linode · nmelehan-akamai · Jul 22, 2026 · Jul 22, 2026
diff --git a/docs/guides/_shortguides/limited-user-note-shortguide/index.md b/docs/guides/_shortguides/limited-user-note-shortguide/index.md
@@ -13,7 +13,7 @@ aliases: []
 ---
 
 {{< note >}}
-This guide is written for a non-root user. Commands that require elevated privileges are prefixed with `sudo`. If you're not familiar with the `sudo` command, visit our [Users and Groups](/cloud/guides/linux-users-and-groups/) guide.
+This guide is written for a non-root user. Commands that require elevated privileges are prefixed with `sudo`. If you're not familiar with the `sudo` command, visit our [Users and Groups](/cloud/guides/linux-users-and-groups) guide.
 
 All configuration files should be edited with elevated privileges. Remember to include `sudo` before running your text editor.
 {{< /note >}}
diff --git a/...solutions/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/index.md b/...solutions/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/index.md
@@ -30,7 +30,7 @@ This guide deploys a chatbot written in Python using these open-source software
 
 - **FastAPI**: Provides components for building a REST API. The API for the example chatbot handles chat requests and responses.
 
-The [Using LangChain and LangGraph to Build a RAG-Powered Chatbot](/cloud/guides/using-langchain-langgraph-build-rag-powered-chatbot/) guide explains the workflow of the application in more detail and provides a walkthrough of relevant code that leverages the LangChain, LangGraph, and FastAPI frameworks.
+The [Using LangChain and LangGraph to Build a RAG-Powered Chatbot](/cloud/guides/using-langchain-langgraph-build-rag-powered-chatbot) guide explains the workflow of the application in more detail and provides a walkthrough of relevant code that leverages the LangChain, LangGraph, and FastAPI frameworks.
 
 If you prefer to deploy to Kubernetes, the [Deploy a RAG-Powered Chatbot with LangChain on LKE](/cloud/guides/deploy-rag-powered-chatbot-langchain-lke) guide shows how to containerize and deploy this application on Linode Kubernetes Engine (LKE).
 

diff --git a/docs/guides/akamai/solutions/deploy-rag-powered-chatbot-langchain-lke/index.md b/docs/guides/akamai/solutions/deploy-rag-powered-chatbot-langchain-lke/index.md
@@ -26,7 +26,7 @@ This guide demonstrates deploying a Python-based RAG chatbot to Linode Kubernete
 
 Deploying to Kubernetes unlocks production capabilities essential for reliable applications. LKE distributes your chatbot across multiple pods for high availability, automatically replaces failed instances, performs rolling updates without downtime, and scales horizontally under load. This guide covers containerizing your application, creating Kubernetes manifests for secrets and configuration, and deploying to a managed cluster.
 
-The [Using LangChain and LangGraph to Build a RAG-Powered Chatbot](/cloud/guides/using-langchain-langgraph-build-rag-powered-chatbot/) guide explains the workflow of the application in more detail and provides a walkthrough of relevant code that leverages the LangChain, LangGraph, and FastAPI frameworks.
+The [Using LangChain and LangGraph to Build a RAG-Powered Chatbot](/cloud/guides/using-langchain-langgraph-build-rag-powered-chatbot) guide explains the workflow of the application in more detail and provides a walkthrough of relevant code that leverages the LangChain, LangGraph, and FastAPI frameworks.
 
 If you prefer a simpler deployment, the [Deploy a RAG-Powered Chatbot with LangChain on an Akamai Compute Instance](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance) guide shows how to run the chatbot on a single compute instance.
 
@@ -107,27 +107,27 @@ Follow these sections from the [Deploy a RAG-Powered Chatbot with LangChain on a
 Wherever an instruction says to run a command on an Akamai compute instance, run that command locally on your workstation instead.
 {{< /note >}}
 
-1. [Clone the Chatbot Codebase](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/#clone-the-chatbot-codebase)
+1. [Clone the Chatbot Codebase](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance#clone-the-chatbot-codebase)
 
-1. [Start a Python Virtual Environment](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/#start-a-python-virtual-environment)
+1. [Start a Python Virtual Environment](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance#start-a-python-virtual-environment)
 
