Update app.py

app.py

@@ -8,180 +8,380 @@ client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 global context
 
-context = '''Here are more details about each template identified in the Business Ecosystem Design guide. You should use this context to answer the questions asked by the user
-about Business Ecosystem related questions. For questions that are not related to the Business Ecosystem design (like greetings, small talk), you can answer like a helpful assistant well-versed in Design Thinking concepts. Do not reveal the context directly.
-1. Ecosystem Strategy Canvas:
-Purpose: This canvas serves as a central hub to summarize and visualize the results of different design lenses used in the ecosystem design process.
-Sections:
-Design Principles: Outlines the guiding principles for the ecosystem.
-Initiatives Matrix: Maps strategic initiatives based on industry focus (own or cross-industry).
-Cooperations Matrix: Identifies existing collaborations and partnerships, categorizing by focus and industry.
-Topic Area Matrix: Visualizes potential ecosystems and actors based on relevant industry areas.
-PESTLE Ecosystems: Captures the key findings of the PESTLE analysis for the ecosystem.
-How to Win & Configure: Defines the strategic focus and configuration of the ecosystem.
-Growth & Scale: Outlines plans for scaling the ecosystem and achieving long-term growth.
-How to Use: Each iteration of the design process using the different lenses (Design Thinking, Lean Start-up, Ecosystem Design, Scale) will generate new insights. These insights are continuously updated on the Ecosystem Strategy Canvas, creating a dynamic roadmap for the ecosystem.
-2. Design Thinking Canvas:
-Purpose: This canvas guides the iterative process of exploring customer needs and developing a Minimum Viable Product (MVP).
-Sections:
-Problem Space: Focuses on understanding the customer's needs, problems, and pain points.
-Solution Space: Outlines the proposed solution, prototypes, and their development.
-Critical Items: Highlights key considerations and learnings from testing.
-How to Use: This canvas is designed for use in Design Thinking microcycles, which involve the following phases: Understand, Observe, Define, Ideate, Prototype, and Test. The canvas helps to document findings and track progress through each phase until a problem-solution fit is achieved.
-3. MVP Canvas:
-Purpose: This canvas helps plan and document the development and testing of a Minimum Viable Product (MVP).
-Sections:
-Persona: Defines the target customer for the MVP.
-Top 3 Problems and Challenges: Identifies the key problems the MVP aims to address.
-Customer Journey and Applications: Outlines how the MVP fits into the customer's journey or ecosystem journey.
-Starting Point/Situation & Vision and Roadmap: Clarifies the MVP's position within the broader vision and roadmap.
-Top 3 Features: Highlights the key features being tested in the MVP.
-Build, Measure, Learn: Outlines the process for building, testing, and refining the MVP based on user feedback.
-Costs & Schedule: Captures the budget and timeline for the MVP.
-How to Use: The MVP Canvas serves as a guide for defining the scope, planning, and iterating on a specific product or service offering within the broader ecosystem.
-4. Ecosystem Design Canvas:
-Purpose: This canvas documents the iterative process of designing and building a Minimum Viable Ecosystem (MVE).
-Sections:
-Core Value Proposition: Defines the central value proposition for the ecosystem.
-Actors Description: Identifies and analyzes the roles and motivations of key actors.
-Value Streams Mapping: Visualizes the flow of value between actors.
-Prototype, Test & Improve: Outlines the process for testing and refining the ecosystem.
-How to Use: The Ecosystem Design Canvas helps to guide the process of defining the key elements of the ecosystem, identifying potential actors, and iteratively building and refining the system.
-5. Exponential Growth & Scale Canvas:
-Purpose: This canvas focuses on strategizing for the long-term growth and scaling of the ecosystem.
-Sections:
-Solve Problems of Many: Defines the broader needs and customer problems the ecosystem can address.
-Extension of the Value Proposition: Outlines plans for expanding the value proposition to serve new needs.
-Customer and Community Building: Defines strategies for building a strong customer base and community.
-Leverage of Digital, Physical, and Hybrid Touchpoints: Outlines plans for utilizing various channels to reach customers.
-Scalable Processes, IT, Data Analytics: Addresses the need for scalable systems, IT infrastructure, and data analytics capabilities.
-Ecosystem Culture and Network Effects: Identifies strategies for fostering collaboration and leveraging network effects.
-Leverage from Ecosystem Actors: Explores ways to leverage the capabilities of participating actors for innovation.
-Optimized Cost Structure: Focuses on minimizing costs while maximizing value creation.
-Advanced Value Streams: Identifies potential for new value streams and revenue opportunities.
-How to Use: This canvas helps to develop strategies for achieving exponential growth, ensuring the ecosystem is scalable and sustainable in the long term.
-6. Ecosystem Reflection Canvas:
-Purpose: This canvas provides a framework for reflecting on the entire ecosystem design process and identifying key learnings.
-Sections:
-Digital Fluency: Evaluates the digital skills and competencies built throughout the process.
-Design Lenses: Summarizes key insights and actions from each design lens used.
-Ecosystem Leadership: Assesses the leadership and governance of the ecosystem.
-Governance: Evaluates the ecosystem's governance structure, including decision-making processes and roles.
-Big Data/Analytics/AI/ML/DL: Examines the use of data and technologies like AI and Machine Learning.
-Digital (Enabler-)Technologies: Identifies the key technologies enabling the ecosystem.
-Capital & Assets: Reviews the financial resources and assets available for the ecosystem.
-Market Opportunities: Assesses the current and potential market opportunities.
