AI Engineering Resources - The document provides 12 cheat sheets for AI engineers covering various topics [1] - The cheat sheets include visuals to aid understanding [1] Key AI Topics Covered - Function calling & MCP (likely Mean Cumulative Probability) for LLMs (Large Language Models) is covered [1] - The cheat sheets detail 4 stages of training LLMs from scratch [1] - Training LLMs using other LLMs is explained [1] - Supervised & Reinforcement fine-tuning techniques are included [1] - RAG (Retrieval-Augmented Generation) vs Agentic RAG is differentiated [1]

Core Insights - The future of "open source" is facing unprecedented challenges as enterprise-level LLM API spending has doubled from $350 million to $840 million in the past six months, indicating a shift towards closed-source models that are establishing a performance moat in the billion-dollar market [1][4][9] - The report highlights that despite the cost advantages of open-source models, performance gaps and deployment complexities are hindering their expansion in the enterprise market [2][14] - The rise of Anthropic, which has surpassed OpenAI with a 32% market share, reflects a preference for performance over price among enterprise users [2][9] Group 1: Market Dynamics - The adoption rate of open-source models in the enterprise market is stabilizing, lagging behind closed-source models by 9 to 12 months in performance [2][14] - Developers prioritize performance over cost, with 66% upgrading models within their existing provider rather than switching vendors [20][23] - The shift in AI spending is moving from model training to inference, with 74% of developers in startups indicating that most of their workloads are now inference-driven [27] Group 2: Competitive Landscape - Code generation has emerged as the first killer application of AI, with Claude capturing 42% of the market share compared to OpenAI's 21% [13] - The competitive landscape is reshaped as enterprises increasingly favor high-performance closed-source models, leading to a decline in the market share of OpenAI from 50% to 25% over two years [9][12] - The introduction of models like Claude Sonnet 3.5 and 3.7 has accelerated Anthropic's momentum, showcasing the importance of performance in model selection [12][13] Group 3: Future Trends - The report suggests that 2025 will be the "year of agents," where large models evolve from simple Q&A machines to more complex problem-solving assistants through tool integration and multi-turn interactions [2][13] - The use of reinforcement learning with verifiers (RLVR) is identified as a new pathway for expanding intelligence, particularly effective in areas like coding [2][13] - The market is expected to continue evolving rapidly, driven by new model releases and advancements in foundational model capabilities [31]

Let's explore practical strategies for building and scaling agents in production. Discover how to move from local MCP implementations to cloud-scale architectures and how engineering teams leverage these patterns to develop sophisticated agent systems. Expect a mix of demos, use case discussions, and a glimpse into the future of agentic services! About Antje Barth Antje Barth is a Principal Developer Advocate at AWS, based in San Francisco. She frequently speaks at AI engineering conferences, events, and me ...

AI Engineering Landscape - The AI engineering community is broad, technical, and growing, with the "AI Engineer" title expected to gain more ground [5] - Many seasoned software developers are AI newcomers, with nearly half of those with 10+ years of experience having worked with AI for three years or less [7] LLM Usage and Customization - Over half of respondents are using LLMs for both internal and external use cases, with OpenAI models dominating external, customer-facing applications [8] - LLM users are leveraging them across multiple use cases, with 94% using them for at least two and 82% for at least three [9] - Retrieval-Augmented Generation (RAG) is the most popular customization method, with 70% of respondents using it [10] - Parameter-efficient fine-tuning methods like LoRA/Q-LoRA are strongly preferred, mentioned by 40% of fine-tuners [12] Model and Prompt Management - Over 50% of respondents are updating their models at least monthly, with 17% doing so weekly [14] - 70% of respondents are updating prompts at least monthly, and 10% are doing so daily [14] - A significant 31% of respondents lack any system for managing their prompts [15] Multimodal AI and Agents - Image, video, and audio usage lag text usage significantly, indicating a "multimodal production gap" [16][17] - Audio has the highest intent to adopt among those not currently using it, with 37% planning to eventually adopt audio [18] - While 80% of respondents say LLMs are working well, less than 20% say the same about agents [20] Monitoring and Evaluation - Most respondents use multiple methods to monitor their AI systems, with 60% using standard observability and over 50% relying on offline evaluation [22] - Human review remains the most popular method for evaluating model and system accuracy and quality [23] - 65% of respondents are using a dedicated vector database [24] Industry Outlook - The mean guess for the percentage of the US Gen Z population that will have AI girlfriends/boyfriends is 26% [27] - Evaluation is the number one most painful thing about AI engineering today [28]

2/ We’re building @EggDotParty as a workspace where creators use agents to build, automate, and monetize, without needing crypto knowledge or 10 disconnected apps. ...

