snowplow · jborlase-snowplow · Jul 28, 2026 · Jul 30, 2026
diff --git a/tutorials/python-tracking-and-signals/introduction.md b/tutorials/python-tracking-and-signals/introduction.md
@@ -30,7 +30,7 @@ This tutorial should take around 45 minutes to complete.
 This tutorial assumes that you have:
 
 * a Snowplow pipeline with a [Collector endpoint](/docs/sources/) you can send events to, because Signals computes attributes from your live event stream
-* [Signals enabled](/docs/signals/get-started/) on your Snowplow account, since the personalization features depend on it
+* [Signals enabled](/docs/signals/setup/) on your Snowplow account, since the personalization features depend on it
 * Python 3.9 or later, to run the SDKs
 * basic familiarity with Python and with [Snowplow events, entities, and schemas](/docs/fundamentals/events/)
 

diff --git a/tutorials/signals-agentic-accelerator/intro.md b/tutorials/signals-agentic-accelerator/intro.md
@@ -36,9 +36,12 @@ Together, Signals provides real-time behavioral context while AgentCore Memory p
 
 ## Prerequisites
 
-* A Snowplow CDI pipeline with Signals enabled - the agent fetches behavioral attributes from the Signals Profiles API
+* A Snowplow CDI pipeline with Signals enabled (see [Set up Signals](/docs/signals/setup/)) - the agent fetches behavioral attributes from the Signals Profiles API
 * An AWS account with [Amazon Bedrock](https://aws.amazon.com/bedrock/) access and AgentCore Memory access - the agent runs on Claude via Bedrock, and your IAM user needs permissions for `bedrock:InvokeModel`, `bedrock-agentcore:*`, and `iam:PassRole` (scoped to `bedrock-agentcore.amazonaws.com`) to create AgentCore Memory resources
 * [AWS CLI](https://aws.amazon.com/cli/) installed and configured - used to authenticate with AWS services from the notebook
 * Python 3.11 or later - required by the Strands Agents framework
 * Familiarity with Python and running Jupyter notebooks
 
+:::note[Snowplow account required]
+The behavioral attributes in this accelerator come from events flowing through a real Snowplow pipeline. If you don't have one, you can deploy and use a [Snowplow free trial](https://snowplow.io/get-started/snowplow-free-trial) to follow along.
+:::
diff --git a/tutorials/signals-ai-agent-context/introduction.md b/tutorials/signals-ai-agent-context/introduction.md
@@ -47,10 +47,14 @@ The flow works like this:
 
 This tutorial requires:
 
-* A Snowplow account with [Signals deployed](/docs/signals/connection/)
+* A Snowplow account with Signals enabled (see [Set up Signals](/docs/signals/setup/))
 * Node.js 18+ and npm/pnpm
 * A [Vercel AI Gateway API key](https://vercel.com/docs/ai-gateway/getting-started)
   * This tutorial uses `openai/gpt-4o-mini` via AI Gateway, but any supported model works
 * Basic familiarity with Next.js and TypeScript
 
+:::note[Snowplow account required]
+Signals computes the session attributes from real events flowing through your pipeline. If you don't have one, you can deploy and use a [Snowplow free trial](https://snowplow.io/get-started/snowplow-free-trial) to follow along.
+:::
+
 This tutorial should take approximately 30 minutes to complete.
diff --git a/tutorials/signals-google-adk-agent/introduction.md b/tutorials/signals-google-adk-agent/introduction.md
@@ -45,10 +45,14 @@ The flow works like this:
 
 ## Prerequisites
 
-- A Snowplow account with [Signals deployed](/docs/signals/connection/)
+- A Snowplow account with Signals enabled (see [Set up Signals](/docs/signals/setup/))
 - Node.js 18+ and npm/pnpm
 - Python 3.12+
 - A Google AI Studio API key
   - [AI Studio](https://aistudio.google.com/app/apikey)
   - [Vertex AI](https://docs.cloud.google.com/agent-builder/agent-engine/quickstart-adk)
 - Basic familiarity with React, Python, and TypeScript
+
+:::note[Snowplow account required]
+The agent fetches session attributes that Signals computes from your live event stream. If you don't have a pipeline, you can deploy and use a [Snowplow free trial](https://snowplow.io/get-started/snowplow-free-trial) to follow along.
+:::
diff --git a/tutorials/signals-interventions/start.md b/tutorials/signals-interventions/start.md
@@ -14,6 +14,10 @@ This tutorial provides a hands-on introduction to Signals. You'll use Python to
 
 To follow this tutorial, you'll need a Snowplow account with Signals enabled.
 
