diff --git a/README.md b/README.md
index 98671eb75bf..50ee8a04389 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,7 @@
-->
+:fire: May 29, 2024: DeepLearning.ai launched a new short course [AI Agentic Design Patterns with AutoGen](https://info.deeplearning.ai/new-course-on-agents-enroll-in-ai-agentic-design-patterns-with-autogen), made in collaboration with Microsoft and Penn State University, and taught by AutoGen creators [Chi Wang](https://github.com/sonichi) and [Qingyun Wu](https://github.com/qingyun-wu).
:fire: May 24, 2024: Foundation Capital published an article on [Forbes: The Promise of Multi-Agent AI](https://www.forbes.com/sites/joannechen/2024/05/24/the-promise-of-multi-agent-ai/?sh=2c1e4f454d97) and a video [AI in the Real World Episode 2: Exploring Multi-Agent AI and AutoGen with Chi Wang](https://www.youtube.com/watch?v=RLwyXRVvlNk).
@@ -75,7 +76,7 @@ AutoGen is a framework that enables the development of LLM applications using mu
- It provides a collection of working systems with different complexities. These systems span a [wide range of applications](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat#diverse-applications-implemented-with-autogen) from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.
- AutoGen provides [enhanced LLM inference](https://microsoft.github.io/autogen/docs/Use-Cases/enhanced_inference#api-unification). It offers utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.
-AutoGen is powered by collaborative [research studies](https://microsoft.github.io/autogen/docs/Research) from Microsoft, Penn State University, and the University of Washington.
+AutoGen is created out of collaborative [research](https://microsoft.github.io/autogen/docs/Research) from Microsoft, Penn State University, and the University of Washington.
diff --git a/autogen/agentchat/contrib/gpt_assistant_agent.py b/autogen/agentchat/contrib/gpt_assistant_agent.py
index 40a28bfbcfa..0dcad27b16d 100644
--- a/autogen/agentchat/contrib/gpt_assistant_agent.py
+++ b/autogen/agentchat/contrib/gpt_assistant_agent.py
@@ -5,8 +5,6 @@
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple, Union
-import openai
-
from autogen import OpenAIWrapper
from autogen.agentchat.agent import Agent
from autogen.agentchat.assistant_agent import AssistantAgent, ConversableAgent
diff --git a/website/blog/2024-05-24-Agent/index.mdx b/website/blog/2024-05-24-Agent/index.mdx
index 3f8664420ff..520805dc693 100644
--- a/website/blog/2024-05-24-Agent/index.mdx
+++ b/website/blog/2024-05-24-Agent/index.mdx
@@ -120,7 +120,7 @@ simulations are good examples, too.
### Cost of multi-agents
-Very complex mult-agent systems with leading frontier models are expensive, but compared to having humans accomplish the same task they can be exponentially more affordable.
+Very complex multi-agent systems with leading frontier models are expensive, but compared to having humans accomplish the same task they can be exponentially more affordable.
> While not inexpensive to operate, our multi-agent powered venture analysis system at BetterFutureLabs is far more affordable and exponentially faster than human analysts performing a comparable depth of analysis.
>
diff --git a/website/docs/topics/groupchat/resuming_groupchat.ipynb b/website/docs/topics/groupchat/resuming_groupchat.ipynb
index bc56cb3cd35..c071c95aba1 100644
--- a/website/docs/topics/groupchat/resuming_groupchat.ipynb
+++ b/website/docs/topics/groupchat/resuming_groupchat.ipynb
@@ -731,8 +731,9 @@
],
"metadata": {
"front_matter": {
- "description": "Custom Speaker Selection Function",
+ "description": "Resume Group Chat",
"tags": [
+ "resume",
"orchestration",
"group chat"
]