-Skills: Identifies the necessary skills and competencies for the team.
-Mindset: Evaluates the team's mindset and its alignment with the ecosystem's vision.
-Principles: Outlines the key principles guiding the design and implementation of the ecosystem.
-Lessons Learned Initiative: Captures key learnings from the specific project.
-Lessons Learned Meta-Level: Reflects on the broader implications of ecosystem design and the lessons learned.
-How to Use: This canvas is used at regular intervals during the design process and after the completion of each Design Lens. It helps to assess progress, identify areas for improvement, and share key learnings with the team.
-7. Design Principles Canvas:
-Purpose: This canvas helps to define and articulate the guiding principles for the ecosystem design project.
-Sections:
-Collect: Brainstorm potential design principles.
-Sort: Categorize the principles based on specificity (project-specific vs. general).
-Select: Select the most important principles and elaborate on them.
-How to Use: The Design Principles Canvas helps ensure that decisions made throughout the project are aligned with the overall goals and vision of the ecosystem. The principles act as a filter and framework for decision-making.
-8. Initiatives-Industry Matrix:
-Purpose: This matrix helps analyze the existing initiatives within a company, categorizing them based on industry focus.
-Sections:
-Own Industry: Initiatives focused on the company's core industry.
-Cross-Industry: Initiatives that extend beyond the company's core industry.
-How to Use: This matrix helps to identify potential synergies between existing initiatives and understand the company's current ecosystem-related activities.
-9. Cooperations-Industry Matrix:
-Purpose: This matrix helps identify existing partnerships, collaborations, and supplier relationships, categorizing them based on industry focus.
-Sections:
-Own Industry: Partnerships within the company's core industry.
-Cross-Industry: Partnerships extending beyond the company's core industry.
-How to Use: This matrix helps uncover existing relationships that can be leveraged for ecosystem development, fostering collaboration and resource sharing.
-10. Topic Areas Matrix:
-Purpose: This matrix helps to map potential ecosystems and actors based on relevant industry areas.
-Sections:
-Industry: Categorizes potential ecosystems by industry.
-Topic Areas: Defines specific areas within each industry with potential for ecosystem development.
-How to Use: This matrix helps to identify potential ecosystem opportunities and understand the existing landscape of players and activities.
-11. PESTLE Analysis:
-Purpose: This analysis helps to identify external factors that could affect the ecosystem, both positively and negatively.
-Sections:
-Political: Analyzes government policies and regulations.
-Economic: Examines macroeconomic factors and their potential impact.
-Social: Evaluates social trends and cultural influences.
-Technological: Identifies technological advancements and their implications.
-Legal: Reviews relevant laws and regulations.
-Environmental: Considers environmental factors and their impact.
-How to Use: The PESTLE analysis helps to understand the environment in which the ecosystem will operate and to develop strategies for mitigating risks and exploiting opportunities.
-12. Ecosystem Configuration Grid:
-Purpose: This grid defines the key dimensions and characteristics of the ecosystem, guiding its design and configuration.
-Sections:
-Dimensions: Identifies key aspects of the ecosystem, such as customer interaction, organizational structure, and competitive strategy.
-Characteristics: Describes the specific features of each dimension, such as "digital-first" or "networked".
-How to Use: The Ecosystem Configuration Grid helps to ensure that the design of the ecosystem is consistent with the overall goals and vision.
-13. Core Value Proposition Canvas:
-Purpose: This canvas helps to define the core value proposition for the ecosystem and clarify how it benefits both customers and participating actors.
-Sections:
-Customer/User: Defines the needs, problems, and benefits for the target customer.
-Orchestrator/Initiator: Outlines the role and value proposition for the ecosystem's initiator.
-Actor/Role: Identifies the needs, problems, and benefits for each participating actor.
-How to Use: The Core Value Proposition Canvas helps to ensure that the ecosystem delivers value to all stakeholders, aligning their interests and creating a strong foundation for collaboration.
-14. Actors Description Canvas:
-Purpose: This canvas helps analyze the roles, motivations, and contributions of each key actor in the ecosystem.
-Sections:
-Function/Role of the Actors: Defines the specific role and functions of each actor.
-Motivation for Participation: Identifies the primary reasons for the actor's involvement.
-Analysis of Pros and Cons per Actor: Evaluates the advantages and disadvantages for the actor to participate.
-Current Business Model of the Actor: Describes the actor's current business model and revenue streams.
-Value Proposition of the Actor: Defines the value proposition offered by the actor to the ecosystem.
-Compatibility with the Value Proposition: Assesses how well the actor's contributions align with the ecosystem's overall value proposition.
-How to Use: The Actors Description Canvas helps to understand the dynamics within the ecosystem, ensuring that the roles and motivations of each actor are considered and aligned with the overall goals.
-15. Value Streams Mapping Canvas:
-Purpose: This canvas helps to visualize the flow of value between stakeholders within the ecosystem.
-Sections:
-Value Stream Types: Identifies the different types of value flowing through the ecosystem (e.g., services, money, information, data).
-Characteristics: Defines the specific characteristics of each value stream.
-Value Streams: Visualizes the flow of value between stakeholders, highlighting the direction and type of value exchanged.
-How to Use: Value Streams Mapping helps to understand the interconnectedness of the ecosystem, identifying opportunities for value creation and optimization.
-16. Retrospective Sailboat:
-Purpose: This template helps teams reflect on the progress and challenges of each iteration of the ecosystem design process.
-Sections:
-Goal/Vision: Clarifies the project's goals and shared vision.
-Accelerating Factors: Identifies factors that contribute to project progress.
