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Embedded LLM Launches First-of-its-Kind Monetisation Platform for AMD AI GPUs
GlobeNewswire News Room· 2025-07-22 02:30
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]
Open Deep Research
LangChain· 2025-07-16 16:01
Hi there. Today you're going to learn all about the Langchain deep research agent and how you can use it as a starting point for your projects. It's highly configurable and allows you to add your own MCP servers and is open source so you can tailor it to your own specific use cases.Let's see how it works. So later this year, my roommates and I want to take a trip to Amsterdam and Norway. We want to leave New York on September 12th and get back on the following Sunday.I want to ask Deep Research if it can he ...
X @Cointelegraph
Cointelegraph· 2025-07-10 14:25
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]
X @Avi Chawla
Avi Chawla· 2025-07-06 06:31
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 ...
12-Factor Agents: Patterns of reliable LLM applications — Dex Horthy, HumanLayer
AI Engineer· 2025-07-03 20:50
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]
Peter Thiel on the Origins of Modern AI: It was always US vs China
All-In Podcast· 2025-07-02 21:51
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]
TME(TME) - 2024 Q3 - Earnings Call Presentation
2025-07-01 12:25
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]
The emerging skillset of wielding coding agents — Beyang Liu, Sourcegraph / Amp
AI Engineer· 2025-06-30 22:54
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]
X @Avi Chawla
Avi Chawla· 2025-06-30 06:33
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):A Python decorator is all you need to trace LLM apps (open-source).Most LLM evals treat the app like an end-to-end black box.But LLM apps need component-level evals and tracing since the issue can be anywhere inside the box, like the retriever, tool call, or the LLM itself. https://t.co/dWXyJb3DNs ...