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]

AI Coding Agents: Efficacy and Usage - Coding agents are substantively useful, though opinions vary on their best practices and applications [1] - The number one mistake people make with coding agents is using them the same way they used AI coding tools six months ago [1] - The evolution of frontier model capabilities drives distinct eras in generative AI, influencing application architecture [1] Design Decisions for Agentic LLMs - Agents should make edits to files without constant human approval [2] - The necessity of a thick client (e.g., forked VS Code) for manipulating LLMs is questionable [2] - The industry is moving beyond the "choose your own model" phase due to deeper coupling in agentic chains [2] - Fixed pricing models for agents introduce perverse incentives to use dumber models [2] - The Unix philosophy of composable tools will be more powerful than vertical integration [2] Best Practices and User Patterns - Power users write very long prompts to program LLMs effectively [4] - Directing agents to relevant context and feedback mechanisms is crucial [5] - Constructing front-end feedback loops (e.g., using Playwright and Storybook) accelerates development [6] - Agents can be used to better understand code, serving as an onboarding tool and enhancing code reviews [9][11] - Sub-agents are useful for longer, more complex tasks by preserving the context window [12][13]