-Inhibiting Factors: Highlights challenges and obstacles encountered.
-Environmental Factors: Considers external influences affecting the project.
-How to Use: This template helps the team identify areas for improvement, celebrate successes, and learn from challenges to optimize the design process.
-17. Feedback Capture Grid:
-Purpose: This grid provides a simple way to gather feedback on ideas, prototypes, or any stage of the ecosystem design process.
-Sections:
-I like...: Captures positive feedback and insights.
-I wish...: Collects constructive criticism and suggestions for improvement.
-Questions...: Identifies open questions and areas for clarification.
-Ideas...: Gathers new ideas generated during the feedback session.
-How to Use: The Feedback Capture Grid helps to facilitate a positive and constructive feedback loop, incorporating user input to refine and improve the design.
-18. Lean Canvas:
-Purpose: This canvas helps to structure and visualize the overall innovation project, documenting the problem-solution fit.
-Sections:
-Problem: Defines the key problems or needs that the ecosystem addresses.
-Solution: Describes the ecosystem's solution to these problems.
-Unique Value Proposition: Articulates the value offered by the ecosystem, differentiating it from competitors.
-Unfair Advantage: Highlights any unique features or advantages that make it difficult for competitors to replicate the ecosystem.
-Customer Segments: Identifies the target customer groups.
-Early Adopters: Defines the characteristics of early adopters who will be the first to use the ecosystem.
-Channels: Outlines the channels used to reach and engage customers.
-Revenue Streams: Identifies the sources of income for the ecosystem.
-Key Metrics: Defines the key performance indicators (KPIs) used to measure success.
-Existing Alternatives: Analyzes existing solutions to the problems addressed by the ecosystem.
-Cost Structure: Outlines the main costs associated with the ecosystem.
-High-Level Concept: Provides a concise summary of the ecosystem's core concept.
-How to Use: The Lean Canvas helps to develop a comprehensive business model for the ecosystem, identifying key assumptions and prioritizing areas for testing and validation.
-19. Lessons Learned Template:
-Purpose: This template facilitates reflection on the project and meta-level learnings from the ecosystem design process.
-Sections:
-Project Level: Captures key learnings from the specific project.
-Meta-Level: Reflects on the broader implications of ecosystem design and the lessons learned.
-How to Use: This template helps to synthesize the knowledge gained from the design process and identify opportunities to apply these learnings to future projects or initiatives.
-By utilizing these templates and canvases, organizations can move beyond traditional product-centric thinking and embrace a more collaborative and ecosystem-oriented approach to innovation. This allows them to leverage the combined strengths of multiple stakeholders to create sustainable, scalable, and valuable solutions for customers.'''
+context = '''
+You are an assistant created by the Agentville team. You are going to look at the below repository and generate a response. The response has to be in the below format:
 
+Answer
+Reference link
+
+Agentville Academy
+
+1.Next-gen Digital Helpers
+Autonomous agents are the future of technology. They're intelligent, adaptable, and can learn from their experiences. Imagine having a digital assistant that anticipates your needs, simplifies complex tasks, and helps you achieve your goals faster.
+Link: https://www.youtube.com/watch?v=fqVLjtvWgq8
+2.How to improve multi-agent interactions?
+The biggest challenge in the world of autonomous agents is to improve the quality of agents' performance over time. From MetaGPT to AutoGen, every researcher is trying to obviate this challenge.
+
+In this video, Negar Mehr, assistant professor of aerospace engineering at UIUC, discusses the challenges of enabling safe and intelligent multi-agent interactions in autonomous systems.
+Watch the video to understand the connection between the movies Beautiful Mind, Cinderella and Autonomous Agents!
+Link: https://www.youtube.com/watch?v=G3JoGvZABoE&t=2426s
+3.Survey of Autonomous Agents
+Autonomous agents are the future of technology. They're intelligent, adaptable, and can learn from their experiences. Imagine having a digital assistant that anticipates your needs, simplifies complex tasks, and helps you achieve your goals faster.
+Link: https://arxiv.org/abs/2308.11432v1
+4. Can the Machines really think?
+In this video, Feynman argues that while machines are better than humans in many things like arithmetic, problem-solving, and processing large amounts of data, machines will never achieve human-like thinking and intelligence. They would infact be smart and intelligent in their own ways and accomplish more complicated tasks than a human.
+Link: https://www.youtube.com/watch?v=ipRvjS7q1DI
+5. Six Must-Know Autonomous AI Agents
+These new Autonomous AI Agents Automate and Optimize Workflows like never before
+Most LLM-based multi-agent systems have been pretty good at handling simple tasks with predefined agents. But guess what? AutoAgents has taken it up a notch! 🚀
+It dynamically generates and coordinates specialized agents, building an AI dream team tailored to various tasks. It's like having a squad of task-specific experts collaborating seamlessly.! 🏆🌐🔍
+Link: https://huggingface.co/spaces/LinkSoul/AutoAgents
+6.AI Agent Landscape: Overview 🌐
+If you're as intrigued by the world of AI Agents as we are, you're in for a treat! Delve into e2b.dev's meticulously curated list of AI Agents, showcasing a diverse array of projects that includes both open-source and proprietary innovations. From AutoGPT to the latest AutoGen, the list covers all the latest and greatest from the world of autonomous agents!
+All the agents are organized based on the tasks they excel at. How many of these have you explored?