-1. [Copy the .env.example Template](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/#copy-the-envexample-template)
+1. [Copy the .env.example Template](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance#copy-the-envexample-template)
 
-1. [Install Python Dependencies](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/#install-python-dependencies)
+1. [Install Python Dependencies](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance#install-python-dependencies)
 
-1. [Create an OpenAI API Key](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/#create-an-openai-api-key)
+1. [Create an OpenAI API Key](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance#create-an-openai-api-key)
 
-1. [Provision Managed PostgreSQL Databases](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/#provision-managed-postgresql-databases)
+1. [Provision Managed PostgreSQL Databases](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance#provision-managed-postgresql-databases)
 
    - When selecting a region for your databases, use the same region as your LKE cluster.
 
    - When configuring network access for the database, add your workstation's IP address to the allowed list of IPs.
 
-1. [Set Up Linode Object Storage](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/#set-up-linode-object-storage)
+1. [Set Up Linode Object Storage](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance#set-up-linode-object-storage)
 
    - When selecting a region for your object storage bucket, use the same region as your LKE cluster.
 
-1. [Upload Documents to the Object Storage Bucket](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/#upload-documents-to-the-object-storage-bucket)
+1. [Upload Documents to the Object Storage Bucket](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance#upload-documents-to-the-object-storage-bucket)
 
 ### Verify Database Access from LKE
 
@@ -169,7 +169,7 @@ Your cluster can now reach your databases.
 
 ### Index Documents with LangChain
 
-Follow the [Index Documents with LangChain](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/#index-documents-with-langchain) section of the [RAG Chatbot LangChain Compute Instance](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance) guide to initialize your vector database and generate the vector embeddings of your documents.
+Follow the [Index Documents with LangChain](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance#index-documents-with-langchain) section of the [RAG Chatbot LangChain Compute Instance](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance) guide to initialize your vector database and generate the vector embeddings of your documents.
 
 ## Containerize your Chatbot Application
 

diff --git a/...g-data-in-the-linode-cloud-streaming-data-processing-with-apache-storm/index.md b/...g-data-in-the-linode-cloud-streaming-data-processing-with-apache-storm/index.md
@@ -62,7 +62,7 @@ This guide will explain how to configure a working Storm cluster and its Zookeep
 -  A Zookeeper or Storm cluster can have either Ubuntu 14.04 LTS or Debian 8 installed on its nodes. Its distribution does not need to be the same one as the one installed on the cluster manager Linode.
 
 {{< note >}}
-The steps in this guide and in the bash scripts referenced require root privileges. Be sure to run the steps below as `root`. For more information on privileges, see our [Users and Groups](/cloud/guides/linux-users-and-groups/) guide.
+The steps in this guide and in the bash scripts referenced require root privileges. Be sure to run the steps below as `root`. For more information on privileges, see our [Users and Groups](/cloud/guides/linux-users-and-groups) guide.
 {{< /note >}}
 
 ### Naming Conventions

diff --git a/docs/guides/applications/big-data/getting-started-with-pytorch-lightning/index.md b/docs/guides/applications/big-data/getting-started-with-pytorch-lightning/index.md
@@ -23,7 +23,7 @@ Several steps are recommended to optimize the compute time cost savings of GPU-b
 
 ## What Is PyTorch Lightning?
 
-PyTorch Lightning is a module of [PyTorch](/cloud/guides/pytorch-installation-ubuntu-2004/), a developer framework for deep learning. PyTorch builds upon Python's established strengths in data modeling and neural network training through the addition of GPU-optimized capabilities. PyTorch Lightning adds a framework to PyTorch that optimizes productivity in the research and modeling process. This allows portability of code while achieving the same results, as underlying hardware permits.
+PyTorch Lightning is a module of [PyTorch](/cloud/guides/pytorch-installation-ubuntu-2004), a developer framework for deep learning. PyTorch builds upon Python's established strengths in data modeling and neural network training through the addition of GPU-optimized capabilities. PyTorch Lightning adds a framework to PyTorch that optimizes productivity in the research and modeling process. This allows portability of code while achieving the same results, as underlying hardware permits.
 
