From 628c77472f2c0b8a189a32522cade0f828cd9ae1 Mon Sep 17 00:00:00 2001
From: Tara S Pande <38411006+TaraSPande@users.noreply.github.com>
Date: Tue, 8 Oct 2024 12:03:55 -0700
Subject: [PATCH] Update index.md

---
 index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/index.md b/index.md
index 81b7331..4849d16 100644
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@@ -140,7 +140,7 @@ Large language models (LLMs) have revolutionized a wide range of domains. In par
 | Sept 16 | **LLM agents: brief history and overview** <br> Shunyu Yao, OpenAI <br> [Livestream](https://www.youtube.com/watch?v=RM6ZArd2nVc) <a href="https://rdi.berkeley.edu/llm-agents-mooc/slides/llm_agent_history.pdf">Slides</a> [Quiz 2](https://forms.gle/t9ictrAjxTrWd9tr6) | - [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://arxiv.org/abs/2207.01206) <br> - [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629)  |          
 | Sept 23 | **Agentic AI Frameworks & AutoGen** <br> Chi Wang, AutoGen-AI <br> **Building a Multimodal Knowledge Assistant** <br> Jerry Liu, LlamaIndex <br> [Livestream](https://www.youtube.com/live/OOdtmCMSOo4) <a href="https://rdi.berkeley.edu/llm-agents-mooc/slides/autogen.pdf">Chi's Slides</a> <a href="https://rdi.berkeley.edu/llm-agents-mooc/slides/MKA.pdf">Jerry's Slides</a> [Quiz 3](https://forms.gle/osuDEXRmDHJvLi1T7) | - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155) <br> - [StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows](https://arxiv.org/abs/2403.11322)  |          
 | Sept 30 | **Enterprise trends for generative AI, and key components of building successful agents/applications** <br> Burak Gokturk, Google <br> [Livestream](https://www.youtube.com/live/Sy1psHS3w3I) <a href="https://rdi.berkeley.edu/llm-agents-mooc/slides/Burak_slides.pdf">Slides</a> [Quiz 4](https://forms.gle/gcoo9HWGdF3xVAPGA) | - [Google Cloud expands grounding capabilities on Vertex AI](https://cloud.google.com/blog/products/ai-machine-learning/rag-and-grounding-on-vertex-ai?e=48754805) <br> - [The Needle In a Haystack Test: Evaluating the performance of RAG systems](https://towardsdatascience.com/the-needle-in-a-haystack-test-a94974c1ad38) <br> - [The AI detective: The Needle in a Haystack test and how Gemini 1.5 Pro solves it](https://cloud.google.com/blog/products/ai-machine-learning/the-needle-in-the-haystack-test-and-how-gemini-pro-solves-it?e=48754805) |          
-| Oct 7 | **Compound AI Systems & the DSPy Framework** <br> Omar Khattab, Databricks <br> [Livestream](https://www.youtube.com/live/JEMYuzrKLUw) | - [Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://arxiv.org/abs/2406.11695) <br> - [Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together](https://arxiv.org/abs/2407.10930)  |          
+| Oct 7 | **Compound AI Systems & the DSPy Framework** <br> Omar Khattab, Databricks <br> [Livestream](https://www.youtube.com/live/JEMYuzrKLUw) <a href="https://rdi.berkeley.edu/llm-agents-mooc/slides/dspy_lec.pdf">Slides</a> | - [Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://arxiv.org/abs/2406.11695) <br> - [Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together](https://arxiv.org/abs/2407.10930)  |          
 | Oct 14 | **Agents for Software Development** <br> Graham Neubig, Carnegie Mellon University | - [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://arxiv.org/abs/2405.15793) <br> - [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://arxiv.org/abs/2407.16741) |          
 | Oct 21 | **Agent for Workflow Applications** <br> Nicolas Chapados, ServiceNow |  |          
 | Oct 28 | **Stronger Together: Marrying Neural Networks with Traditional Symbolic Decision-Making** <br> Yuandong Tian, Meta AI (FAIR) |  |