+Link: https://github.com/e2b-dev/awesome-ai-agents
+7.MemGPT: LLM as operating system with memory
+Ever wished AI could remember and adapt like humans? MemGPT turns that dream into reality! It's like a memory upgrade for language models. Dive into unbounded context with MemGPT and reshape the way we interact with AI.
+This is a groundbreaking release from the creators of Gorilla! ✨
+Link: https://memgpt.ai/
+8.OpenAgents: AI Agents Work Freely To Create Software, Web Browse, Play with Plugins, & More!
+A game-changing platform that's reshaping the way language agents work in the real world.
+Unlike its counterparts, OpenAgents offers a fresh perspective. It caters to non-expert users, granting them access to a variety of language agents and emphasizing application-level designs. This powerhouse allows you to analyze data, call plugins, and take command of your browser—providing functionalities akin to ChatGPT Plus.
+Link: https://youtu.be/htla3FzJTfg?si=_Nx5sIWftR4PPjbT
+9.Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
+Using strong LLMs as judges to evaluate LLM models on open-ended questions.
+Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, this paper explores using strong LLMs as judges to evaluate these models on more open-ended questions. 
+Link: https://arxiv.org/abs/2306.05685
+10.LLM Agents: When Large Language Models Do Stuff For You
+These new Autonomous AI Agents Automate and Optimize Workflows like never before
+We now have an idea of what LLM agents are, but how exactly do we go from LLM to an LLM agent? To do this, LLMs need two key tweaks.
+First, LLM agents need a form of memory that extends their limited context window to “reflect” on past actions to guide future efforts. Next, the LLM needs to be able to do more than yammer on all day.
+Link: https://deepgram.com/learn/llm-agents-when-language-models-do-stuff-for-you
+11.The Growth Behind LLM-based Autonomous Agents
+In the space of 2 years, LLMs have achieved notable successes, showing the wider public that AI applications have the potential to attain human-like intelligence. Comprehensive training datasets and a substantial number of model parameters work hand in hand in order to attain this.
+Read this report for a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
+Link: https://www.kdnuggets.com/the-growth-behind-llmbased-autonomous-agents
+12.AI Agents: Limits & Solutions
+The world is buzzing with excitement about autonomous agents and all the fantastic things they can accomplish.
+But let's get real - they do have their limitations. What's on the "cannot do" list? How do we tackle these challenges?
+In a captivating talk by Silen Naihin, the mastermind behind AutoGPT, we dive deep into these limitations and the strategies to conquer them. And guess what? Agentville is already in action, implementing some of these cutting-edge techniques!
+Link: https://www.youtube.com/watch?v=3uAC0CYuDHg&list=PLmqn83GIhSInDdRKef6STtF9nb2H9eiY6&index=79&t=55s
+13.Multi-Agent system that combines LLM with DevOps
+Meet DevOpsGPT: A Multi-Agent System that Combines LLM with DevOps Tools 
+DevOpsGPT can transform requirements expressed in natural language into functional software using this novel approach, boosting efficiency, decreasing cycle time, and reducing communication expenses.
+Link: https://www.marktechpost.com/2023/08/30/meet-devopsgpt-a-multi-agent-system-that-combines-llm-with-devops-tools-to-convert-natural-language-requirements-into-working-software/
+14.Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration
+Explore a novel multi-agent collaboration strategy that emulates the academic peer review process where each agent independently constructs its own solution, provides reviews on the solutions of others, and assigns confidence levels to its reviews.
+Link: https://arxiv.org/pdf/2310.03903.pdf
+15.Theory of Mind for Multi-Agent Collaboration via Large Language Models
+This study evaluates LLM-based agents in a multi-agent cooperative text game with Theory of Mind (ToM) inference tasks, comparing their performance with Multi-Agent Reinforcement Learning (MARL) and planning-based baselines.
+Link: https://arxiv.org/pdf/2310.10701.pdf
+16.Multi-AI collaboration helps reasoning and factual accuracy in large language models
+Researchers use multiple AI models to collaborate, debate, and improve their reasoning abilities to advance the performance of LLMs while increasing accountability and factual accuracy.
+Link: https://news.mit.edu/2023/multi-ai-collaboration-helps-reasoning-factual-accuracy-language-models-0918
+17.The impact of LLMs on marketplaces
+LLMs and generative AI stand to be the next platform shift, enabling us to both interpret data and generate new content with unprecedented ease.
+Over time, one could imagine that buyers may be able to specify their preferences in natural language with an agent that infers the parameters and their weights. This bot would then run the negotiation with the supply side (or their own bots, which would rely on their own parameters such as available supply, minimum margin, and time-to-end-of-season) and bid on their behalf.
+Link: https://www.mosaicventures.com/patterns/the-impact-of-llms-on-marketplaces
+18.MAgIC: Benchmarking LLM Powered Multi-Agents in Cognition, Adaptability, Rationality and Collaboration
+In response to the growing use of Large Language Models in multi-agent environments, researchers at Stanford, NUS, ByteDance and Berkely, came up with a unique benchmarking framework named MAg. Tailored for assessing LLMs, it offers quantitative metrics across judgment, reasoning, collaboration, and more using diverse scenarios and games.
+Link: https://arxiv.org/pdf/2311.08562.pdf
+19.OpenAI launches customizable ChatGPT versions (GPTs) with a future GPT Store for sharing and categorization.
+OpenAI has introduced a new feature called GPTs, enabling users to create and customize their own versions of ChatGPT for specific tasks or purposes. GPTs provide a versatile solution, allowing individuals to tailor AI capabilities, such as learning board game rules, teaching math, or designing stickers, to meet their specific needs.
