}}
 
-You can also add all images with a certain label to a dataset using the [`viam dataset data add` command](/cli/#dataset) or the [Data Client API](/appendix/apis/data-client/#addtagstobinarydatabyfilter):
+You can also add all images with a certain label to a dataset using the [`viam dataset data add` command](/dev/cli/#dataset) or the [Data Client API](/dev/reference/apis/data-client/#addtagstobinarydatabyfilter):
 
 {{< tabs >}}
 {{% tab name="CLI" %}}

docs/data-ai/ai/deploy.md

@@ -59,7 +59,7 @@ After deploying your model, you need to configure an additional service to use t
 For example, you can configure an [`mlmodel` vision service](/services/vision/) to visualize the inferences your model makes.
 Follow our docs to [run inference](/data-ai/ai/run-inference/) to add an `mlmodel` vision service and see inferences.
 
-For other use cases, consider [creating custom functionality with a module](/how-tos/create-module/).
+For other use cases, consider [creating custom functionality with a module](/operate/get-started/other-hardware/).
 
 {{< alert title="Add support for other models" color="tip" >}}
 ML models must be designed in particular shapes to work with the `mlmodel` [classification](/services/vision/mlmodel/) or [detection](/services/vision/mlmodel/) model of Viam's [vision service](/services/vision/).

docs/data-ai/ai/run-inference.md

@@ -33,7 +33,7 @@ Then, from the **Select model** dropdown, select the name of the ML model servic
 
 ### Test your changes
 
-You can test a deployed vision service by clicking on the **Test** area of its configuration panel or from the [**CONTROL** tab](/fleet/control/).
+You can test a deployed vision service by clicking on the **Test** area of its configuration panel or from the [**CONTROL** tab](/manage/troubleshoot/teleoperate/default-interface/#viam-app).
 
 The camera stream shows when the vision service identifies something.
 Try pointing the camera at a scene similar to your training data.

docs/data-ai/ai/train-tflite.md

@@ -149,7 +149,7 @@ Your training script may output logs at the error level but still succeed.
 
 {{< /alert >}}
 
-You can also view your training jobs' logs with the [`viam train logs`](/cli/#train) command.
+You can also view your training jobs' logs with the [`viam train logs`](/dev/cli/#train) command.
 
 {{% /tablestep %}}
 {{< /table >}}

docs/data-ai/ai/train.md

@@ -27,7 +27,7 @@ If you wish to do this, skip to [Submit a training job](#submit-a-training-job).
 
 For image data, you can follow the instructions to [Create a dataset](/data-ai/ai/create-dataset/) to create a dataset and label data.
 
-For other data you can use the [Data Client API](/appendix/apis/data-client/) from within the training script to get data stored in the Viam Cloud.
+For other data you can use the [Data Client API](/dev/reference/apis/data-client/) from within the training script to get data stored in the Viam Cloud.
 
 {{% /expand%}}
 
@@ -414,7 +414,7 @@ Update the main to call the functions you have just created.
 {{% tablestep %}}
 **9. Using Viam APIs in a training script**
 
-If you need to access any of the [Viam APIs](/appendix/apis/) within a custom training script, you can use the environment variables `API_KEY` and `API_KEY_ID` to establish a connection.
+If you need to access any of the [Viam APIs](/dev/reference/apis/) within a custom training script, you can use the environment variables `API_KEY` and `API_KEY_ID` to establish a connection.
 These environment variables will be available to training scripts.
 
 ```python
@@ -442,7 +442,7 @@ You can export one of your Viam datasets to test your training script locally.
 {{% tablestep %}}
 **1. Export your dataset**
 
-You can get the dataset ID from the dataset page or using the [`viam dataset list`](/cli/#dataset) command:
+You can get the dataset ID from the dataset page or using the [`viam dataset list`](/dev/cli/#dataset) command:
 
 ```sh {class="command-line" data-prompt="$"}
 viam dataset export --destination=<destination> --dataset-id=<dataset-id> --include-jsonl=true
@@ -514,7 +514,7 @@ viam training-script upload --path=my-training.tar.gz \
 {{% /tab %}}
 {{< /tabs >}}
 
-You can also [specify the version, framework, type, visibility, and description](/cli/#training-script) when uploading a custom training script.
+You can also [specify the version, framework, type, visibility, and description](/dev/cli/#training-script) when uploading a custom training script.
 
 To find your organization's ID, run the following command:
 
@@ -530,7 +530,7 @@ You can view uploaded training scripts by navigating to the [registry's **Traini
 
 ## Submit a training job
 
-After uploading the training script, you can run it by submitting a training job through the Viam app or using the Viam CLI or [ML Training client API](/appendix/apis/ml-training-client/#submittrainingjob).
+After uploading the training script, you can run it by submitting a training job through the Viam app or using the Viam CLI or [ML Training client API](/dev/reference/apis/ml-training-client/#submittrainingjob).
 
 {{< table >}}
 {{% tablestep %}}
@@ -548,7 +548,7 @@ Click **Train model** and select **Train on a custom training script**, then fol
 {{% /tab %}}
 {{% tab name="CLI" %}}
 
-You can use [`viam train submit custom from-registry`](/cli/#positional-arguments-submit) to submit a training job.
+You can use [`viam train submit custom from-registry`](/dev/cli/#positional-arguments-submit) to submit a training job.
 
