Technical review of Ollama concierge agent

Author: annietllnd

content/learning-paths/cross-platform/ai-agent-cpu-orchestration/2-setup.md

@@ -12,15 +12,6 @@ In this section, you'll prepare everything the agent needs before running it: a
 
 These steps are identical on an Apple silicon MacBook, an Arm Linux machine, and an NVIDIA DGX Spark.
 
-## Get a Serper API key
-
-The agent searches the web through [Serper](https://serper.dev/), a Google Search API. The free tier is enough to complete this Learning Path.
-
-1. Go to [serper.dev](https://serper.dev/) and create a free account.
-2. Open your dashboard and copy your API key.
-
-You'll set this key as an environment variable later in this section, so the agent can read it without the key being written into the code.
-
 ## Create a project directory and virtual environment
 
 A virtual environment keeps the agent's dependencies isolated from your system Python, so you always run against the right package versions.
@@ -53,7 +44,12 @@ pip install requests beautifulsoup4
 
 ## Set your Serper API key
 
-Export your API key as an environment variable in the same terminal session:
+The agent searches the web through [Serper](https://serper.dev/), a Google Search API. The free tier is enough to complete this Learning Path.
+
+1. Go to [serper.dev](https://serper.dev/) and create a free account.
+2. Once logged in, you will see the dashboard. On the left, click **API Keys**.
+3. Copy the Default key.
+4. Export your API key as an environment variable in the same terminal session as your virtual environment:
 
 ```bash
 export SERPER_API_KEY="your-serper-api-key"

content/learning-paths/cross-platform/ai-agent-cpu-orchestration/3-ollama.md

@@ -20,26 +20,33 @@ Running locally has three benefits that matter for an agent:
 
 ## Install and start Ollama
 
-Install [Ollama](https://ollama.com/) and start its server using one of the following options. The server exposes a local API at `http://localhost:11434` that the agent connects to.
+Install [Ollama](https://ollama.com/) and start its server. The server exposes a local API at `http://localhost:11434` that the agent connects to.
+
+1. Install Ollama using one of the following options:
 
 {{< tabpane code=true >}}
-{{< tab header="Homebrew (Recommended)" language="bash">}}
-# Install Ollama
+{{< tab header="Homebrew" language="bash">}}
 brew install ollama
-
-# Start the server as a background service (no terminal to keep open)
-brew services start ollama
 {{< /tab >}}
 {{< tab header="Install script" language="bash">}}
-# Install Ollama
 curl -fsSL https://ollama.com/install.sh | sh
+{{< /tab >}}
+{{< /tabpane >}}
+
+2. Start the Ollama server using the same method you used to install it:
 
-# Start the server (leave this running, and open a second terminal)
+{{< tabpane code=true >}}
+{{< tab header="Homebrew" language="bash">}}
+brew services start ollama
+{{< /tab >}}
+{{< tab header="Install script" language="bash">}}
 ollama serve
 {{< /tab >}}
 {{< /tabpane >}}
 
-With [Homebrew](https://brew.sh/), `brew services` runs Ollama in the background, so you can use the same terminal throughout. With the install script, `ollama serve` runs in the foreground, so keep that terminal open and use a second terminal for the remaining commands.
+With [Homebrew](https://brew.sh/), `brew services` runs Ollama in the background, so you can use the same terminal throughout. To stop it later, run `brew services stop ollama`.
+
+With the install script, `ollama serve` runs in the foreground. Keep that terminal open and use a second terminal for the remaining commands. To stop it later, press **Ctrl+C** in the terminal running `ollama serve`.
 
 ## Pull the Gemma model