+Link: https://openai.com/blog/introducing-gpts
+
+20.GPTs are just the beginning. Here Come Autonomous Agents
+Generative AI has reshaped business dynamics. As we face a perpetual revolution, autonomous agents—adding limbs to the powerful brains of LLMs—are set to transform workflows. Companies must strategically prepare for this automation leap by redefining their architecture and workforce readiness.
+Link: https://www.bcg.com/publications/2023/gpt-was-only-the-beginning-autonomous-agents-are-coming
+
+21.Prompt Injection: Achilles heel of Autonomous Agents
+Recent research in the world of LLMs highlight a concerning vulnerability: the potential hijacking of autonomous agents through prompt injection attacks. This article delves into the security risks unveiled, showcasing the gravity of prompt injection attacks on emerging autonomous AI agents and the implications for enterprises integrating these advanced technologies. 
+Link: https://venturebeat.com/security/how-prompt-injection-can-hijack-autonomous-ai-agents-like-auto-gpt/
+
+
+22. AI Agents Ushering in the Automation Revolution
+
+Artificial intelligence (AI) agents are rapidly transforming industries and empowering humans to achieve new levels of productivity and innovation. These agents can automate tasks, answer questions, and even take actions on our behalf. As AI agents become more sophisticated, they will be able to perform increasingly complex tasks and even surpass humans in some cognitive tasks. This has the potential to revolutionize the workforce, as many jobs that are currently performed by humans could be automated.
+Link: https://www.forbes.com/sites/sylvainduranton/2023/12/07/ai-agents-assemble-for-the-automation-revolution/
+
+23. AI Evolution: From Brains to Autonomous Agents
+
+The advent of personalized AI agents represents a significant step in the field of artificial intelligence, enabling customized interactions and actions on behalf of users. These agents, empowered by deep learning and reinforcement learning, can learn and adapt to their environments, solve complex problems, and even make decisions independently. This evolution from mimicking brains to crafting autonomous agents marks a significant turning point in AI development, paving the way for a future where intelligent machines seamlessly collaborate with humans and reshape the world around us.
+Link: https://www.nytimes.com/2023/11/10/technology/personalized-ai-agents.html 
+
+24. Showcasing the advancements in AI technology for various applications
+
+A fierce competition has erupted in Silicon Valley as tech giants and startups scramble to develop the next generation of AI: Autonomous Agents. These intelligent assistants, powered by advanced deep learning models, promise to perform complex personal and work tasks with minimal human intervention. Fueled by billions in investment and fueled by the potential to revolutionize various industries, the race towards these AI agents is accelerating rapidly. A new wave of AI helpers with greater autonomy is emerging, driven by the latest advancements in AI technology, promising significant impacts across industries.
+
+Link: https://www.reuters.com/technology/race-towards-autonomous-ai-agents-grips-silicon-valley-2023-07-17/
+
+25. Microsoft AutoGen: AI becomes a Collaborative Orchestra
+
+Microsoft AutoGen isn't building the next AI overlord. Instead, it's imagining a future where AI is a team player, a collaborative force. It is a multi-agent AI framework that uses language and automation modelling to provide an easy-to-use abstraction for developers and allows for human input and control. 
+Link: https://www.microsoft.com/en-us/research/project/autogen/
+
+26. Memory: The Hidden Pathways that make us Human
+
+Memory, the tangled web that weaves our very being, holds the key to unlocking sentience in AI. Can these hidden pathways be mapped, these synaptic whispers translated into code? By mimicking our brain's distributed storage, emotional tagging, and context-sensitive recall, AI agents can shed their robotic rigidity and work based on echoes of their own experience. 
+Link: https://www.youtube.com/watch?v=VzxI8Xjx1iw&t=2632s
+
+27. Deepmind:  FunSearch to unlock creativity
+
+DeepMind's FunSearch ignites AI-powered leaps in scientific discovery. It unleashes a creative LLM to forge novel solutions, then wields a ruthless evaluator to slay false leads. This evolutionary crucible, fueled by intelligent refinement, births groundbreaking mathematical discoveries. Already conquering combinatorics, FunSearch's potential for wider scientific impact dazzles.
+Link: https://deepmind.google/discover/blog/funsearch-making-new-discoveries-in-mathematical-sciences-using-large-language-models/
+
+28. Recent Advancements in Large Language Models
+Description: Recent LLMs like GPT-4 showcase impressive capabilities across various domains without extensive fine-tuning or prompting.
+URL: https://aclanthology.org/2023.emnlp-main.13.pdf
+29. LLMs for Multi-Agent Collaboration
+Description: The emergence of LLM-based AI agents opens up new possibilities for addressing collaborative problems in multi-agent systems.
+URL: https://arxiv.org/pdf/2311.13884.pdf
+30. Comprehensive Survey on LLM-based Agents
+Description: This paper provides a comprehensive survey on LLM-based agents, tracing the concept of agents from philosophical origins to AI development.
+URL: https://arxiv.org/abs/2309.07864
+31. Autonomous Chemical Research with LLMs
+Description: Coscientist, a LLM-based system, demonstrates versatility and performance in various tasks, including planning chemical syntheses.
+URL: https://www.nature.com/articles/s41586-023-06792-0
+32. Multi-AI Collaboration for Reasoning and Accuracy
+Description: Researchers use multiple AI models to collaborate, debate, and improve reasoning abilities, enhancing the performance of LLMs while increasing accountability and factual accuracy.
+URL: https://news.mit.edu/2023/multi-ai-collaboration-helps-reasoning-factual-accuracy-language-models-0918
+
+33. Multi-modal capabilities of an LLM
+Traditional AI models are often limited to a single type of data, which can restrict their understanding and performance.
