Rewrite docs links to cloud

.gitignore

@@ -45,4 +45,5 @@ hugo_stats.json
 .hugo_build.lock
 redirects.conf
 scripts/duplicates.csv
+scripts/.product_link_cache.json
 algolia_out/

docs/guides/_shortguides/email-warning-shortguide/index.md

@@ -14,5 +14,5 @@ aliases: ['/email-warning-shortguide/']
 ---
 
 {{< note type="warning" title="Email restrictions on the Linode Platform" >}}
-In an effort to fight spam originating from our platform, outbound connections on ports 25, 465, and 587 are blocked by default on Compute Instances for *some* new accounts. These restrictions prevent applications from sending email. If you intend to send email from a Compute Instance, review the [Send Email on the Linode Platform](/docs/products/platform/get-started/guides/send-email/) guide to learn more about our email policies and to request the removal of these restrictions.
+In an effort to fight spam originating from our platform, outbound connections on ports 25, 465, and 587 are blocked by default on Compute Instances for *some* new accounts. These restrictions prevent applications from sending email. If you intend to send email from a Compute Instance, review the [Send Email on the Linode Platform](https://techdocs.akamai.com/cloud-computing/docs/send-email) guide to learn more about our email policies and to request the removal of these restrictions.
 {{< /note >}}

docs/guides/_shortguides/limited-user-note-shortguide/index.md

@@ -13,7 +13,7 @@ aliases: ['/limited-user-note-shortguide/']
 ---
 
 {{< 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](/docs/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 >}}

docs/guides/akamai/solutions/complete-observability-for-live-stream-events-with-trafficpeak/index.md

@@ -43,7 +43,7 @@ TrafficPeak offers sub-second querying and optimizes log indexing with fully cus
 
 1.  [Akamai Media Services Live (Akamai MSL)](https://www.akamai.com/resources/product-brief/media-services-live) ingests the live stream feeds in a duplicated fashion. MSL logs are sent to TrafficPeak to ensure full visibility for any ingest-related issues in real-time.
 
-1.  [Linode Object Storage](/docs/products/storage/object-storage/) stores all live streaming content for instantaneous, low-latency delivery, as well as playback. Object Storage logs are sent to TrafficPeak.
+1.  [Linode Object Storage](https://techdocs.akamai.com/cloud-computing/docs/object-storage) stores all live streaming content for instantaneous, low-latency delivery, as well as playback. Object Storage logs are sent to TrafficPeak.
 
 1.  [Akamai CDN](https://www.akamai.com/solutions/content-delivery-network) caches and delivers live streaming content to millions of concurrent users. CDN logs are also sent to TrafficPeak via Akamai DataStream, including all relevant HTTP(S) information for troubleshooting purposes.
 

docs/guides/akamai/solutions/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/index.md

@@ -30,9 +30,9 @@ 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](/docs/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](/docs/guides/deploy-rag-powered-chatbot-langchain-lke) guide shows how to containerize and deploy this application on Linode Kubernetes Engine (LKE).
+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).
 
 ## Systems and Components
 

docs/guides/akamai/solutions/deploy-rag-powered-chatbot-langchain-lke/index.md

@@ -26,9 +26,9 @@ 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](/docs/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](/docs/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance) guide shows how to run the chatbot on a single compute instance.
+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.
 
 ## Systems and Components
 
@@ -101,33 +101,33 @@ This design means you can destroy any pod without losing data. A replacement pod
 
 ### Set Up the Code Repository, Object Storage, Databases, and OpenAI API Key
 
-Follow these sections from the [Deploy a RAG-Powered Chatbot with LangChain on an Akamai Compute Instance](/docs/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance) guide:
+Follow these sections from the [Deploy a RAG-Powered Chatbot with LangChain on an Akamai Compute Instance](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance) guide:
 
 {{< note >}}
 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](/docs/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](/docs/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](/docs/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](/docs/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](/docs/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](/docs/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](/docs/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](/docs/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](/docs/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance/#index-documents-with-langchain) section of the [RAG Chatbot LangChain Compute Instance](/docs/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
 

docs/guides/akamai/solutions/observability-with-datastream-and-multiplexing/index.md

@@ -13,7 +13,7 @@ Having real-time visibility into log data can help determine how applications ar
 
 One way to achieve an efficient, predictable, and cost-effective observability workflow is to implement a cloud-based multiplexing solution to ingest and parse log data before it’s sent to the relevant DevOps team. Combined with Akamai’s [DataStream](https://techdocs.akamai.com/datastream2/docs/welcome-datastream2) edge-based log reporting, multiplexing can help manage how and where logs are transmitted, improve data security, and reduce overall cost.
 