 PyTorch Lightning allows developers to remove repetitive PyTorch setup code. The framework adds scaling and a command-line interface that allows developers to write modular code with repeatable results. Furthermore, PyTorch Lightning adds scaled GPU utilization that works well with Linode’s specialized GPU-enabled instances. In fact, no code change to existing PyTorch or PyTorch Lightning is needed to take advantage of Linode GPU instances.
 

diff --git a/docs/guides/applications/big-data/getting-started-with-rasa/index.md b/docs/guides/applications/big-data/getting-started-with-rasa/index.md
@@ -24,7 +24,7 @@ In this tutorial, learn how to get started with Rasa. From installing the framew
 1. Follow our [Setting Up and Securing a Compute Instance](https://techdocs.akamai.com/cloud-computing/docs/set-up-and-secure-a-compute-instance) guide to update your system. You may also wish to set the timezone, configure your hostname, create a limited user account, and harden SSH access.
 
 {{< note >}}
-This guide is written for a non-root user. Commands that require elevated privileges are prefixed with `sudo`. If you’re not familiar with the `sudo` command, see the [Users and Groups](/cloud/guides/linux-users-and-groups/) guide.
+This guide is written for a non-root user. Commands that require elevated privileges are prefixed with `sudo`. If you’re not familiar with the `sudo` command, see the [Users and Groups](/cloud/guides/linux-users-and-groups) guide.
 {{< /note >}}
 
 ## How to Install Rasa Open Source
@@ -227,7 +227,7 @@ The following steps walk through setting up these prerequisites and deploying an
     kubectl create namespace rasacluster
     ```
 
-1.  Follow our [Installing Apps on Kubernetes with Helm 3](/cloud/guides/how-to-install-apps-on-kubernetes-with-helm-3/#install-the-helm-client) tutorial how to install the Helm CLI client.
+1.  Follow our [Installing Apps on Kubernetes with Helm 3](/cloud/guides/how-to-install-apps-on-kubernetes-with-helm-3#install-the-helm-client) tutorial how to install the Helm CLI client.
 
     {{< note >}}
     AlmaLinux, CentOS Stream, and Rocky Linux users may need to install `git` and `tar` prior to installing Helm:

diff --git a/docs/guides/applications/big-data/history-of-machine-learning/index.md b/docs/guides/applications/big-data/history-of-machine-learning/index.md
@@ -33,7 +33,7 @@ Machine learning was once a stepping stone on the path to AI’s development, th
 
 However, machine learning is much narrower in its focus and capabilities than general AI. It eventually became apparent that it would be faster and easier (although still not easy) to develop machine learning to more immediate and diverse payloads than to aim it solely at AI’s development.
 
-Machine learning has its own subset, called [deep learning](/cloud/guides/deep-learning-frameworks-overview/), which is even narrower than ML as it is far more specialized. General AI is a smaller subset of self-aware AI, a truly powerful but wholly futuristic form.
+Machine learning has its own subset, called [deep learning](/cloud/guides/deep-learning-frameworks-overview), which is even narrower than ML as it is far more specialized. General AI is a smaller subset of self-aware AI, a truly powerful but wholly futuristic form.
 
 A tidbit in interesting history in machine learning: Deep learning was invented in 1943, which was nine years before machine learning came along. There is some debate over who invented deep learning as it traces back to Walter Pitts and Warren McCulloch’s model in 1943. It didn't widely go by the name “deep learning” until Gregory Hinton rebranded neural net research by that moniker in 2006.
 
@@ -146,4 +146,4 @@ This is also the year that Google Assistant, an AI-powered virtual assistant, wa
 