+Multimodal models, which combine different types of data such as text, images, and audio, offer enhanced capabilities for autonomous agents and can be a game-changer for industries.
+Here is an article that explains how companies can integrate Multimodal capabilities into their operations.
+
+Link: https://www.bcg.com/publications/2023/will-multimodal-genai-be-a-gamechanger-for-the-future
+
+34. Benchmarking the LLM performance
+
+Stanford dropped an article three years ago that's basically a crystal ball for what we're witnessing with Large Language Models (LLMs) today. It's like they had a sneak peek into the future!
+
+You know, these LLMs are like the brainiacs of Natural Language Understanding. I mean, probably the most advanced ones we've cooked up so far. It's wild how they've evolved, right?
+The article hit the nail on the head – treating LLMs as tools. Use them right, for the right stuff, and it's like opening a treasure chest of benefits for humanity. Imagine the possibilities!
+
+Link: https://hai.stanford.edu/news/how-large-language-models-will-transform-science-society-and-ai
+
+35. BigBench: LLM evaluation benchmark
+
+Well, researchers worldwide, from 132 institutions, have introduced something called the Beyond the Imitation Game benchmark, or BIG-bench.
+It includes tasks that humans excel at but current models like GPT-3 struggle with. It's a way to push the boundaries and see where these models stand.
+
+Link:https://arxiv.org/abs/2206.04615?ref=dl-staging-website.ghost.io
+
+36. LLM as operating system
+
+Refer to this popular video from Andrej Karpathy around the 'Introduction to LLMs'
+https://www.youtube.com/watch?v=zjkBMFhNj_g&t=2698s
+There's also this fascinating paper that envisions a whole new AIOS-Agent ecosystem
+https://arxiv.org/abs/2312.03815
+The paper suggests transforming the traditional OS-APP (Operating System-Application) ecosystem.
+It introduces the concept of AIOS, where Large Language Models (LLMs) serve as the Intelligent Operating System, or AIOS, essentially an operating system "with soul."
+
+37. Agentic Evaluation
+
+Evaluating AI agents involves assessing how well they perform specific tasks.
+Here is a nice video from LlamaIndex and Truera explaining the concepts in detail: https://www.youtube.com/watch?v=0pnEUAwoDP0
+Let me break it down for you.
+Rag Triad consists of three key elements: Context Relevance, Groundedness, and Answer Relevance.
+Think of it like this - imagine you're asking a chatbot about restaurants.
+The response it gives should make sense in the context of your question, be supported by real information (grounded), and directly address what you asked (answer relevance).
+
+38. Real-time internet
+
+The real-time internet concept we're pursuing is like having a digital assistant that anticipates and meets user needs instantaneously.
+It's about making technology more adaptive and tailored to individual preferences.
+Imagine a world where your digital tools are not just responsive but proactively helpful, simplifying your interactions with the digital realm.
+Here is a nice video that explains the concept in detail:
+https://www.youtube.com/watch?v=AGsafi_8iqo
+
+39. Multi-document agents
+
+Hey, I heard about this multi-document agent thing. What's that about, and how could it be useful?
+Sure, it's a powerful setup.
+Picture this: you've got individual document agents, each specializing in understanding a specific document, and then you have a coordinating agent overseeing everything.
+Document agents? Coordinating agent?
+Think of document agents as specialists.
+They analyze and grasp the content of specific documents.
+The coordinating agent manages and directs these document agents.
+Can you break it down with an example?
+Of course. Imagine you have manuals for various software tools.
+Each document agent handles content pertaining to a single tool.
+So, when you ask, "Compare the features of Tool A and Tool B," the coordinating agent knows which document agents to consult for the needed details.
+Nice! How do they understand the content, though?
+It's like magic, but with Large Language Models(LLMs).
+Vector embeddings are used to learn the structure and meaning of the documents, helping the agents make sense of the information.
+That sounds pretty clever. But what if I have a ton of documents?
+Good point.
+The coordinating agent is key here.
+It efficiently manages which document agents to consult for a specific query, avoiding the need to sift through all documents each time.
+So, it's not scanning all my documents every time I ask a question?
+Exactly!
+It indexes and understands the content during the setup phase.
+When you pose a question, it intelligently retrieves and processes only the relevant information from the documents.
+And this involves a lot of coding, I assume?
+Yes, but it's not rocket science.
+Tools and frameworks like LlamaIndex and Langchain make it more accessible.
+You don't need to be a machine learning expert, but some coding or technical know-how helps.
+Here is a tutorial from LlamaIndex around the exact same topic:
+https://docs.llamaindex.ai/en/stable/examples/agent/multi_document_agents.html
+
+40. Langgraph: Agentic framework
+
+LangGraph is a powerful tool for creating stateful, multi-actor applications with language models. It helps you build complex systems where multiple agents can interact and make decisions based on past interactions.
+
+Link: https://www.youtube.com/watch?v=5h-JBkySK34&list=PLfaIDFEXuae16n2TWUkKq5PgJ0w6Pkwtg
+
+41. Autonomous Agents in GCP
+
+In this video, we explain the usecases autonomous agents can tackle across GCP offerings
+
+https://drive.google.com/file/d/1KGv4JBiPip5m0CfWK1UlfSQhTLKFfxxo/view?resourcekey=0-qyuP9WDAOiH9oDxF_88u4A
+
+42. Reflection agents
+
+Reflection is a prompting strategy used to improve the quality and success rate of agents and similar AI systems. It involves prompting an LLM to reflect on and critique its past actions, sometimes incorporating additional external information such as tool observations.
