[music] morning. How's everyone doing. >> Good.>> I'm going to need a lot of energy for this talk, so please back me up. I'm very nervous. Uh but we'll get through this.I'm declaring war on slop today. Let's talk about this. Every AIE has a secret.I I've told this to uh some folks that are personal friends and I'll just show show the secret. Now the first summit we had the secret which was we knew that the AI engineer was going to be a thing. Second summit we extended it to leadership.Third summit we realiz ...

AI Engineering & Industry Leaders - Neo4j's Co-Founder and CEO discusses "The State of AI Engineering" [1] - Docker focuses on "Democratizing AI Agents: Building, Sharing, and Securing Made Simple" [1] - GitHub addresses "Building MCP's at GitHub Scale" [1] - H Company is assembling open source bricks for the next generation of AI [1] - Google DeepMind shares updates on generative AI [1] AI Infrastructure & Tools - Koyeb explores "Building for the Agentic Era: The Future of AI Infrastructure" [1] - Black Forest Labs presents "Inside FLUX, How It Really Works" [1] - LlamaIndex is building an open-source NotebookLM alternative [1] Open Source & Community - Hugging Face reports on the "State of Open LLMs in 2025" [1] AI Applications & Techniques - Arize AI studies "System Prompt Learning for Agents" [1] - ZML is working "Towards unlimited contexts: faster-than-GPU sparse logarithmic attention on CPU" [1] - Kyutai is scaling real-time voice AI [1]

RT Avi Chawla (@_avichawla)An AI Engineering Roadmap that beginners can actually follow!All using 100% free, open-source, and community resources. No need to pay $1000s for AI engineering courses!(find the resources below) https://t.co/y0DHxFrvLK ...

An AI Engineering Roadmap that beginners can actually follow!All using 100% free, open-source, and community resources. No need to pay $1000s for AI engineering courses!(find the resources below) https://t.co/y0DHxFrvLK ...

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]

Event Overview - Workshop focused on learning to use Gemini 2.5 Pro with Agentic tooling and MCP Servers [1] - Workshop was recorded at the AI Engineer World's Fair in San Francisco [1] Speaker Information - Philipp Schmid is a Senior AI Developer Relations Engineer at Google DeepMind [1] - Philipp Schmid's mission is to help developers create and benefit from AI responsibly [1] Resources - Newsletter available for updates on upcoming events and content [1] - Newsletter signup link: https://www.ai.engineer/newsletter [1]

General Information - This document is a recommendation to reshare insightful content related to Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval Augmented Generation (RAGs) [1] - The document highlights 10 free GitHub repositories that can help individuals prepare for a career in AI engineering [1] Resources - The author, Avi Chawla (@_avichawla), shares tutorials and insights on DS, ML, LLMs, and RAGs daily [1]

Project Highlights - AI Engineering Hub 即将突破 10k GitHub stars [1] - 该项目是 100% 开源的，并托管了 70 多个免费的实践演示 [1] - 提供了 10 个 MCP、RAG 和 AI Agents 项目供 AI 工程师使用 [1] Resource Sharing - 分享了关于 DS, ML, LLMs 和 RAGs 的教程和见解 [1]