skrawcz commented on code in PR #1527:
URL: https://github.com/apache/hamilton/pull/1527#discussion_r3049370987
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README.md:
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@@ -53,6 +53,39 @@ under the License.
Apache Hamilton (incubating) is a lightweight Python library for directed
acyclic graphs (DAGs) of data transformations. Your DAG is **portable**; it
runs anywhere Python runs, whether it's a script, notebook, Airflow pipeline,
FastAPI server, etc. Your DAG is **expressive**; Apache Hamilton has extensive
features to define and modify the execution of a DAG (e.g., data validation,
experiment tracking, remote execution).
+## Quick Start (2 minutes)
+
+Get started with Apache Hamilton in just a few lines of code:
+
+```python
+# Step 1: Install (run in terminal)
+# pip install sf-hamilton
+
+# Step 2: Define your functions (nodes in the DAG)
+def A() -> int:
+ return 1
+
+def B(A: int) -> int:
+ return A + 1
+
+# Step 3: Execute the DAG
+from hamilton import driver
+
+dr = driver.Driver({}, __name__)
+result = dr.execute(["B"])
+
+print(result)
+
+Expected output: {'B': 2}
+
+## What’s happening?
+
+Each function becomes a node in the DAG
+Dependencies are defined through function parameters
+Hamilton automatically builds and executes the dependency graph
+
+This is the simplest possible pipeline — from here, you can scale to ML
workflows, ETL pipelines, and production data systems.
Review Comment:
```suggestion
This is the simplest possible pipeline — from here, you can scale to ML
workflows, ETL pipelines, LLM workflows, and production data systems.
```
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