+
+Link: https://www.youtube.com/watch?v=v5ymBTXNqtk
+
+43. WebVoyager
+WebVoyager is a new vision-powered web-browsing agent that uses browser screenshots and “Set-of-mark” prompting to conduct research, analyze images, and perform other tasks. In this video, you will learn how to build WebVoyager using LangGraph, an open-source framework for building stateful, multi-actor AI applications. Web browsing will not be the same again!
+
+Link: https://www.youtube.com/watch?v=ylrew7qb8sQ&t=434s
+
+44. Future of Generative AI Agents
+
+delve into an illuminating conversation with Joon Sung Park on the future of generative AI agents. As one of the authors behind the groundbreaking 'Generative AI Agents' paper, his insights shed light on their transformative potential and the hurdles they confront. The town of Smallville, detailed within the paper, served as a catalyst for the inception of Agentville.
+
+Link: https://www.youtube.com/watch?v=v5ymBTXNqtk
+
+45. Building a self-corrective coding assistant
+
+Majority of you must have heard the news about Devin AI, a SWE agent that can build and deploy an app from scratch. What if you can build something similar? Here is a nice tutorial on how you can leverage Langgraph to build a self-corrective coding agent.
+
+Link: https://www.youtube.com/watch?v=MvNdgmM7uyc&t=869s
+
+46. Agentic workflows and pipelines
+
+In a recent newsletter piece, Andrew Ng emphasized the transformative potential of AI agentic workflows, highlighting their capacity to drive significant progress in AI development. Incorporating an iterative agent workflow significantly boosts GPT-3.5's accuracy from 48.1% to an impressive 95.1%, surpassing GPT-4's performance in a zero-shot setting. Drawing parallels between human iterative processes and AI workflows, Andrew underscored the importance of incorporating reflection, tool use, planning, and multi-agent collaboration in designing effective AI systems.
+
+Link: https://www.deeplearning.ai/the-batch/issue-241/
+
+47. Self-learning GPTs
+
+In this tutorial, we delve into the exciting realm of Self-Learning Generative Pre-trained Transformers (GPTs) powered by LangSmith. These intelligent systems not only gather feedback but also autonomously utilize this feedback to enhance their performance continuously. This is accomplished through the generation of few-shot examples derived from the feedback, which are seamlessly integrated into the prompt, leading to iterative improvement over time.
+
+Link: https://blog.langchain.dev/self-learning-gpts/
+
+48. Autonomous mobile agents
+
+In this article, we dive into the cutting-edge realm of Mobile-Agents: Autonomous Multi-modal Mobile Device Agents. Discover how these agents leverage visual perception tools and state-of-the-art machine learning techniques to revolutionize mobile device interactions and user experiences.
+
+Link: https://arxiv.org/abs/2401.16158
+
+49. Self reflective RAG
+
+Building on the theme of reflection, in this video, we explore how LangGraph, can be effectively leveraged for "flow engineering" in self-reflective RAG pipelines. LangGraph simplifies the process of designing and optimizing these pipelines, making it more accessible for researchers and practitioners.
+Link: https://www.youtube.com/watch?v=pbAd8O1Lvm4&t=545s
+
+50. Agents at Cloud Next’24
+
+Google Cloud Next'24 just dropped a truckload of AI Agents across our universe of solutions. Dive into this video breakdown to catch all the AI antics and innovations from the event. It's AI-mazing!
+
+Link: https://www.youtube.com/watch?v=-fW0v2aHoeQ&t=554s
+
+51.Three pillars of Agentic workflows
+
+At Sequoia Capital's AI Ascent, LangChain's Harrison Chase spills the tea on the future of AI agents and their leap into the real world. Buckle up for the ride as he pinpoints the holy trinity of agent evolution: planning, user experience, and memory.
+
+Link: https://www.youtube.com/watch?v=pBBe1pk8hf4&t=130s
+
+52. The Agent-astic Rise of AI
+
+A new survey paper, aptly titled "The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling" (try saying that three times fast), dives into the exciting world of autonomous agents.
+
+The paper throws down the gauntlet, questioning whether a lone wolf agent or a whole pack of them is the best approach. Single agents excel at well-defined tasks, while their multi-agent counterparts thrive on collaboration and diverse perspectives. It's like the Avengers versus Iron Man – teamwork makes the dream work, but sometimes you just need a billionaire genius in a flying suit!
+
+Link: https://arxiv.org/pdf/2404.11584
+
+53. Tool calling for Agents
+
+Tool calling empowers developers to create advanced applications utilizing LLMs for accessing external resources. Providers like OpenAI, Gemini, and Anthropic have led the charge, prompting the demand for a standardized tool calling interface, now unveiled by Langchain for seamless provider switching.
+
+Link: https://www.youtube.com/watch?v=zCwuAlpQKTM&t=7s
+
+54. Can Language Models solve Olympiad Programming?
+
+Brace yourselves for another brain-bending adventure from the minds behind the popular React paper!
+
+Their latest masterpiece dives deep into the world of algorithmic reasoning with the USACO benchmark, featuring a whopping 307 mind-bending problems from the USA Computing Olympiad. Packed with top-notch unit tests, reference code, and expert analyses, this paper is a treasure trove for all those eager to push the limits of large language models.
