fix typo and update news (#2825)

* fix typo and update news

* add link

* update link

* fix metadata

* tag
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Chi Wang 2024-05-30 20:50:41 -07:00 committed by GitHub
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@ -15,6 +15,7 @@
<img src="https://github.com/microsoft/autogen/blob/main/website/static/img/flaml.svg" width=200>
<br>
</p> -->
: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.
<p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">
<a href="#readme-top" style="text-decoration: none; color: blue; font-weight: bold;">

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@ -5,8 +5,6 @@ import time
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

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@ -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.
>

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@ -731,8 +731,9 @@
],
"metadata": {
"front_matter": {
"description": "Custom Speaker Selection Function",
"description": "Resume Group Chat",
"tags": [
"resume",
"orchestration",
"group chat"
]