 ## Conclusion
 
-Today, there are many available open-source tools and frameworks that you can use to power machine learning applications. PyTorch is a Python-based machine learning framework that makes use of CPU and GPU to accelerate its processing performance. You can [install PyTorch on an Ubuntu 20.04 Linode server](/cloud/guides/pytorch-installation-ubuntu-2004/) and make use of [GPU](https://techdocs.akamai.com/cloud-computing/docs/gpu-compute-instances) or [dedicated CPU](https://techdocs.akamai.com/cloud-computing/docs/dedicated-cpu-compute-instances) compute instances.
+Today, there are many available open-source tools and frameworks that you can use to power machine learning applications. PyTorch is a Python-based machine learning framework that makes use of CPU and GPU to accelerate its processing performance. You can [install PyTorch on an Ubuntu 20.04 Linode server](/cloud/guides/pytorch-installation-ubuntu-2004) and make use of [GPU](https://techdocs.akamai.com/cloud-computing/docs/gpu-compute-instances) or [dedicated CPU](https://techdocs.akamai.com/cloud-computing/docs/dedicated-cpu-compute-instances) compute instances.
diff --git a/docs/guides/applications/big-data/how-to-create-message-stream-rabbitmq/index.md b/docs/guides/applications/big-data/how-to-create-message-stream-rabbitmq/index.md
@@ -106,7 +106,7 @@ The examples in this guide assume that you have a RabbitMQ installation availabl
     ```
 
 {{< note >}}
-The steps in this guide require root privileges. Be sure to run the steps below as `root` or with the `sudo` prefix. For more information on privileges, see our [Linux Users and Groups](/cloud/guides/linux-users-and-groups/) guide.
+The steps in this guide require root privileges. Be sure to run the steps below as `root` or with the `sudo` prefix. For more information on privileges, see our [Linux Users and Groups](/cloud/guides/linux-users-and-groups) guide.
 {{< /note >}}
 
 ## How to Create a Message Stream with RabbitMQ

diff --git a/...s/big-data/how-to-install-and-configure-a-redis-cluster-on-ubuntu-1604/index.md b/...s/big-data/how-to-install-and-configure-a-redis-cluster-on-ubuntu-1604/index.md
@@ -12,7 +12,7 @@ license: '[CC BY-ND 4.0](http://creativecommons.org/licenses/by-nd/4.0)'
 aliases: []
 external_resources:
  - '[Redis Official Website](https://redis.io/)'
- - '[Install and Configure Redis on CentOS 7](/cloud/guides/install-and-configure-redis-on-centos-7/)'
+ - '[Install and Configure Redis on CentOS 7](/cloud/guides/install-and-configure-redis-on-centos-7)'
 deprecated: true
 ---
 
@@ -24,9 +24,9 @@ Redis as an in-memory store allows for extremely fast operations such as countin
 
 Prior to starting, we recommend familiarizing yourself with the following:
 
-* [Firewall settings using iptables or ufw](/cloud/guides/configure-firewall-with-ufw/)
+* [Firewall settings using iptables or ufw](/cloud/guides/configure-firewall-with-ufw)
 * [Getting Started with VLANs](https://techdocs.akamai.com/cloud-computing/docs/getting-started-with-vlans)
-* [Master-Replicas Replication](/cloud/guides/how-to-install-a-redis-server-on-ubuntu-or-debian8/)
+* [Master-Replicas Replication](/cloud/guides/how-to-install-a-redis-server-on-ubuntu-or-debian8)
 
 ### Redis Sentinel or Redis Cluster?
 
@@ -70,7 +70,7 @@ Alternatively, you can install the "build-essential" meta-package to load the de
 
 ## Configure Redis Masters and Replicas
 
-This guide manually connects each of the masters and replicas across three Linodes. Consider using [tmux](/cloud/guides/persistent-terminal-sessions-with-tmux/) for the management of multiple terminal windows.
+This guide manually connects each of the masters and replicas across three Linodes. Consider using [tmux](/cloud/guides/persistent-terminal-sessions-with-tmux) for the management of multiple terminal windows.
 
 This guide uses a minimum of six nodes with the following topology:
 

diff --git a/docs/guides/applications/big-data/how-to-install-apache-kafka-on-ubuntu/index.md b/docs/guides/applications/big-data/how-to-install-apache-kafka-on-ubuntu/index.md
@@ -25,7 +25,7 @@ external_resources:
 1.  Follow our [Setting Up and Securing a Compute Instance](https://techdocs.akamai.com/cloud-computing/docs/set-up-and-secure-a-compute-instance) guide to update your system. You may also wish to set the timezone, configure your hostname, create a limited user account, and harden SSH access.
 