-This guide outlines the business challenges of observability workflows, integration and migration need-to-knows, and illustrates a working multiplexing reference architecture using [Linode Kubernetes Engine (LKE)](/docs/products/compute/kubernetes/) running [Elastic Stack (ELK)](https://www.elastic.co/elastic-stack/) and [Vector](https://vector.dev/).
+This guide outlines the business challenges of observability workflows, integration and migration need-to-knows, and illustrates a working multiplexing reference architecture using [Linode Kubernetes Engine (LKE)](https://techdocs.akamai.com/cloud-computing/docs/linode-kubernetes-engine) running [Elastic Stack (ELK)](https://www.elastic.co/elastic-stack/) and [Vector](https://vector.dev/).
 
 ## DataStream and Multiplexing Workflow
 

docs/guides/akamai/solutions/using-langchain-langgraph-build-rag-powered-chatbot/index.md

@@ -31,8 +31,8 @@ This guide describes how to leverage LangChain and LangGraph, two open-source pr
 
 {{< note >}}
 Two companion guides demonstrate how to deploy this chatbot on Akamai Cloud:
-- [Deploy a RAG-Powered Chatbot with LangChain on an Akamai Compute Instance](/docs/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance)
-- [Deploy a RAG-Powered Chatbot with LangChain on LKE](/docs/guides/deploy-rag-powered-chatbot-langchain-lke)
+- [Deploy a RAG-Powered Chatbot with LangChain on an Akamai Compute Instance](/cloud/guides/deploy-rag-powered-chatbot-langchain-akamai-compute-instance)
+- [Deploy a RAG-Powered Chatbot with LangChain on LKE](/cloud/guides/deploy-rag-powered-chatbot-langchain-lke)
 {{< /note >}}
 
 ## Workflow Diagram

docs/guides/applications/big-data/big-data-in-the-linode-cloud-streaming-data-processing-with-apache-storm/index.md

@@ -30,7 +30,7 @@ Some use cases where Storm is a good solution:
 -  Analysis of server logs
 -  Internet of Things (IoT) sensor data processing
 
-This guide explains how to create Storm clusters on the Linode cloud using a set of shell scripts that use Linode's Application Programming Interface (APIs) to programmatically create and configure large clusters. The scripts are all provided by the author of this guide via [GitHub repository](https://github.com/pathbreak/storm-linode). This application stack could also benefit from large amounts of disk space, so consider using our [Block Storage](/docs/products/storage/block-storage/) service with this setup.
+This guide explains how to create Storm clusters on the Linode cloud using a set of shell scripts that use Linode's Application Programming Interface (APIs) to programmatically create and configure large clusters. The scripts are all provided by the author of this guide via [GitHub repository](https://github.com/pathbreak/storm-linode). This application stack could also benefit from large amounts of disk space, so consider using our [Block Storage](https://techdocs.akamai.com/cloud-computing/docs/block-storage) service with this setup.
 
 {{< note type="alert" >}}
 External resources are outside of our control, and can be changed and/or modified without our knowledge. Always review code from third party sites yourself before executing.
@@ -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](/docs/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
@@ -78,7 +78,7 @@ These are the names we'll use, but you are welcome to choose your own when creat
 
 ### Get a Linode API Key
 
-Follow the steps in [Generating an API Key](/docs/products/platform/accounts/guides/manage-api-tokens/) and save your key securely. It will be entered into configuration files in upcoming steps.
+Follow the steps in [Generating an API Key](https://techdocs.akamai.com/cloud-computing/docs/manage-personal-access-tokens) and save your key securely. It will be entered into configuration files in upcoming steps.
 
 If the key expires or is removed, remember to create a new one and update the `api_env_linode.conf` API environment configuration file on the cluster manager Linode. This will be explained further in the next section.
 
@@ -253,7 +253,7 @@ Creating a new Storm cluster involves four main steps, some of which are necessa
 
 ### Create a Zookeeper Image
 
-A *Zookeeper image* is a master disk image with all necessary Zookeeper software and libraries installed. We'll create our using [Linode Images](/docs/products/tools/images/) The benefits of using a Zookeeper image include:
+A *Zookeeper image* is a master disk image with all necessary Zookeeper software and libraries installed. We'll create our using [Linode Images](https://techdocs.akamai.com/cloud-computing/docs/images) The benefits of using a Zookeeper image include:
 
 -  Quick creation of a Zookeeper cluster by simply cloning it to create as many nodes as required, each a perfect copy of the image
 -  Distribution packages and third party software packages are identical on all nodes, preventing version mismatch errors

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](/docs/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.