[Music] Thanks Alex for the introduction. That was a very impressive LLM generated summary of me. Uh I've never heard it before but uh nice.Um so um today I'm going to talk to you about something that has been keeping me up at night. Uh probably some of you too. So how to build enterprise aware agents.How to bring the brilliance of AI into the messy complex realities of uh how your business operated. So let's jump straight to the hottest question of the month for AI builders. Uh should I build workflows or ...

AG-UI Protocol Overview - AG-UI protocol has become the standard for building front-end Agentic apps where Agents are part of the interface [1] - AG-UI defines a common interface between Agents and the UI layer, remaining Agent framework agnostic [2] Key Features of AG-UI - AG-UI enables streaming token-level updates, showing tool progress in real time, sharing mutable state, and pausing for human input [2] - Developers can spin up a full-stack AG-UI app directly from CLI and visualize A2A interactions [2] - Pydantic AI is now AG-UI compatible [2] Development Efficiency - Building AG-UI frontends is now 10x faster with a plug-and-play interface [1][2] - A fully revamped contributor flow is available for developers [2] Agent Connectivity - MCP connects agents to tools, A2A connects agents to other agents, and AG-UI connects agents to users [2]

Agentic Apps Development - AG-UI protocol simplifies front-end Agentic app development, making it 10x easier [1] - AG-UI is becoming the standard for apps where Agents are part of the interface [1] Agent Communication Protocols - MCP connects agents to tools [1] - A2A connects agents to other agents [1]

Building front-end Agentic apps just got 10x easier (open-source)!If you're building apps where Agents are part of the interface, not just running in the background, AG-UI protocol has become the standard.For context:- MCP connects agents to tools- A2A connects agents to other agents- AG-UI connects agents to usersIt defines a common interface between Agents and the UI layer.AG-UI itself is Agent framework agnostic, and it lets you:- stream token-level updates- show tool progress in real time- share mutable ...

Summary of Conference Call Industry Overview - The conference focused on the U.S. AI industry and stock market changes, highlighting significant movements by major companies like NVIDIA and Microsoft [1][2] - The discussion emphasized the evolving landscape of AI, particularly the introduction of next-generation Internet concepts such as Agents Network and Agent Web [2][10] Key Companies and Developments NVIDIA - NVIDIA's NVLink Fusion product was a major highlight, showcasing advancements in AI chip architecture and model training capabilities [3][4] - NVLink has evolved from version 1.0 in 2016 to version 5.0, enhancing interconnectivity between different computing units [3] - The company is adapting to customer needs by offering customized solutions for AI and IoT applications, indicating a shift towards more tailored products [4][5] - NVIDIA's partnerships with companies like Boton and Marvell are expanding its market reach, particularly in customized chip solutions [5][6] - The anticipated launch of larger AI clusters (up to 500,000 cards) for model training is expected by the end of the third quarter [9] Microsoft - Microsoft's Build conference emphasized the concept of Agents as a core focus, with a shift towards a more integrated Internet experience [10][11] - The company is primarily targeting B2B markets, but its progress in AI model development is perceived as average compared to competitors like Google [11][12] - Microsoft’s token generation in the last quarter was about 1 million tokens, significantly lower than Google's performance [11] Google - Google’s IoT conference was noted for its comprehensive approach to AI, with a focus on its Gemini model and various cloud-based products [12][14] - The company is leading in AI commercialization, with a monthly token processing capacity significantly higher than Microsoft’s [15][17] - Google’s AI strategy includes a robust framework for developers and a strong emphasis on integrating AI into its existing products [14][15] OpenAI - OpenAI's acquisition of the design company IOU for $6.5 billion aims to enhance its hardware product offerings, indicating a strategic move towards the next generation of Internet [18][20] - The focus on hardware development is seen as crucial for maintaining competitive advantage in the evolving AI landscape [21][22] Additional Insights - The conference highlighted the competitive dynamics between major players in the AI space, with NVIDIA, Google, and OpenAI positioned as leaders [15][20] - The discussion also touched on the importance of product design and innovation, particularly in the context of hardware development in Silicon Valley [22] - Future trends in AI are expected to revolve around the integration of agents and the development of platforms that unify various AI applications [31] Conclusion - The AI industry is rapidly evolving, with significant advancements in technology and strategic partnerships among leading companies. The focus on customized solutions and the integration of AI into various sectors will likely shape the future landscape of the industry [31]