Small edits to update blog post (#1869)

website/blog/2024-03-03-AutoGen-Update/index.mdx

@@ -7,8 +7,8 @@ tags: [news, summary, roadmap]
 ![autogen is loved](img/love.png)
 
 **TL;DR**
-- **AutoGen has received tremenduous interest and recognition.**
-- **AutoGen has many exciting new features and ongoing reserach.**
+- **AutoGen has received tremendous interest and recognition.**
+- **AutoGen has many exciting new features and ongoing research.**
 
 Five months have passed since the initial spinoff of AutoGen from [FLAML](https://github.com/microsoft/FLAML). What have we learned since then? What are the milestones achieved? What's next?
 
@@ -138,9 +138,9 @@ These tools have been used for improving the AutoGen library as well as applicat
 
 We are making rapid progress in further improving the interface to make it even easier to build agent applications. For example:
 
-- [AutoBuild](/blog/2023/11/26/Agent-AutoBuild). AutoBuild is an ongoing research to automatically create or select a group of agents for a given task and objective. If successful, it will greatly reduce the effort from users or developers when using the multi-agent technology. It also paves the way of agentic decomposition to handle complex tasks. It is available as an experimental feature and demonstrated in two modes: free-form [creation](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_basic.ipynb) and [selection](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_agent_library.ipynb) from a library.
-- [AutoGen Studio](/blog/2023/12/01/AutoGenStudio). AutoGen Studio is a no-code UI for fast experimentation with the multi-agent conversations. It lowers the barrier of entrance to the AutoGen technology. Models, agents, and workflows can all be configured without writing code. And chatting with multiple agents in a playground is immediately available after the configuration. Although only a subset of `pyautogen` features are available in this sample app, it demonstrates a promising experience. It has generated a tremenduous excitement in the community.
-- Conversation Programming+. The [AutoGen paper](https://arxiv.org/abs/2308.08155) introduced a key concept of *Conversation Programming*, which can be used to program diverse conversation patterns such as 1-1 chat, group chat, hierarchical chat, nested chat etc. While we offered dynamic group chat as an example of high-level orchestration, it made others patterns relatively less discoverable. Therefore, we have added more convenient conversation programming features which enables easier definition of other types of complex workflow, such as [finite state machine based group chat](/blog/2024/02/11/FSM-GroupChat), [sequential chats](/docs/notebooks/agentchats_sequential_chats), and [nested chats](/docs/notebooks/agentchat_nestedchat). Many users have found them useful in implementing specific patterns, which have been always possible but more obvious with the added features. I will write another blog post for a deep dive.
+- [AutoBuild](/blog/2023/11/26/Agent-AutoBuild). AutoBuild is an ongoing area of research to automatically create or select a group of agents for a given task and objective. If successful, it will greatly reduce the effort from users or developers when using the multi-agent technology. It also paves the way for agentic decomposition to handle complex tasks. It is available as an experimental feature and demonstrated in two modes: free-form [creation](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_basic.ipynb) and [selection](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_agent_library.ipynb) from a library.
+- [AutoGen Studio](/blog/2023/12/01/AutoGenStudio). AutoGen Studio is a no-code UI for fast experimentation with the multi-agent conversations. It lowers the barrier of entrance to the AutoGen technology. Models, agents, and workflows can all be configured without writing code. And chatting with multiple agents in a playground is immediately available after the configuration. Although only a subset of `pyautogen` features are available in this sample app, it demonstrates a promising experience. It has generated tremendous excitement in the community.
+- Conversation Programming+. The [AutoGen paper](https://arxiv.org/abs/2308.08155) introduced a key concept of *Conversation Programming*, which can be used to program diverse conversation patterns such as 1-1 chat, group chat, hierarchical chat, nested chat etc. While we offered dynamic group chat as an example of high-level orchestration, it made other patterns relatively less discoverable. Therefore, we have added more convenient conversation programming features which enables easier definition of other types of complex workflow, such as [finite state machine based group chat](/blog/2024/02/11/FSM-GroupChat), [sequential chats](/docs/notebooks/agentchats_sequential_chats), and [nested chats](/docs/notebooks/agentchat_nestedchat). Many users have found them useful in implementing specific patterns, which have been always possible but more obvious with the added features. I will write another blog post for a deep dive.
 
 ### Learning/Optimization/Teaching
 
@@ -154,7 +154,7 @@ This feature works for GPTAssistantAgent (using OpenAI's assistant API) and grou
 
 ### Integration
 
-The extensible design of AutoGen makes it integratable with new technologies. For example:
+The extensible design of AutoGen makes it easy to integrate with new technologies. For example:
 - [Custom models and clients](/blog/2024/01/26/Custom-Models) can be used as backends of an agent, such as Huggingface models and inference APIs.
 - [OpenAI assistant](/blog/2023/11/13/OAI-assistants) can be used as the backend of an agent (GPTAssistantAgent). It will be nice to reimplement it as a custom client to increase the compatibility with ConversableAgent.
 - [Multimodality](/blog/2023/11/06/LMM-Agent). LMM models like GPT-4V can be used to provide vision to an agent, and accomplish interesting multimodal tasks by conversing with other agents, including advanced image analysis, figure generation, and automatic iterative improvement in image generation.