wording

Signed-off-by: stroblme <stroblme@posteo.de>

backend/README.md

@@ -1,22 +1,20 @@
 # Fluksio
 
-A node-based automation engine: flows, dashboards and batch runs, in one
+Fluksio is a node-based automation software that brings trust and reliability to your flow.
-resident process with no infrastructure behind it.
+It just works and looks good.
+Get started by running
 
 ```sh
 pip install fluksio
 fluksio serve
 ```
 
-That is the whole installation — no Docker, no database server, no ports to
+and you're ready to go!
-open. It keeps a SQLite database, a git repository of your flows and an
+Fluksio keeps a SQLite database, a git repository of your flows and an artifact store under `~/.fluksio`, and prints an admin password once upon start.
-artifact store under `~/.fluksio`, and prints an admin password once.
 
 ## For data science
 
-Your functions become nodes where they already live. Install Fluksio into the
+You can turn your existing data science project into a flow by decorating your functions with `@node` ...
-environment you work in and your nodes run on it — the packages are already
-there:
 
 ```python
 # myresearch/train.py
@@ -35,6 +33,8 @@ def fit(dataset, lr, epochs=25):
     return {"weights": fluksio.save_artifact("weights.pt")}
 ```
 
+... and passing them to a `Flow`:
+
 ```python
 # myresearch/pipeline.py
 from fluksio import Flow, Port
@@ -44,36 +44,30 @@ train = Flow("train", nodes=[prepare, fit, evaluate],
              inputs=[Port("lr", "float", initial=0.01)], outputs=["score"])
 ```
 
+Fluksio will automatically infer the order of nodes based on the inputs and outputs you defined.
+When everything is set, you can access a dashboard as follows:
+
 ```sh
 fluksio login --url http://127.0.0.1:8000
 fluksio sync myresearch
 fluksio run train --lr 0.05 --wait
 ```
 
-The decorators return your functions untouched, so everything stays callable,
+Checkout our [documentation](https://docs.fluksio.com/getting-started/data-science/) for more infos.
-testable and importable as what it was. A metric leaves through a declared
-port rather than a logging call, which is why there is no `log_metric()`: the
-run keeps the whole series, a chart can bind to it, and a downstream node can
-consume it.
 
-## What else it does
+## Some other features
 
-- **Flows** — typed messages between nodes, wired by name, edited on a canvas
+- **Flows**: typed messages between nodes, wired by name, edited on a canvas
   or declared in code. Every change is a commit in a git repository you own.
-- **Runs** — an experiment and a CI-style job are the same entity. Parameters,
+- **Runs**: an experiment and a CI-style job are the same entity. Parameters,
   seed, result, per-node timings, artifacts and the commit it ran at.
-- **Dashboards** — charts and controls bound to the same messages the flows
+- **Dashboards**: charts and controls bound to the same messages the flows
   carry, with no separate metrics pipeline.
-- **Remote workers** — `pip install fluksio-worker` on the GPU box; it dials
+- **Remote workers**: `pip install fluksio-worker` on the GPU box; it dials
   *out* over one websocket, so nothing there has to be reachable.
 
-## Links
+Fluksio can also be used for facility automation.
-
+Visit us on [Fluksio.com](fluksio.com) or go straight to our [documentation](https://docs.fluksio.com).
-- Documentation: <https://docs.fluksio.com>
-- Getting started (data science): <https://docs.fluksio.com/getting-started/data-science/>
-- Home: <https://fluksio.com>
-
-Python 3.12 or newer, Linux or macOS.
 
 ## License