diff --git a/README.md b/README.md
index c996ccdff0..5b0ab9c674 100644
--- a/README.md
+++ b/README.md
@@ -4,25 +4,38 @@
 [![Coverage Status](https://coveralls.io/repos/github/kubeflow/tf-operator/badge.svg?branch=master)](https://coveralls.io/github/kubeflow/tf-operator?branch=master)
 [![Go Report Card](https://goreportcard.com/badge/github.com/kubeflow/tf-operator)](https://goreportcard.com/report/github.com/kubeflow/tf-operator)
 
-## Quick Links
-
-* [Prow test dashboard](https://k8s-testgrid.appspot.com/sig-big-data)
-* [Prow jobs dashboard](https://prow.k8s.io/?repo=kubeflow%2Ftf-operator)
-* [Argo UI for E2E tests](http://testing-argo.kubeflow.org)
-
 ## Overview
 
 TFJob provides a Kubernetes custom resource that makes it easy to
 run distributed or non-distributed TensorFlow jobs on Kubernetes.
 
-Please refer to the [user guide](https://www.kubeflow.org/docs/guides/components/tftraining/) for more information.
+- For a complete reference of the custom resource definitions, please refer to the [API Definition](pkg/apis/tensorflow/v1/types.go).
+- For details on its design, please refer to the [v1alpha2 design doc](https://github.com/kubeflow/community/blob/master/proposals/tf-operator-design-v1alpha2.md).
+- For details on its obersibility, please refer to the [monitoring design doc](docs/monitoring/README.md).
+
+## Prerequisites
+
+* Version >= 1.16 of Kubernetes
 
-## Deploy
+## Installation
 
 ```bash
 kubectl apply -f ./deploy/v1/tf-operator.yaml
 ```
 
+## Quick Start
+
+Please refer to the [quick-start-v1.md](docs/quick-start-v1.md) and [Kubeflow user guide](https://www.kubeflow.org/docs/guides/components/tftraining/) for more information.
+
+## Community
+
+You can:
+
+- Join our [Slack](https://join.slack.com/t/kubeflow/shared_invite/zt-lhkwrmkh-JPT2g9eva1oPkS00~VHZDQ) channel.
+- Check out [who is using this operator](./docs/adopters.md).
+
+This is a part of Kubeflow, so please see [readme in kubeflow/kubeflow](https://github.com/kubeflow/kubeflow#get-involved) to get in touch with the community.
+
 ## Contributing
 
 Please refer to the [developer_guide](developer_guide.md)
@@ -31,10 +44,11 @@ Please refer to the [developer_guide](developer_guide.md)
 
 Please refer to [CHANGELOG](CHANGELOG.md)
 
-## Adopters
+## Version Matrix
 
-Please refer to [adopters list](./docs/adopters.md).
+The following table lists the most recent few versions of the operator.
 
-## Community
-
-This is a part of Kubeflow, so please see [readme in kubeflow/kubeflow](https://github.com/kubeflow/kubeflow#get-involved) to get in touch with the community.
+| Operator Version | API Version | Kubernetes Version |
+| ------------- | ------------- | ------------- |
+| `latest` (master HEAD) | `v1` | 1.16+ |
+| `v1.0.x`| `v1` | 1.16+ |
diff --git a/developer_guide.md b/developer_guide.md
index f64ddbe4bd..2196fa6d34 100644
--- a/developer_guide.md
+++ b/developer_guide.md
@@ -13,12 +13,10 @@ ln -sf ${GIT_TRAINING} ${go env GOPATH}/src/github.com/kubeflow/tf-operator
 
 * GIT_TRAINING should be the location where you checked out https://github.com/kubeflow/tf-operator
 
-Resolve dependencies (if you don't have dep install, check how to do it [here](https://github.com/golang/dep))
-
 Install dependencies
 
 ```sh
-dep ensure
+GO111MODULE="on" go mod vendor
 ```
 
 Build it
@@ -27,30 +25,6 @@ Build it
 go install github.com/kubeflow/tf-operator/cmd/tf-operator.v1
 ```
 
-## Building all the artifacts.
-
-[pipenv](https://pipenv.pypa.io/en/stable/) is recommended to manage local Python environment.
-You can find setup information on their website.
-
-To build the following artifacts:
-
-* Docker image for the operator
-* Helm chart for deploying it
-
-You can run
-
-```sh
-# to setup pipenv you have to step into the directory where Pipfile is located
-cd py/kubeflow/tf_operator
-pipenv install
-pipenv shell
-cd ../..
-python -m kubeflow.tf_operator.release local --registry=${REGISTRY}
-```
-
-* The docker image will be tagged into your registry
-* The helm chart will be created in **./bin**
-
 ## Running the Operator Locally
 
 Running the operator locally (as opposed to deploying it on a K8s cluster) is convenient for debugging/development.