Better landing page

README.md

@@ -12,24 +12,30 @@
 
 # Beavers
 
-**Beavers** is a python library for stream processing, optimized for analytics. 
+[Beavers][1] is a python library for stream processing, optimized for analytics. 
 
-It is used at [Tradewell Technologies](https://www.tradewelltech.co/), 
+It is used at [Tradewell Technologies][2], 
 to calculate analytics and serve model predictions,
 in both realtime and batch jobs.
 
-# Key Features
+## Key Features
 
 
 * Works in **real time** (eg: reading from kafka) and **replay mode** (eg: reading from parquet)
 * Optimized for analytics, it uses micro-batching (instead of processing records one by one)
-* Similar to [incremental](https://github.com/janestreet/incremental), it  updates nodes in a dag incrementally
-* Taking inspiration from [kafka streams](https://www.confluent.io/blog/kafka-streams-tables-part-1-event-streaming/), there are two types of nodes in the dag:
+* Similar to [incremental][3], it  updates nodes in a dag incrementally
+* Taking inspiration from [kafka streams][4], there are two types of nodes in the dag:
   * **Stream:** ephemeral micro-batches of events (cleared after every cycle)
   * **State:** durable state derived from streams
 * Clear separation between the business logic and the IO. 
   So the same dag can be used in real time mode, replay mode or can be easily tested.
-* functional interface (no inheritance required)
+* Functional interface: no inheritance or decorator required
+
+
+[1]: https://github.com/tradewelltech/beavers
+[2]: https://www.tradewelltech.co/
+[3]: https://github.com/janestreet/incremental
+[4]: https://www.confluent.io/blog/kafka-streams-tables-part-1-event-streaming/
 
 [pypi-image]: https://img.shields.io/pypi/v/beavers
 [pypi-url]: https://pypi.org/project/beavers/

docs/index.md

@@ -1,3 +1,27 @@
+
 # Beavers
 
-[**Beavers**](https://github.com/tradewelltech/beavers) is a python library for stream processing, optimized for analytics.
+[Beavers][1] is a python library for stream processing, optimized for analytics. 
+
+It is used at [Tradewell Technologies][2], 
+to calculate analytics and serve model predictions,
+in both realtime and batch jobs.
+
+## Key Features
+
+
+* Works in **real time** (eg: reading from kafka) and **replay mode** (eg: reading from parquet)
+* Optimized for analytics, it uses micro-batching (instead of processing records one by one)
+* Similar to [incremental][3], it  updates nodes in a dag incrementally
+* Taking inspiration from [kafka streams][4], there are two types of nodes in the dag:
+  * **Stream:** ephemeral micro-batches of events (cleared after every cycle)
+  * **State:** durable state derived from streams
+* Clear separation between the business logic and the IO. 
+  So the same dag can be used in real time mode, replay mode or can be easily tested.
+* Functional interface: no inheritance or decorator required
+
+
+[1]: https://github.com/tradewelltech/beavers
+[2]: https://www.tradewelltech.co/
+[3]: https://github.com/janestreet/incremental
+[4]: https://www.confluent.io/blog/kafka-streams-tables-part-1-event-streaming/