[Music] to quickly introduce myself. My name is Mitesh. I lead the develop advocate team at Nvidia.And the goal of my team is to uh create technical workflows, notebooks uh for different applications and then we release that codebase uh on GitHub. So developers in general which is me and you all of us together we can harness that uh that knowledge and take it further for the application or use case that you're working on. So that is what my uh my team does including myself.In today's talk, I'm I'm I'm going ...

LLM & MCP Integration - A framework enables connecting any LLM to any MCP server [1] - The framework facilitates building custom MCP Agents without relying on closed-source applications [1] - It is compatible with tools like Ollama and LangChain [1] - The framework allows building 100% local MCP clients [1]

Core Insights - Embedded LLM has launched TokenVisor, a monetization and management platform for GPUs, aimed at addressing the challenges organizations face in translating hardware investments into revenue [1][3][6] - TokenVisor is designed to simplify operations for GPU owners, enabling them to manage and monetize LLM workloads effectively [4][5][6] Industry Context - As organizations build "AI factories," they encounter difficulties in achieving positive ROI from significant hardware investments without effective tools for billing and usage tracking [3] - The platform is positioned as a commercialization layer for the AMD AI ecosystem, enhancing the capabilities of GPU providers [4][6] Product Features - TokenVisor allows users to set custom, token-based pricing for LLM models, monitor real-time usage, automate billing, manage resource allocation, and implement governance policies [7] - Early adopters have reported that TokenVisor has streamlined the commercialization process, enabling rapid deployment of revenue-generating services [8] Strategic Partnerships - The collaboration between Embedded LLM and AMD, as well as Lenovo, highlights the importance of integrated solutions in accelerating AI revenue and providing financial frameworks for AI investments [5][6] - Lenovo's integration of TokenVisor with its ThinkSystem servers and AMD Instinct GPUs is expected to enhance customer capabilities in launching LLM services [5] Market Impact - The launch of TokenVisor signifies a new phase of maturity for the AMD AI ecosystem, allowing providers to compete more effectively by deploying and billing for LLM services [6] - The platform's comprehensive support for popular LLM models and responsive technical support are critical for rapid deployment and ROI [8]

AI发展趋势 - AI正在向体验时代演进 [1] - LLM（大型语言模型）是通用方案的核心 [1] - AI发展呈现不均衡性 [1] - AI领域存在新的前沿 [1]

Agent Architecture & Functionality - The Langchain deep research agent is highly configurable and open source, allowing for customization to specific use cases [1] - The agent operates in three main phases: scoping the problem, research, and report writing [3] - The research phase utilizes a supervisor to delegate tasks to sub-agents for in-depth research on specific subtopics [4] - Sub-agents use a tool calling loop, which can be configured with default or custom tools (like MCP servers) for searching flights, hotels, etc [17][18] - A compression step is used by sub-agents to synthesize research findings into comprehensive mini-reports before returning to the supervisor, mitigating context window overload [21][23] - The supervisor analyzes findings from sub-agents to either complete research or continue with follow-up questions [25] - Final report generation is done in a single shot using all condensed research findings [5][27] Implementation & Configuration - The agent is built on Langraph and can be run locally by cloning the Open Deep Research repository [29] - Configuration involves setting API keys for models (default OpenAI) and search tools (default Tavily) [30] - Langraph Studio can be used for iterating and testing the agent with different configurations [32] - The agent is highly configurable, allowing users to choose between default or model provider native search tools, connect to MCP servers, and select models for different steps [33][34] Application & Output - The agent can be used for complex research tasks, such as planning a trip, by iteratively calling tools and searching the web [2] - The agent provides a final report with an overview, flight options, transportation options, accommodation options with booking links, a sample itinerary, and a list of sources [36] - Open Agent Platform provides a UI to configure and try out the research agent without cloning the code [37]

Partnership & Integration - Coinbase partnered with Perplexity AI, indicating a strategic move beyond data provision [1] - The partnership aims to provide traders with an AI edge, suggesting enhanced trading capabilities [1] - The collaboration lays the groundwork for full Large Language Model (LLM) integration into the crypto economy [1] Potential Impact - The integration could significantly alter the crypto landscape, implying transformative changes [1]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Now you can supercharge your terminal with MCP servers (open-source).MCP CLI lets you interact with local and remote MCP servers, built with a rich UI, and full LLM provider integration.You can run tools, manage conversations, or automate workflows directly from your https://t.co/FME7lArlTZ ...

Core Principles of Agent Building - The industry emphasizes rethinking agent development from first principles, applying established software engineering practices to build reliable agents [11] - The industry highlights the importance of owning the control flow in agent design, allowing for flexibility in managing execution and business states [24][25] - The industry suggests that agents should be stateless, with state management handled externally to provide greater flexibility and control [47][49] Key Factors for Reliable Agents - The industry recognizes the ability of LLMs to convert natural language into JSON as a fundamental capability for building effective agents [13] - The industry suggests that direct tool use by agents can be harmful, advocating for a more structured approach using JSON and deterministic code [14][16] - The industry emphasizes the need to own and optimize prompts and context windows to ensure the quality and reliability of agent outputs [30][33] Practical Applications and Considerations - The industry promotes the use of small, focused "micro agents" within deterministic workflows to improve manageability and reliability [40] - The industry encourages integrating agents with various communication channels (email, Slack, Discord, SMS) to meet users where they are [39] - The industry advises focusing on the "hard AI parts" of agent development, such as prompt engineering and flow optimization, rather than relying on frameworks to abstract away complexity [52]

AI Development History - The AI debate in the 2010s was framed by two books: one predicting superhuman intelligence and the other focusing on AI as surveillance tech [1][2] - Reality, exemplified by LLMs and ChatGPT, fell between these extremes, aligning with the traditional definition of AI as passing the Turing test [2][3] - ChatGPT's ability to pass the Turing test is considered a significant achievement [3]

Company Overview - TME is committed to the healthy development of China's online music industry[7] - TME has a large user base with 576 million online music MAUs[13] and 90 million social entertainment mobile MAUs[15] in 3Q2024 - TME boasts an extensive content library with over 200 million music and audio tracks[14, 20] and 480K+ indie musicians[15] - TME's total cash, cash equivalents, term deposits, and short-term investments reached RMB 3604 billion[15, 18, 49] Business Overview - TME has partnerships with hundreds of domestic and international music labels[20] - TME is expanding LLM capabilities, AIGC tools & full-suite of resources and services to streamline content production[21] - TME cultivates and empowers indie musicians & original music through Tencent Musician Platform[22] Financial Highlights - TME's online music monthly ARPPU was RMB 108 in 3Q24, a 49% year-over-year increase[37] - TME's revenue from music subscriptions reached RMB 384 billion in 3Q24, a 203% year-over-year increase[37] - TME's gross margin was 426% in 3Q24, a 69 percentage point year-over-year increase[37] - TME's Non-IFRS net profit was RMB 194 billion in 3Q24, a 291% year-over-year increase[37]