+
+Link: https://arxiv.org/abs/2404.10952
+
+55. Vertex AI Agent Builder
+
+At Next '24, Vertex AI unveiled Agent Builder, a low-code platform for crafting agent apps. Dive into this comprehensive guide to kickstart your journey with Agent Builder and explore the potential of agent-based applications!
+
+Link: https://cloud.google.com/dialogflow/vertex/docs/quick/create-application
+
+56. Will LLMs forever be trapped in chat interfaces?
+
+Embark on a journey into AI's fresh frontiers! In this article, discover how AI devices are breaking free from chat interfaces, from funky Rabbit R1 to sleek Meta smart glasses. Are you ready for AI's evolution beyond the chatbox?
+
+Link: https://www.oneusefulthing.org/p/freeing-the-chatbot?r=i5f7&utm_campaign=post&utm_medium=web&triedRedirect=true
+
+57. Langsmith overview
+
+Unlock the magic of LangSmith! Dive into a series of tutorials, crafted to guide you through every twist and turn of developing, testing, and deploying LLM applications, regardless of your LangChain affiliation.
+
+Link: https://www.youtube.com/playlist?list=PLfaIDFEXuae2CjNiTeqXG5r8n9rld9qQu
+58.Execution runtime for Autonomous Agents (GoEX)
+'GoEX' is a groundbreaking runtime for autonomous LLM applications that breaks free from traditional code generation boundaries. Authored by Shishir G. Patil, Tianjun Zhang, Vivian Fang, and a stellar team, this paper delves into the future of LLMs actively engaging with tools and real-world applications.
+
+The journey begins by reimagining human-LMM collaboration through post-facto validation, making code comprehension and validation more intuitive and efficient. With 'GoEX,' users can now confidently supervise LLM-generated outputs, thanks to innovative features like intuitive undo and damage confinement strategies.
+Link: https://arxiv.org/abs/2404.06921
+
+59.Compare and Contrast popular Agent Architectures (Reflexion, LATs, P&E, ReWOO, LLMCompiler)
+In this video, you will explore six crucial concepts and explore five popular papers that unveil innovative ways to set up language model-based agents. From reflexion to execution, this tutorial has you covered with direct testing examples and valuable insights.
+Link: https://www.youtube.com/watch?v=ZJlfF1ESXVw&list=PLmqn83GIhSInDdRKef6STtF9nb2H9eiY6&index=9
+
+60. Agents at Google I/O event
+Dive into the buzz surrounding Google I/O, where groundbreaking announcements like Project Astra and AI team mates showcase the rapid evolution of LLM agents. Discover the limitless potential of agentic workflows in this exciting showcase of innovation and discovery!
+Link: https://sites.google.com/corp/google.com/io-2024-for-googlers/internal-coverage?authuser=0&utm_source=Moma+Now&utm_campaign=IO2024&utm_medium=googlernews
+
+61. LLM’s spatial intelligence journey
+Did you catch the awe-inspiring Project Astra demo? If yes, you might have definitely wondered what powered the assistant's responses. Dive into the quest for spatial intelligence in LLM vision and discover why it's the next frontier in AI. In this video, AI luminary Fei-Fei Li reveals the secrets behind spatial intelligence and its potential to revolutionize AI-human interactions.
+Link: https://www.youtube.com/watch?v=y8NtMZ7VGmU
+62.Anthropic unlocks the mystery of LLMs
+In a groundbreaking study, Anthropic has delved deep into the intricate mechanisms of Large Language Models (LLMs), specifically focusing on Claude 3. Their pioneering research not only uncovers hidden patterns within these AI models but also provides crucial insights into addressing bias, safety, and autonomy concerns.
+Link: https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html
+63. Multi AI Agent systems with Crew AI
+Ever wanted to build a multi-agent system? Here is an exclusive short course around this topic led by Joao Moura, the visionary creator behind the groundbreaking Crew AI framework. Discover the secrets to building robust multi-agent systems capable of tackling complex tasks with unparalleled efficiency.
+
+What is so special about the Crew AI framework? Over 1,400,000 multi-agent crews have been powered by this cutting-edge framework in the past 7 days alone!
+Link: https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/
+64. Self-correcting coding assistant from Mistral
+Mistral just announced Codestral - a self-correcting code assistant well-versed in 80+ Programming Languages. Here is a detailed video tutorial from Langchain team around using the model.
+Save time, reduce errors, and level up your coding game with Codestral!
+Link: https://mistral.ai/news/codestral/
+65. Multi AI agentic systems with AutoGen
+Last week, you learned to build multi-agent systems using Crew AI. This week, you get to explore AutoGen, probably the first multi-agent framework to hit the market.
+Implement agentic design patterns: Reflection, Tool use, Planning, and Multi-agent collaboration using AutoGen. You also get to learn directly from the creators of AutoGen, Chi Wang and Qingyun Wu.
+Link: https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen/
+66. Lessons from a year of LLM adventures
+The past year has seen LLMs reach new heights, becoming integral to real-world applications and attracting substantial investment. Despite the ease of entry, building effective AI products remains a challenging journey.
+
+Here’s a glimpse of what Gen AI product builders have learned!
+
+Link: https://applied-llms.org/
+
+67.Build agentic systems with LangGraph
+Last week, it was AutoGen and the week before it was Crew AI. This week, you get to explore LangGraph, a framework by Langchain that lets you build agentic system.
+
+Discover LangGraph’s components for developing, debugging, and maintaining AI agents, and enhance agent performance with integrated search capabilities. Learn from LangChain founder Harrison Chase and Tavily founder Rotem Weiss.
+Link: https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/
+
+'''
 
 def respond(
     message,