+:::note[Snowplow account required]
+If you don't have a Snowplow account, you can sign up for a [Snowplow free trial](https://snowplow.io/get-started/snowplow-free-trial) to follow along.
+:::
+
 This tutorial should take approximately 20-30 minutes to complete.
 
 ## What you'll learn

diff --git a/tutorials/signals-ml-prospect-scoring/intro.md b/tutorials/signals-ml-prospect-scoring/intro.md
@@ -36,10 +36,14 @@ We're calculating aggregated attributes based off real-time stream event data, s
 
 ## Prerequisites
 
-1. Follow the [Connect to Signals](/docs/signals/connection/) page to set up the Signals connection in the Snowplow Console
+1. [Enable Signals](/docs/signals/setup/) for your Snowplow account
 2. Open this [Google Colab](https://colab.research.google.com/github/snowplow-incubator/signals-notebooks/blob/main/web/web_prospect_scoring_end_to_end.ipynb) notebook to follow along
 3. [Optional] Integrate Snowplow into your website
 
+:::note[Snowplow account required]
+Signals calculates the prospect attributes from events flowing through your Snowplow pipeline. If you don't have one, you can deploy and use a [Snowplow free trial](https://snowplow.io/get-started/snowplow-free-trial) to follow along.
+:::
+
 ## Architecture
 
 The system consists of three main blocks:

diff --git a/tutorials/signals-personalize-travel/intro.md b/tutorials/signals-personalize-travel/intro.md
@@ -21,9 +21,13 @@ You'll build a complete personalization system that captures user interests thro
 
 ## Prerequisites
 
-* Signals enabled for your CDI pipeline
+* [Enable Signals](/docs/signals/setup/) for your CDI pipeline
 * The [Snowplow Inspector](https://chromewebstore.google.com/detail/snowplow-inspector/maplkdomeamdlngconidoefjpogkmljm) browser extension installed
 * Familiarity with running Jupyter notebooks, either locally or in Google Colab
 * [Docker](https://www.docker.com/) installed and configured
 * Access to the Jupyter [notebook](https://colab.research.google.com/github/snowplow/documentation/blob/main/static/notebooks/signals-personalize-travel-demo.ipynb)
 * Optional: an [OpenAI API key](https://platform.openai.com/api-keys) to customize agent responses
+
+:::note[Snowplow account required]
+Signals processes the behavioral data from events flowing through your Snowplow pipeline. If you don't have one, you can deploy and use a [Snowplow free trial](https://snowplow.io/get-started/snowplow-free-trial) to follow along.
+:::
diff --git a/tutorials/signals-quickstart/start.md b/tutorials/signals-quickstart/start.md
@@ -22,10 +22,14 @@ This tutorial assumes that you have:
 
 * Snowplow page view tracking on a web application
 * Snowflake warehouse
-* A Signals connection
+* Signals enabled on your Snowplow account
 
-## Connecting to Signals
+:::note[Snowplow account required]
+Signals calculates attributes from events flowing through your Snowplow pipeline. If you don't have an account, you can sign up for a [Snowplow free trial](https://snowplow.io/get-started/snowplow-free-trial) to follow along.
+:::
+
+## Enable Signals
 
 Log in to [Console](https://console.snowplowanalytics.com) and navigate to the **Signals** section.
 
-You'll need to [set up a Signals connection](/docs/signals/connection/) if you don't have one yet.
+If Signals isn't enabled for your organization yet, follow [Set up Signals](/docs/signals/setup/) first.