update community talks

website/talks/2024-11-11/index.mdx

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+---
+title: Exploring Pragmatic Patterns in Agentic Systems - Nov 04, 2024
+---
+
+### Speakers: Chia-Tung Ho
+
+### Biography of the speakers:
+
+Chia-Tung Ho is a senior research scientist at Nvidia Research. He received his Ph.D. in electrical and computer engineering from the University of California, San Diego, USA, in 2022. Chia-Tung has several years of experience in the EDA industry. Before moving to the US, he worked for IDM and EDA companies in Taiwan, developing in-house design-for-manufacturing (DFM) flows at Macronix, as well as fastSPICE solutions at Mentor Graphics and Synopsis. During his Ph.D., he collaborated with the Design Technology Co-Optimization (DTCO) team at Synopsis and served as an AI resident at X, the Moonshot Factory (formerly Google X). His recent work focuses on developing LLM agents for chip design and integrating advanced knowledge extraction, task graph solving, and reinforcement learning techniques for debugging and design optimization.
+
+### Abstract:
+
+Hardware design presents numerous challenges due to its complexity and rapidly advancing technologies. The stringent requirements for performance, power, area, and cost (PPAC) in modern complex designs, which can include up to billions of transistors, make hardware design increasingly demanding compared to earlier generations. These challenges result in longer turnaround times (TAT) for optimizing PPAC during RTL synthesis, simulation, verification, physical design, and reliability processes. In this talk, we introduce multi-AI agents built on top of Autogen to improve efficiency and reduce TAT in the chip design process. The talk explores the integration of novel distilled knowledge debugging graphs, task graph solving, and multimodal capabilities within multi-AI agents to address tasks such as timing debugging, Verilog debugging, and Design Rule Check (DRC) code generation. Based on these studies, multi-AI agents demonstrate promising improvements in performance, productivity, and efficiency in chip design.

website/talks/2024-11-12/index.mdx

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+---
+title: Exploring Pragmatic Patterns in Agentic Systems - Nov 04, 2024
+---
+
+### Speakers: Davor Runje
+
+### Biography of the speakers:
+
+Davor Runje is a seasoned software engineer, computer scientist, and serial entrepreneur with a strong background in technology and business. Most recently, he co-founded an AI startup Airt. Prior to that, he co-founded and exited from two companies. Davor is a very active member of open source community. He is a maintainer of FastStream and FastAgency and a core contributor to AutoGen. During his PhD studies, under the mentorship of Dean Rosenzweig from the University of Zagreb and Yuri Gurevich at Microsoft Research, Davor made significant contributions to programming for multiprocessor/multicore systems. He made the design, implementation, and technology transfer of a system that facilitated structured concurrency program execution, which became known as the Task Parallel Library in the .NET framework. This earned him the SSCLI and Phoenix 2005 award from Microsoft Research, recognising it as one of the top 16 international research projects. Davor is also an esteemed academic author with over 20 publications in theoretical computer science and artificial intelligence. He also holds two US patents. Between 2020 and 2024, he served as the president of the board of CISEx, the largest software industry association in Croatia, advocating for legal and tax reforms to enhance the global competitiveness of the Croatian IT sector.
+
+### Abstract:
+
+Inspired by the design and philosophy of modern Python frameworks such as FastAPI, we designed a framework that allows you to go from a working multi-agent prototype written in AutoGen to a scalable, multi-tenant application with SSO authentication hosted on the cloud in less than one hour. Depending on your needs, you can quickly build a REST-based web service running multiple workers or, in the case of an even larger scale, a distributed service built around a message broker protocol. The framework is powerful and uses complex technologies under the hood, yet it is simple to use and it requires only a few lines of code to get the desired results. FastAgency also has a simple-to-use component that allows you to render rich information in UI, giving you a better way to communicate with the end user. Last but not least, FastAgency allows you to import external REST APIs using their OpenAPI specifications and automatically build tools that can be attached to agents in just a few lines of code. In this walk, I'll walk you over the core concepts behind the framework and illustrate them with examples.

website/talks/2024-11-18/index.mdx

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+---
+title: Exploring Pragmatic Patterns in Agentic Systems - Nov 04, 2024
+---
+
+### Speakers: Yongchao Chen
+
+### Biography of the speakers:
+
+Yongchao Chen is a PhD student of Electrical Engineering at Harvard SEAS and MIT LIDS. He is currently working on Robot Planning with Foundation Models under the guidance of Prof. Chuchu Fan and Prof. Nicholas Roy at MIT and co-advised by Prof. Na Li at Harvard. He is also doing the research in AI for Physics and Materials, particularly interested in applying Robotics/Foundation Models into AI4Science. Yongchao interned at Microsoft Research in 2024 summer and has been working with MIT-IBM Watson AI Lab starting from 2023 Spring.
+
+### Abstract:
+
+State-of-the-art language models, like GPT-4o and O1, continue to face challenges in solving tasks with intricate constraints involving logic, geometry, iteration, and optimization. While it's common to query LLMs to generate a plan purely through text output, we stress the importance of integrating symbolic computing to enhance general planning capabilities. By combining LLMs with symbolic planners and solvers, or guiding LLMs to generate code for planning, we enable them to address complex decision-making tasks for both real and virtual robots. This approach extends to various applications, including task and motion planning for drones and manipulators, travel itinerary planning, website agent design, and more.

