Docs: x86 and arm64, Raspberry Pi included; one container in the start pages

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

README.md

@@ -49,7 +49,7 @@ FLUKSIO_WORKER_TOKEN=… fluksio-worker --url wss://api.fluksio.com/api/v1/worke
 
 ## Getting started
 
-Needs Docker with Compose v2.
+Needs Docker with Compose v2, on x86-64 or arm64 such as a Raspberry Pi 4 or 5.
 
 ```sh
 docker compose up -d           # http://localhost:8080

docs/getting-started/facility-automation.md

@@ -5,8 +5,9 @@ No hardware required for the first lap.
 
 ## Start Fluksio
 
-You need Git and Docker with Compose v2. Clone the app repository and start
-its single container:
+You need Git and Docker with Compose v2 on a 64-bit machine, x86 or ARM. A
+Raspberry Pi 4 or 5 with a 64-bit OS works too. Clone the app repository and
+start its single container:
 
 ```sh
 git clone https://git.stroblme.de/Fluksio/app.git fluksio
@@ -15,9 +16,10 @@ docker compose up -d
 docker compose logs fluksio
 ```
 
-The first build takes a few minutes. The log shows the admin account and its
-password once, on the first start. Open [http://localhost:8080](http://localhost:8080)
-and sign in. Adding Python code requires that superuser account.
+The first build takes a few minutes, longer on a Raspberry Pi. The log shows
+the admin account and its password once, on the first start. Open
+[http://localhost:8080](http://localhost:8080) and sign in. Adding Python code
+requires that superuser account.
 
 Everything runs in that one container on one port: the engine, its API and the
 dashboard. To use another port, change the left side of `ports` in

docs/getting-started/index.md

@@ -26,8 +26,9 @@ node, record metrics and artifacts, and compare experiments. Connect to
 
 *"I have a bunch of sensors. I want them to do something."*
 
-Start the Docker stack for a local engine and browser interface. Build a live
-flow, connect your devices, and put their values on a dashboard.
+Start one Docker container for a local engine and browser interface, on a
+server, a PC or a Raspberry Pi. Build a live flow, connect your devices, and
+put their values on a dashboard.
 
 [Start with Docker →](facility-automation.md)
 

docs/index.md

@@ -17,8 +17,8 @@ next bright idea comes with an undo trail.
 
 - [Data science](getting-started/data-science.md): install with pip in your
   research environment and run your first experiment.
-- [Facility automation](getting-started/facility-automation.md): start the
-  Docker stack and build your first live flow in the browser.
+- [Facility automation](getting-started/facility-automation.md): start one
+  Docker container and build your first live flow in the browser.
 
 ## Explore