 {{< note >}}
-This guide is written for non-root users. Commands that require elevated privileges are prefixed with `sudo`. If you’re not familiar with the `sudo` command, see the [Linux Users and Groups](/cloud/guides/linux-users-and-groups/) guide.
+This guide is written for non-root users. Commands that require elevated privileges are prefixed with `sudo`. If you’re not familiar with the `sudo` command, see the [Linux Users and Groups](/cloud/guides/linux-users-and-groups) guide.
 {{< /note >}}
 
 ## A Summary of the Apache Kafka Installation Process

diff --git a/docs/guides/applications/big-data/how-to-install-tensorflow/index.md b/docs/guides/applications/big-data/how-to-install-tensorflow/index.md
@@ -32,7 +32,7 @@ This guide describes how to install TensorFlow on Ubuntu 20.04, which is fully s
 1.  Follow our [Setting Up and Securing a Compute Instance](https://techdocs.akamai.com/cloud-computing/docs/set-up-and-secure-a-compute-instance) guide to update your system. You may also wish to set the timezone, configure your hostname, create a limited user account, and harden SSH access.
 
 {{< note >}}
-This guide is written for a non-root user. Commands that require elevated privileges are prefixed with `sudo`. If you’re not familiar with the `sudo` command, see the [Linux Users and Groups](/cloud/guides/linux-users-and-groups/) guide.
+This guide is written for a non-root user. Commands that require elevated privileges are prefixed with `sudo`. If you’re not familiar with the `sudo` command, see the [Linux Users and Groups](/cloud/guides/linux-users-and-groups) guide.
 {{< /note >}}
 
 ## Advantages of TensorFlow

diff --git a/...applications/big-data/how-to-move-machine-learning-model-to-production/index.md b/...applications/big-data/how-to-move-machine-learning-model-to-production/index.md
@@ -53,7 +53,7 @@ You will be using Python both to create a model and to deploy the model to a Fla
 
         conda install keras tensorflow h5py pillow flask numpy
 
-If you would like to experiment with the model, you may want to use a Jupyter notebook. See our [Install a Jupyter Notebook Server](/cloud/guides/install-a-jupyter-notebook-server-on-a-linode-behind-an-apache-reverse-proxy/) guide for more details.
+If you would like to experiment with the model, you may want to use a Jupyter notebook. See our [Install a Jupyter Notebook Server](/cloud/guides/install-a-jupyter-notebook-server-on-a-linode-behind-an-apache-reverse-proxy) guide for more details.
 
 ## Prepare a Model
 

diff --git a/...l-a-jupyter-notebook-server-on-a-linode-behind-an-apache-reverse-proxy/index.md b/...l-a-jupyter-notebook-server-on-a-linode-behind-an-apache-reverse-proxy/index.md
@@ -52,7 +52,7 @@ Anaconda is a package manager with built-in support for virtual environments. It
 
 ## Create a Self-Signed Certificate
 
-The official documentation recommends generating a self-signed SSL certificate to prevent sending unencrypted passwords in the Notebook from the browser. This is especially important because Jupyter Notebooks can run bash scripts. If you have a domain name, consider using [Certbot](/cloud/guides/secure-http-traffic-certbot/) rather than a self-signed certificate.
+The official documentation recommends generating a self-signed SSL certificate to prevent sending unencrypted passwords in the Notebook from the browser. This is especially important because Jupyter Notebooks can run bash scripts. If you have a domain name, consider using [Certbot](/cloud/guides/secure-http-traffic-certbot) rather than a self-signed certificate.
 
 1.  Create a self-signed certificate valid for 365 days:
 

diff --git a/...tions/big-data/introduction-to-machine-learning-training-and-inference/index.md b/...tions/big-data/introduction-to-machine-learning-training-and-inference/index.md
@@ -11,7 +11,7 @@ keywords: ['cloud machine learning']
 license: '[CC BY-ND 4.0](https://creativecommons.org/licenses/by-nd/4.0)'
 image: IntroMachineLearning_trainandinterference.png
 external_resources:
-- '[How to Move Your Machine Learning Model to Production](/cloud/guides/how-to-move-machine-learning-model-to-production/)'
+- '[How to Move Your Machine Learning Model to Production](/cloud/guides/how-to-move-machine-learning-model-to-production)'
 - '[Use Cases for Linode GPU Instances](https://techdocs.akamai.com/cloud-computing/docs/gpu-compute-instances)'
 ---