website/talks/future_talks/index.mdx

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 title: Upcoming  Talks
 ---
 
-## Integrating Foundation Models and Symbolic Computing for Next-Generation Robot Planning - Nov 18, 2024
+## Mosaia - The AI community’s platform for creating, sharing and deploying AI agents in a serverless cloud environment - Nov 28, 2024
 
-### Speakers: Yongchao Chen
+### Speakers: Aaron Wong-Ellis
 
 ### Biography of the speakers:
 
-Yongchao Chen is a PhD student of Electrical Engineering at Harvard SEAS and MIT LIDS. He is currently working on Robot Planning with Foundation Models under the guidance of Prof. Chuchu Fan and Prof. Nicholas Roy at MIT and co-advised by Prof. Na Li at Harvard. He is also doing the research in AI for Physics and Materials, particularly interested in applying Robotics/Foundation Models into AI4Science. Yongchao interned at Microsoft Research in 2024 summer and has been working with MIT-IBM Watson AI Lab starting from 2023 Spring.
+Aaron Wong-Ellis is the co-founder and CTO at Mosaia. His several years of experience in the field of AI, IOT and building enterprise platforms has equipped him with the right skill set to build Mosaia. Aaron has worked as an application architect and engineer for small startups and large Fortune 100 companies like AWS. His recent work focuses on developing a platform for creating, sharing and running LLM agents in a scalable serverless cloud infrastructure.
 
 ### Abstract:
 
-State-of-the-art language models, like GPT-4o and O1, continue to face challenges in solving tasks with intricate constraints involving logic, geometry, iteration, and optimization. While it's common to query LLMs to generate a plan purely through text output, we stress the importance of integrating symbolic computing to enhance general planning capabilities. By combining LLMs with symbolic planners and solvers, or guiding LLMs to generate code for planning, we enable them to address complex decision-making tasks for both real and virtual robots. This approach extends to various applications, including task and motion planning for drones and manipulators, travel itinerary planning, website agent design, and more.
+Running multiple AI agents reliably on the cloud, can encounter numerous challenges. At Mosaia we faced the challenges head on and created a way to do this in a scalable serverless cloud environment. Allowing people to run their agents with little to no code at all. Just write up your prompts and construct your groups of agents through a browser based UI. Being able to do this, opened up many possibilities to construct and share agents with others to use on Mosaia or run locally using Autogen. Mosaia was created as a platform to not only run agents but allow prompt engineers to host and share these agents with others. Fostering a community of collaboration and creativity around building AI agents.
+
+### Sign Up: https://discord.gg/NrNP5ZAx?event=1308232124062503012
+
+
+## Make AI Agents Collaborate: Drag, Drop, and Orchestrate with Waldiez - Dec 9, 2024
+
+### Speakers: Panagiotis Kasnesis
+
+### Biography of the speakers:
+
+Panagiotis Kasnesis holds a Ph.D degree in computer science from the Department of Electrical and Computer Engineering at NTUA. He received his diploma degree in chemical engineering and his M.Sc. in techno-economic systems from NTUA, in 2008 and 2013 respectively. His research interests include Machine/Deep learning, Multi-Agent Systems and IoT, while he has published more than 50 scientific articles in international journals/conferences in these fields. He is founder and CEO of Waldiez (https://waldiez.io/), co-founder and CTO of ThinGenious and serves as a senior researcher at University of West Attica. Moreover, he is a lecturer at the MSc program “Artificial Intelligence and Deep Learning” (https://aidl.uniwa.gr/) and is certified as University Ambassador, by NVIDIA Deep Learning Institute (DLI), in the tasks of Building Transformer-Based NLP Applications, and Rapid Application Development Using LLMs.
+
+### Abstract:
+
+Current LLM-based orchestration tools often lack support for multi-agent interactions, are restricted to basic communication patterns, or only provide information after the entire workflow has completed. Waldiez is an open-source workflow tool that lets you orchestrate your LLM-agents using drag-and-drop and develop complex agentic applications.  It is a low-code tool that assists you design and visualize your multi-agent workflow in jupyter lab as a plugin. Wadiez runs over AG2 supporting all the communication patterns (e.g., sequential, nested and group chat), supporting several LLM-based services offered by OpenAI, Anthropic, NVIDIA NIM, local hosted models and several others.  In this talk, we’ll dive into the powerful features of Wadiez, demonstrating its capabilities through real-world use cases. Join us as we explore how Wadiez can streamline complex workflows and enhance multi-agent interactions, showcasing exactly what sets it apart from other LLM-based orchestration tools.
+
+### Sign Up: https://discord.gg/NrNP5ZAx?event=1308233315442098197
 
-### Sign Up:  https://discord.gg/Swn3DmBV?event=1303162642298306681
 
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