fix: Update svg and simplify readme (#1297)

fix

Signed-off-by: Ce Gao <cegao@tensorchord.ai>

Signed-off-by: Ce Gao <cegao@tensorchord.ai>

README.md

@@ -29,7 +29,7 @@ Development environments are full of python and system dependencies, CUDA, BASH
 1. Develop in the isolated environment.
 
 <p align="center">
-  <img src="https://user-images.githubusercontent.com/5100735/189058399-3865a039-9459-4e74-83dd-3ee2ecadfef5.svg" width="75%"/>
+  <img src="https://user-images.githubusercontent.com/5100735/207217321-34c30dde-4b55-4871-b6fe-f9fc6ec19986.svg" width="75%"/>
 </p>
 
 ## Why use `envd`?
@@ -102,44 +102,6 @@ For example, the PyPI cache is shared across builds and thus the package will be
   <img src="https://user-images.githubusercontent.com/16186646/197944452-4a5dcd5f-68d0-4505-b419-e95c298978d7.svg#gh-dark-mode-only" width="65%"/>
 </p>
 
-🐍 **One configuration to rule them all**
-
-Development environments are full of Dockerfiles, bash scripts, Kubernetes YAML manifests, and many other clunky files that are always breaking. You just need one configuration file `build.envd`[^1], it works both for local Docker and Kubernetes clusters in the cloud.
-
-![envd](https://user-images.githubusercontent.com/5100735/188821980-dcbd9069-b504-436a-9ffd-05ac5543a6d1.png)
-
-[^1]: The build language is [starlark](https://docs.bazel.build/versions/main/skylark/language.html), which is a dialect of Python.
-
-✍️ **Don't sacrifice your developer experience**
-
-SSH is configured for the created environment. You can use vscode-remote, jupyter, pycharm or other IDEs that you love. Besides this, declare the IDE extensions you want, let `envd` take care of them.
-
-```python
-def build():
-    install.vscode_extensions([
-        "ms-python.python",
-    ])
-```
-
-☁️ **No polluted environment**
-
-Are you working on multiple projects, all of which need different versions of CUDA? `envd` helps you create isolated and clean environments.
-
-## Who should use envd?
-
-We're focused on helping data scientists and teams that develop AI/ML models. And they may suffer from:
-
-- building the development environments with Python/R/Julia, CUDA, Docker, SSH, and so on. Do you have a complicated Dockerfile or build script that sets up all your dev environments, but is always breaking?
-- Updating the environment. Do you always need to ask infrastructure engineers how to add a new Python/R/Julia package in the Dockerfile?
-- Managing environments and machines. Do you always forget which machines are used for the specific project, because you handle multiple projects concurrently?
-
----
-
-**Talk with us**
-
-💬 Interested in talking with us about your experience building or managing AI/ML applications?
-
-[**Set up a time to chat!**](https://forms.gle/9HDBHX5Y3fzuDCDAA)
 
 ## Getting Started 🚀