 For example:
 
@@ -562,7 +562,7 @@ viam train submit custom from-registry --dataset-id=<INSERT DATASET ID> \
 
 This command submits a training job to the previously uploaded `MyCustomTrainingScript` with another input dataset, which trains `MyRegistryModel` and publishes that to the registry.
 
-You can get the dataset id from the dataset page or using the [`viam dataset list`](/cli/#dataset) command.
+You can get the dataset id from the dataset page or using the [`viam dataset list`](/dev/cli/#dataset) command.
 
 {{% /tab %}}
 {{< /tabs >}}
@@ -595,7 +595,7 @@ Your training script may output logs at the error level but still succeed.
 
 {{< /alert >}}
 
-You can also view your training jobs' logs with the [`viam train logs`](/cli/#train) command.
+You can also view your training jobs' logs with the [`viam train logs`](/dev/cli/#train) command.
 
 {{% /tablestep %}}
 {{< /table >}}

docs/data-ai/capture-data/conditional-sync.md

@@ -289,7 +289,7 @@ To test your setup, [configure a webcam](/components/camera/webcam/) or another
 Make sure to physically connect any hardware parts to the computer controlling your machine.
 For a camera component, use the `ReadImage` method.
 The data manager will now capture data.
-Go to the [**CONTROL** tab](/fleet/control/).
+Go to the [**CONTROL** tab](/manage/troubleshoot/teleoperate/default-interface/#viam-app).
 You should see the sensor.
 Click on `GetReadings`.
 

docs/data-ai/data/visualize.md

@@ -267,7 +267,7 @@ See the [guide on querying sensor data](/how-tos/sensor-data-query-with-third-pa
 
 For more detailed instructions on using Grafana, including a full step-by-step configuration walkthrough, see [visualizing data with Grafana](/tutorials/services/visualize-data-grafana/).
 
-On top of visualizing sensor data with third-party tools, you can also [query it with the Python SDK](/appendix/apis/data-client/) or [query it with the Viam app](/data-ai/data/query/).
+On top of visualizing sensor data with third-party tools, you can also [query it with the Python SDK](/dev/reference/apis/data-client/) or [query it with the Viam app](/data-ai/data/query/).
 
 To see full projects using visualization, check out these resources:
 

docs/data-ai/reference/vision/mlmodel.md

@@ -228,7 +228,7 @@ If the classifier's results exceed the confidence threshold, the **Run model** s
 
 ### Live camera footage
 
-You can test your detector or classifier from the [**Control tab**](/fleet/control/) or with code using a camera that is part of your machine.
+You can test your detector or classifier from the [**Control tab**](/manage/troubleshoot/teleoperate/default-interface/#viam-app) or with code using a camera that is part of your machine.
 
 #### Test your vision service
 

docs/dev/_index.md

@@ -430,7 +430,7 @@ result, err := myTwilioSvc.DoCommand(context.Background(), command)
 
 Using the Viam Registry you can turn services and your own custom business logic into _{{< glossary_tooltip term_id="module" text="modules" >}}_. You can then deploy your modules to your machines.
 
-[Create a module →](/how-tos/create-module/)
+[Create a module →](/operate/get-started/other-hardware/)
 
   </div>
 </div>
@@ -711,7 +711,7 @@ for m in machines:
 
 Get status information and logs from all your deployed machines using the fleet management API.
 
-[Learn about Platform APIs →](/appendix/apis/#platform-apis)
+[Learn about Platform APIs →](/dev/reference/apis/#platform-apis)
 
   </div>
 </div>

docs/dev/contributing.md

@@ -159,7 +159,7 @@ The docs use the [Diátaxis Framework](https://diataxis.fr/) as the basis of t
   {{< /expand >}}
 
 - **Reference**: A concise, information-oriented piece of content that generally starts with an overview/introduction and then a list of some kind (configuration options, API methods, etc.).
-  Examples include the [API pages](/appendix/apis/) as well as [component and service pages](/operate/reference/components/arm/).
+  Examples include the [API pages](/dev/reference/apis/) as well as [component and service pages](/operate/reference/components/arm/).
 
   Example template: [Component template](https://github.com/viamrobotics/docs/blob/main/docs/components/component/_index.md).
 

docs/dev/reference/apis/_index.md

@@ -17,7 +17,7 @@ date: "2024-10-01"
 
 Every Viam {{< glossary_tooltip term_id="resource" text="resource" >}} exposes an [application programming interface (API)](https://en.wikipedia.org/wiki/API) described through [protocol buffers](https://developers.google.com/protocol-buffers).
 
-The API methods provided by the SDKs for each of these resource APIs wrap gRPC client requests to the machine when you execute your program, providing you a convenient interface for accessing information about and controlling the {{< glossary_tooltip term_id="resource" text="resources" >}} you have [configured](/configure/) on your machine.
+The API methods provided by the SDKs for each of these resource APIs wrap gRPC client requests to the machine when you execute your program, providing you a convenient interface for accessing information about and controlling the {{< glossary_tooltip term_id="resource" text="resources" >}} you have [configured](/operate/get-started/supported-hardware/) on your machine.
 
 ## Platform APIs