diff --git a/README.md b/README.md
index dbfbd1b5124..004fa1d4082 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,11 @@
 # DocArray - Version 2
 
-_**NOTE**: This introduction refers to version 2 of DocArray, a rewrite that is currently at alpha stage.
-Not all features that are mentioned here are already implemented.
-If you are looking for the version 2 implementation roadmap see [here](https://github.com/docarray/docarray/issues/780),
-for the (already released) version 1 of DocArray
-see [here](https://github.com/docarray/docarray)._
+> **Note**
+> This introduction refers to version 2 of DocArray, a rewrite that is currently at the alpha stage.
+> Not all features that are mentioned here are implemented yet.
+> If you are looking for the version 2 implementation roadmap see [here](https://github.com/docarray/docarray/issues/780),
+> for the (already released) version 1 of DocArray
+> see [here](https://github.com/docarray/docarray)._
 
 DocArray is a library for **representing, sending and storing multi-modal data**, with a focus on applications in **ML** and
 **Neural Search**.
@@ -42,8 +43,8 @@ doc.embedding = clip_image_encoder(
 print(doc.embedding.shape)
 ```
 
-- **Model** data of any type (audio, video, text, images, 3D meshes, raw tensors, etc) as a single, unified data structure, the `Document`
-  - A `Document` is a juiced-up [Pydantic Model](https://pydantic-docs.helpmanual.io/usage/models/), inheriting all the benefits, while extending it with ML focussed features 
+- **Model** data of any type (audio, video, text, images, 3D meshes, raw tensors, etc) as a Document, a single, unified data structure.
+  - A `Document` is a juiced-up [Pydantic Model](https://pydantic-docs.helpmanual.io/usage/models/), inheriting all the benefits, while extending it with ML focused features.
 
 ### Use pre-defined `Document`s for common use cases:
 
@@ -102,7 +103,6 @@ da = DocumentArray[Image](
 )
 ```
 
-
 Access fields at the DocumentArray level:
 
 ```python
@@ -124,7 +124,7 @@ print(da.tensor.shape)
 ## Send
 - **Serialize** any `Document` or `DocumentArray` into _protobuf_, _json_, _jsonschema_, _bytes_ or _base64_
 - Use in **microservice** architecture: Send over **HTTP** or **gRPC**
-- Integrate seamlessly with **FastAPI** and **Jina**
+- Integrate seamlessly with **[FastAPI](https://github.com/tiangolo/fastapi/)** and **[Jina](https://github.com/jina-ai/jina/)**
 
 ```python
 from docarray.documents import ImageDoc
@@ -144,7 +144,7 @@ Image.from_protobuf(doc.to_protobuf())
 ```
 
 ## Store
-- Persist and `DocumentArray` using a **`DocumentStore`**
+- Persist a `DocumentArray` using a **`DocumentStore`**
 - Store your Documents in any supported (vector) database: **Elasticsearch**, **Qdrant**, **Weaviate**, **Redis**, **Milvus**, **ANNLite** or **SQLite**
 - Leverage DocumentStores to **perform vector search on your multi-modal data**
 
@@ -175,7 +175,7 @@ _DocArray v2 is that idea, taken seriously._ Every `Document` is created through
 courtesy of [Pydantic](https://pydantic-docs.helpmanual.io/usage/models/).
 
 This gives the following advantages:
-- **Flexibility:** No need to conform to a fixed set of fields, your data defines the schema
+- **Flexibility:** No need to conform to a fixed set of fields -- your data defines the schema
 - **Multi-modality:** Easily store multiple modalities and multiple embeddings in the same Document
 - **Language agnostic:** At its core, Documents are just dictionaries. This makes it easy to create and send them from any language, not just Python.
 
@@ -183,16 +183,16 @@ This gives the following advantages:
 
 If you come from Pydantic, you can see Documents as juiced up models, and DocArray as a collection of goodies around them.
 
-- **ML focussed types**: Tensor, TorchTensor, TFTensor, Embedding, ...
+- **ML focused types**: Tensor, TorchTensor, TFTensor, Embedding, ...
 - **Types that are alive**: ImageUrl can `.load()` a URL to image tensor, TextUrl can load and tokenize text documents, etc.
-- **Pre-built Documents** for different data modalities: Image, Text, 3DMesh, Video, Audio, ... Note that all of these will be valid Pydantic models!
+- **Pre-built Documents** for different data modalities: Image, Text, 3DMesh, Video, Audio and more. Note that all of these will be valid Pydantic models!
 - The concepts of **DocumentArray and DocumentStore**
-- Cloud ready: Serialization to **Protobuf** for use with microservices and **gRPC**
+- Cloud-ready: Serialization to **Protobuf** for use with microservices and **gRPC**
 - Support for **vector search functionalities**, such as `find()` and `embed()`
 
 ## Coming from PyTorch
 
-DocArray can be used directly inside ML models to handle and represent multi-modal data. This allows you to reason about your data using DocArray's abstractions deep inside of `nn.Module`, and provides a (FastAPI compatible) schema that eases the transition between model training and model serving.
+DocArray can be used directly inside ML models to handle and represent multi-modal data. This allows you to reason about your data using DocArray's abstractions deep inside of `nn.Module`, and provides a (FastAPI-compatible) schema that eases the transition between model training and model serving.
 
 To see the effect of this, let's first observe a vanilla PyTorch implementation of a tri-modal ML model:
 
@@ -228,9 +228,9 @@ class MyMultiModalModel(nn.Module):
         )
 ```
 
-Not very easy on the eyes if you ask us. And even worse, if you need to add one more modality you have to touch every part of your code base, changing the `forward()` return type and make a whole lot of changes downstream from that.
+Not very easy on the eyes if you ask us. And even worse, if you need to add one more modality you have to touch every part of your code base, changing the `forward()` return type and making a whole lot of changes downstream from that.
 
-So now let's see what the same code looks like with DocArray:
+So, now let's see what the same code looks like with DocArray:
 
 ```python
 from docarray import DocumentArray, BaseDocument
@@ -274,23 +274,23 @@ class MyPodcastModel(nn.Module):
 
 Looks much better, doesn't it?
 You instantly win in code readability and maintainability. And for the same price you can turn your PyTorch model into a FastAPI app and reuse your Document
-schema definition (see below). Everything handles in a pythonic manner by relying on type hints.
-
+schema definition (see below). Everything is handled in a pythonic manner by relying on type hints.
 
 ## Coming from TensorFlow
 
 Similar to the PyTorch approach, you can also use DocArray with TensorFlow to handle and represent multi-modal data inside your ML model.
 
 First off, to use DocArray with TensorFlow we first need to install it as follows:
+
 ```
 pip install tensorflow==2.11.0
 pip install protobuf==3.19.0
 ```
 
 Compared to using DocArray with PyTorch, there is one main difference when using it with TensorFlow:\
-While DocArray's `TorchTensor` is a subclass of `torch.Tensor`, this is not the case for the `TensorFlowTensor`: Due to technical limitations on `tf.Tensor`, docarray's `TensorFlowTensor` is not a subclass of `tf.Tensor` but instead stores a `tf.Tensor` in its `.tensor` attribute. 
+While DocArray's `TorchTensor` is a subclass of `torch.Tensor`, this is not the case for the `TensorFlowTensor`: Due to some technical limitations of `tf.Tensor`, DocArray's `TensorFlowTensor` is not a subclass of `tf.Tensor` but rather stores a `tf.Tensor` in its `.tensor` attribute. 
 
-How does this effect you? Whenever you want to access the tensor data to e.g. do operations with it or hand it to your ML model, instead of handing over your `TensorFlowTensor` instance, you need to access its `.tensor` attribute.
+How does this affect you? Whenever you want to access the tensor data to, let's say, do operations with it or hand it to your ML model, instead of handing over your `TensorFlowTensor` instance, you need to access its `.tensor` attribute.
 
 This would look like the following:
 
@@ -319,8 +319,6 @@ class MyPodcastModel(tf.keras.Model):
         return inputs
 ```
 
-
-
 ## Coming from FastAPI
 
 Documents are Pydantic Models (with a twist), and as such they are fully compatible with FastAPI:
@@ -368,6 +366,7 @@ async with AsyncClient(app=app, base_url="http://test") as ac:
 The big advantage here is **first-class support for ML centric data**, such as {Torch, TF, ...}Tensor, Embedding, etc.
 
 This includes handy features such as validating the shape of a tensor:
+
 ```python
 from docarray import BaseDocument
 from docarray.typing import TorchTensor
@@ -401,7 +400,7 @@ Image(
 
 ## Coming from a vector database
 
-If you came across docarray as a universal vector DB client, you can best think of it as **a new kind of ORM for vector databases**.
+If you came across DocArray as a universal vector database client, you can best think of it as **a new kind of ORM for vector databases**.
 
 DocArray's job is to take multi-modal, nested and domain-specific data and to map it to a vector database,
 store it there, and thus make it searchable:
@@ -445,6 +444,7 @@ match = store.find(
 ```
 
 ## Enable logging
+
 You can see more logs by setting the log level to `DEBUG` or `INFO`:
 
 ```python
@@ -478,16 +478,19 @@ INFO - docarray - HnswDocumentIndex[SimpleDoc] has been initialized
 
 ## Install the alpha
 
-to try out the alpha you can install it via git:
+To try out the alpha you can install it via git:
+
 ```shell
 pip install "git+https://github.com/docarray/docarray@2023.01.18.alpha#egg=docarray[common,torch,image]"
 ```
-or from the latest development branch
+
+...or from the latest development branch
+
 ```shell
 pip install "git+https://github.com/docarray/docarray@feat-rewrite-v2#egg=docarray[common,torch,image]"
 ```
 
-## Further reading
+## See also
 
 - [Join our Discord server](https://discord.gg/WaMp6PVPgR)
 - [V2 announcement blog post](https://github.com/docarray/notes/blob/main/blog/01-announcement.md)
@@ -496,4 +499,3 @@ pip install "git+https://github.com/docarray/docarray@feat-rewrite-v2#egg=docarr
 - [v2 Documentation](https://docarray-v2--jina-docs.netlify.app/)
 - ["Legacy" DocArray github page](https://github.com/docarray/docarray)
 - ["Legacy" DocArray documentation](https://docarray.jina.ai/)
-