diff --git a/README.md b/README.md
index 4c32c8fc7fbcc..8342b30aa1b14 100644
--- a/README.md
+++ b/README.md
@@ -102,6 +102,7 @@ jina hello-world --help
 | 🚀  | [Customize Encoder](#customize-encoder) • [Test Encoder in Flow](#test-encoder-in-flow) • [Parallelism & Batching](#parallelism--batching) • [Add Data Indexer](#add-data-indexer) • [Compose Flow from YAML](#compose-flow-from-yaml) • [Search](#search) • [Evaluation](#evaluation) • [REST Interface](#rest-interface) |
 
 #### Create
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jina-ai/jupyter-notebooks/main?filepath=basic-create-flow.ipynb)
 
 Jina provides a high-level [Flow API](https://github.com/jina-ai/jina/tree/master/docs/chapters/101#flow) to simplify building search/index workflows. To create a new Flow:
 
@@ -113,6 +114,7 @@ f = Flow().add()
 This creates a simple Flow with one [Pod](https://github.com/jina-ai/jina/tree/master/docs/chapters/101#pods). You can chain multiple `.add()`s in a single Flow.
 
 #### Visualize
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jina-ai/jupyter-notebooks/main?filepath=basic-visualize-a-flow.ipynb)
 
 To visualize the Flow, simply chain it with `.plot('my-flow.svg')`. If you are using a Jupyter notebook, the Flow object will be automatically displayed inline *without* `plot`:
 
@@ -121,6 +123,7 @@ To visualize the Flow, simply chain it with `.plot('my-flow.svg')`. If you are u
 `Gateway` is the entrypoint of the Flow. 
 
 #### Feed Data
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jina-ai/jupyter-notebooks/main?filepath=basic-feed-data.ipynb)
 
 Let's create some random data and index it:
 
@@ -138,6 +141,7 @@ with Flow().add() as f:
 To use a Flow, open it using the `with` context manager, like you would a file in Python. You can call `index` and `search` with nearly all types of data. The whole data stream is asynchronous and efficient.
 
 #### Fetch Result
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jina-ai/jupyter-notebooks/main?filepath=basic-fetch-result.ipynb)
 
 Once a request is done, callback functions are fired. Jina Flow implements Promise-like interface, you can add callback functions `on_done`, `on_error`, `on_always` to hook different event. In the example below, our Flow passes the message then prints the result when success. If something wrong, it beeps. Finally, the result is written to `output.txt`.
 
@@ -154,6 +158,7 @@ with Flow().add() as f, open('output.txt', 'w') as fp:
 
 
 #### Construct Document
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jina-ai/jupyter-notebooks/main?filepath=basic-construct-document.ipynb)
 
 `Document` is [Jina's primitive data type](https://hanxiao.io/2020/11/22/Primitive-Data-Types-in-Neural-Search-System/#primitive-types). It can contain text, image, array, embedding, URI, and accompanied by rich meta information. It can be recurred both vertically and horizontally to have nested documents and matched documents. To construct a Document, one can use:
 
@@ -222,22 +227,24 @@ Interested readers can refer to [`jina-ai/example`: how to build a multimodal se
 </details>
   
 #### Add Logic
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jina-ai/jupyter-notebooks/main?filepath=basic-add-logic.ipynb)
 
 To add logic to the Flow, use the `uses` parameter to attach a Pod with an [Executor](https://github.com/jina-ai/jina/tree/master/docs/chapters/101#executors). `uses` accepts multiple value types including class name, Docker image, (inline) YAML or built-in shortcut.
 
 
 ```python
 f = (Flow().add(uses='MyBertEncoder')  # class name of a Jina Executor
-           .add(uses='docker://jinahub/pretrained-cnn:latest')  # the container name
-           .add(uses='myencoder.yaml')  # YAML serialization of a Jina Executor
+           .add(uses='docker://jinahub/pretrained-cnn:latest')  # the image name
+           .add(uses='myencoder.yml')  # YAML serialization of a Jina Executor
            .add(uses='!WaveletTransformer | {freq: 20}')  # inline YAML config
-           .add(uses='_pass'))  # built-in shortcut executor
-           .add(uses={'__cls': 'MyBertEncoder', 'with': {'param': 1.23}})  # dict config object with __cls keyword
+           .add(uses='_pass')  # built-in shortcut executor
+           .add(uses={'__cls': 'MyBertEncoder', 'with': {'param': 1.23}}))  # dict config object with __cls keyword
 ```
 
 The power of Jina lies in its decentralized architecture: each `add` creates a new Pod, and these Pods can be run as a local thread/process, a remote process, inside a Docker container, or even inside a remote Docker container.
 
 #### Inter & Intra Parallelism
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jina-ai/jupyter-notebooks/main?filepath=basic-inter-intra-parallelism.ipynb)
 
 Chaining `.add()`s creates a sequential Flow. For parallelism, use the `needs` parameter:
 
@@ -262,6 +269,7 @@ f = (Flow().add(name='p1', needs='gateway')
 <img src="https://github.com/jina-ai/jina/blob/master/.github/simple-plot4.svg?raw=true"/>
 
 #### Asynchronous Flow
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jina-ai/jupyter-notebooks/main?filepath=basic-inter-intra-parallelism.ipynb)
 
 Synchronous from outside, Jina runs asynchronously underneath: it manages the eventloop(s) for scheduling the jobs. In some scenario, user wants more control over the eventloop, then `AsyncFlow` comes to use. In the example below, Jina is part of the integration where another heavy-lifting job is running concurrently:
 
diff --git a/jina/__init__.py b/jina/__init__.py
index 1ce127b4a8fee..8de6d3c8d7502 100644
--- a/jina/__init__.py
+++ b/jina/__init__.py
@@ -161,5 +161,14 @@ def _set_nofile(nofile_atleast=4096):
 from jina.clients import Client
 from jina.clients.asyncio import AsyncClient
 
+# Executor
+from jina.executors.classifiers import BaseClassifier as Classifier
+from jina.executors.crafters import BaseCrafter as Crafter
+from jina.executors.encoders import BaseEncoder as Encoder
+from jina.executors.evaluators import BaseEvaluator as Evaluator
+from jina.executors.indexers import BaseIndexer as Indexer
+from jina.executors.rankers import BaseRanker as Ranker
+from jina.executors.segmenters import BaseSegmenter as Segmenter
+
 __all__ = [_s for _s in dir() if not _s.startswith('_')]
 __all__.extend([_s for _s in _names_with_underscore])