weaviategh-1633: improve readme

TraceMachina · Jul 23, 2021 · 5ea18d7 · 5ea18d7
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 [![Slack](https://img.shields.io/badge/slack--channel-blue?logo=slack)](https://join.slack.com/t/weaviate/shared_invite/zt-goaoifjr-o8FuVz9b1HLzhlUfyfddhw)
 [![Newsletter](https://img.shields.io/badge/newsletter-blue?logo=mailchimp)](https://www.semi.technology/newsletter/)
 
-Weaviate is a vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale. Weaviate helps in various domains: 
+## Description
+
+**Weaviate in a nutshell**: Weaviate is a vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale.
+
+**Weaviate in detail**: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance of a cloud-native database, all accessible through GraphQL, REST, and various language clients.
+
+## Weaviate helps ...
 
-1. Weaviate for **Software Engineers**: Use Weaviate as a ML-first database for your applications. ([docs](https://www.semi.technology/developers/weaviate/current/))
-    * [Out-of-the-box modules](https://www.semi.technology/developers/weaviate/current/modules/) for: NLP/semantic search, automatic classification and image similarity search.
-    * Easy to integrate in your current architecture, with full [CRUD support](https://db-engines.com/en/blog_post/87) like you're used to from other OSS databases.
-    * Designed for [scale](https://www.semi.technology/developers/weaviate/current/architecture/roadmap.html).
+1. **Software Engineers** ([docs](https://www.semi.technology/developers/weaviate/current/)) - Who use Weaviate as an ML-first database for your applications. 
+    * Out-of-the-box modules for: NLP/semantic search, automatic classification and image similarity search.
+    * Easy to integrate in your current architecture, with full CRUD support like you're used to from other OSS databases.
+    * Containerized and built on top of Kubernetes for scalability.
 
-2. Weaviate for **Data Engineers**: Weaviate is a vector database built from the ground up with ANN at its core, and with the same UX you love from Lucene-based search engines. ([docs](https://www.semi.technology/developers/weaviate/current/))
+2. **Data Engineers** ([docs](https://www.semi.technology/developers/weaviate/current/)) - Who use Weaviate as a vector database that is built up from the ground with ANN at its core, and with the same UX they love from Lucene-based search engines.
     * Weaviate has a modular setup that allows to use your own ML models inside Weaviate, but you can also use out-of-the-box ML models (e.g., SBERT, ResNet, fasttext, etc).
     * Weaviate takes care of the scalability, so that you don't have to.
     * Deploy and maintain ML models in production reliably and efficiently.
 
-3. Weaviate for **Data Scientists**: Use Weaviate for a seamless handover of your Machine Learning models to MLOps. ([docs](https://www.semi.technology/developers/weaviate/current/))
+3. **Data Scientists** ([docs](https://www.semi.technology/developers/weaviate/current/)) - Who use Weaviate for a seamless handover of their Machine Learning models to MLOps.
     * Deploy and maintain your ML models in production reliably and efficiently.
     * Weaviate's modular design allows you to easily package any custom trained model you want.
     * Smooth and accelerated handover of your Machine Learning models to engineers.
 
-## Weaviate & GraphQL Demo
+## GraphQL interface demo
 
 <a href="https://semi.technology/developers/weaviate/current/" target="_blank"><img src="https://www.semi.technology/img/weaviate-demo.gif?i=7" alt="Demo of Weaviate" width="100%"></a>
 
 <sup>Weaviate GraphQL demo on news article dataset containing: Transformers module, GraphQL usage, semantic search, _additional{} features, Q&A, and Aggregate{} function. You can the demo on this dataset in the GUI here: <a href="https://console.semi.technology/console/query#weaviate_uri=https://demo.dataset.playground.semi.technology&graphql_query=%7B%0A%20%20Get%20%7B%0A%20%20%20%20Article(%0A%20%20%20%20%20%20nearText%3A%20%7B%0A%20%20%20%20%20%20%20%20concepts%3A%20%5B%22Housing%20prices%22%5D%0A%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20where%3A%20%7B%0A%20%20%20%20%20%20%20%20operator%3A%20Equal%0A%20%20%20%20%20%20%20%20path%3A%20%5B%22inPublication%22%2C%20%22Publication%22%2C%20%22name%22%5D%0A%20%20%20%20%20%20%20%20valueString%3A%20%22The%20Economist%22%0A%20%20%20%20%20%20%7D%0A%20%20%20%20)%20%7B%0A%20%20%20%20%20%20title%0A%20%20%20%20%20%20inPublication%20%7B%0A%20%20%20%20%20%20%20%20...%20on%20Publication%20%7B%0A%20%20%20%20%20%20%20%20%20%20name%0A%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20_additional%20%7B%0A%20%20%20%20%20%20%20%20certainty%0A%20%20%20%20%20%20%7D%0A%20%20%20%20%7D%0A%20%20%7D%0A%7D" target="_blank">semantic search</a>, <a href="https://console.semi.technology/console/query#weaviate_uri=https://demo.dataset.playground.semi.technology&graphql_query=%7B%0A%20%20Get%7B%0A%20%20%20%20Article(%0A%20%20%20%20%20%20ask%3A%20%7B%0A%20%20%20%20%20%20%20%20question%3A%20%22What%20did%20Jemina%20Packington%20predict%3F%22%0A%20%20%20%20%20%20%20%20properties%3A%20%5B%22summary%22%5D%0A%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20limit%3A%201%0A%20%20%20%20)%7B%0A%20%20%20%20%20%20title%0A%20%20%20%20%20%20inPublication%20%7B%0A%20%20%20%20%20%20%20%20...%20on%20Publication%20%7B%0A%20%20%20%20%20%20%20%20%20%20name%0A%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20_additional%20%7B%0A%20%20%20%20%20%20%20%20answer%20%7B%0A%20%20%20%20%20%20%20%20%20%20endPosition%0A%20%20%20%20%20%20%20%20%20%20property%0A%20%20%20%20%20%20%20%20%20%20result%0A%20%20%20%20%20%20%20%20%20%20startPosition%0A%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%7D%0A%20%20%20%20%7D%0A%20%20%7D%0A%7D" target="_blank">Q&A</a>, <a href="https://console.semi.technology/console/query#weaviate_uri=https://demo.dataset.playground.semi.technology&graphql_query=%7B%0A%20%20Aggregate%20%7B%0A%20%20%20%20Article%20%7B%0A%20%20%20%20%20%20meta%20%7B%0A%20%20%20%20%20%20%20%20count%0A%20%20%20%20%20%20%7D%0A%20%20%20%20%7D%0A%20%20%7D%0A%7D" target="_blank">Aggregate</a>.</sup>
 
-## Description
-
-Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance of a cloud-native database, all accessible through GraphQL, REST, and various language clients.
-
 ## Features
 
 Weaviate makes it easy to use state-of-the-art ML models while giving you the scalability, ease of use, safety and cost-effectiveness of a purpose-built vector database. Most notably: