Overview - Mastra.ai is a TypeScript framework designed to streamline the development of agentic AI systems, offering an alternative to traditional approaches using LangChain and vector databases [1] - The workshop aims to equip participants with the skills to develop scalable AI-driven internal tools based on sound software engineering principles [1] Technical Aspects - Participants will learn to build structured AI workflows with composable tools and reliable control [1] - The session covers Mastra installation, running a local MCP server, defining tools and agents in TypeScript, and using the Mastra playground [1] - Practical examples include RAG setups and tool-chaining agents [1] Application - The framework enables the creation of internal AI assistants capable of handling requests like data cleaning, email drafting, and document summarization with minimal code [1] Speakers - Nick Nisi is an elite software engineer with expertise in open source web development [1] - Zachary Proser builds AI systems and shares his learnings through sample applications, technical guides, and real-world lessons [1]

Core Insights - Workday's research reveals that 76% of employees lack clarity on contract ownership, leading to lost revenue and missed opportunities [2][4] - The report emphasizes the importance of contract intelligence in transforming contracts from static documents into strategic assets that drive business growth [3][4] Key Findings - A significant portion of employees (50% of legal and 49% of enterprise employees) report financial losses due to unintended auto-renewals, with sales and marketing departments being the most affected (60%) [4] - There is a disconnect between legal teams and other employees regarding contract approvals, with an 18% discrepancy indicating potential risks from bypassing legal processes [4] - Contracts are often fragmented across various systems, with 70% of legal and 50% of non-legal contracts stored in shared drives, which hinders holistic visibility and opportunity identification [4] - Over 41% of respondents believe that slow contract processes stifle innovation and productivity, particularly affecting departments like R&D and engineering (79%) [4]

🤖🤖🤖Aaron Levie (@levie):The play right now is to go deep in a vertical and build AI Agents with the context of the critical workflows, domain expertise, specialized instructions, and data of that industry. And build anticipating what’s possible with AI models in a year from now, not just today. ...

RT The Rollup (@therollupco)NEW EP: Why AI Agents Need Better Blockchain Data with Marcel FohrmannWhile the hype of the narrative has slowed, the developments in ai x cryptoIn today's episode, @ayyyeandy sits down with @I_1337_I from @helloSQD to cover:> Why Current Data Infra Can't Scale> SQD's "Airbnb of Databases" Model> Why Institutional Players Are Taking Crypto Data Seriously> The Fight to Keep AI DecentralizedFull episode links below.Timestamps:00:00 Intro00:59 Magic Ad01:27 Starknet Ad01:53 Marcel’s ...

AI Tools & Resources - Recommends resharing insightful content related to DS, ML, LLMs, and RAGs [1] - Highlights 6 no-code LLMs, Agents, and RAG builder tools for AI engineers [1] - Focuses on open-source and production-grade AI tools [1] Author Information - Identifies Avi Chawla (@_avichawla) as a source of tutorials and insights [1]

AI技术发展 - LLM（大型语言模型）时代正在走向终结，行业正迈向更强大的自主代理（Autonomous Agents）时代 [2] - 从LLM到自主代理的转变是生成式AI的自然演进，而非彻底的革命 [18] - 行业不应犹豫是否采用自主代理，而应思考如何快速拥抱它们以释放其全部潜力 [31] 自主代理的优势与应用 - 自主代理能够独立完成整个工作流程，无需持续的人工指令或输入，是协作工具 [10] - 自主代理可以应用于多个行业，包括商业运营、内容发布、医疗和教育等 [13] - 在商业运营中，自主代理可以生成报告、发送给利益相关者、在社交媒体上发布摘要，并根据回复进行个性化修改 [20][21] - 在医疗领域，自主代理可以查看患者历史记录、X光片，并提供医生只需批准的建议 [23] 教育领域的革新 - 自主代理可以为每个学生定制课程、分发课程、监控参与度、评分作业，并向教师和家长提供报告 [24][25] - 通过自动化教师的重复性工作，教师可以将更多精力集中在与学生的个人互动上 [26] - AI教师Kaya已经实现了5倍以上的课程完成率和平均每小时4个学生提问 [27] - 行业正在努力使Kaya成为完全自主的代理，可以在VR世界甚至手机游戏中无缝支持学生 [29]

Core Insights - Zhihua Technology announced a strategic investment of 1 billion yuan from Shanghai Pudong Innovation Investment Development Group and Zhangjiang Group to enhance its AI infrastructure [1] - The company unveiled its new visual language model GLM-4.1V-Thinking, which excels in multimodal reasoning tasks, marking a significant advancement in AI capabilities [2][3] - The lightweight version, GLM-4.1V-9B-Thinking, achieved top performance in 23 out of 28 authoritative evaluations, demonstrating the potential of smaller models [3] Investment and Collaboration - The strategic investment aims to foster deeper collaboration in equity investment, industrial empowerment, and ecosystem development between Zhihua Technology and its partners [2] - The partnership with Zhangjiang Group includes the establishment of the "Moli Community," which focuses on large model enterprises and provides a dedicated model pool for member companies [1] Technological Advancements - GLM-4.1V-Thinking supports various input types, including images, videos, and documents, and is designed for complex cognitive tasks, such as live sports commentary [2] - The model incorporates a "Chain-of-Thought Reasoning" mechanism and a "curriculum sampling reinforcement learning strategy" to enhance cross-modal causal reasoning capabilities [2] New Platforms and Initiatives - The launch of the "Agent Application Space" aims to aggregate AI agent capabilities for enterprise clients and developers, facilitating easy access to advanced AI functionalities [3] - The "Agents Pioneer Program" will invest hundreds of millions to support AI agent startups, promoting innovation in the AI ecosystem [3] Industry Perspective - The future of AI is seen as a reconstruction of production paradigms, emphasizing the role of developers, designers, and entrepreneurs as co-creators of the intelligent ecosystem [4]

Core Problem & Solution - Traditional agent development suffers from slow iteration cycles due to code modifications for each use case, hindering business teams' experimentation [1] - LangGraph Assistants solve this by separating agent architecture from configuration, enabling code reuse across different use cases and faster experimentation [2] Key Features & Benefits - **Customization:** Allows customization of prompts, models, and tools without altering the underlying code, enabling rapid experimentation [3] - **Deployment:** Facilitates quick deployment of agent variations, allowing developers to push configuration changes without code deployments and business teams to launch assistants rapidly [4] - **Control:** Offers programmatic control for developers to automate assistant lifecycles, manage configurations at scale, and integrate with CI/CD pipelines [5] - **Configuration:** Configuration allows specifying customizable details such as prompts, models, and tools, enabling the same graph to have different capabilities based on runtime configuration [7] - **Versioning:** Provides robust version control and rollbacks, allowing for A/B testing and safe experimentation with configuration changes [44][45][46] LangGraph Studio - LangGraph Studio is a visual agent IDE that allows users to visualize and test agents [14][15] - It enables instant experimentation with different agent configurations, whether debugging locally or pulling production deployments [22] - It simplifies the configuration of complex multi-agent systems by allowing individual nodes to be configured separately [31][32][33][34][35][36] LangGraph Platform - LangGraph Platform is Langchain's enterprise solution for developing, deploying, and managing AI agents [38] - It allows users to create production-ready versions of assistants and access them via API [40][41][42] - It provides a complete REST API specification for creating, managing, and updating assistants programmatically [42][54] SDK & API - LangGraph provides an SDK and API for programmatically creating, using, and managing assistants [47][54] - The SDK allows integration with existing applications and systems, enabling management of the complete lifecycle of agents and assistants from code [54]

RT GG (@ggDecrypt)Ubisoft Adds AI Agents to ‘Captain Laserhawk’ Game That Vote and Govern► https://t.co/84s9J5z4er https://t.co/84s9J5z4er ...

Core Argument - The presentation argues that event-driven architecture (EDA), while seemingly loosely coupled at runtime, is tightly coupled at design time, leading to complexities and challenges in AI agent development [21][22] - It proposes a shift in focus from events to durable execution as the core of AI agent architecture, which simplifies development and handles failures more effectively [26][27] Problems with Event-Driven Architecture - EDA sacrifices clear APIs, as events lack the documentation and structure of traditional APIs [15] - Business logic becomes fragmented and scattered across multiple services, making debugging and understanding the system more difficult [16] - Services become ad hoc state machines, leading to potential race conditions and difficult-to-debug issues [18][19] - EDA can lead to reluctance to iterate on architecture due to fear of breaking existing functionality [25] Durable Execution as a Solution - Durable execution is presented as a crash-proof execution environment that automatically preserves application state, virtualizes execution, and is not limited by time or hardware [27][28][29][30][31][32][33][34] - It allows developers to focus on business logic rather than managing events and queues [38] - Temporal provides durable execution as an open-source, MIT-licensed product with SDKs for multiple programming languages [38][39] - Durable execution abstracts away the complexities of events into the software layer [40][43] Temporal's Offering - Temporal's durable execution system offers automatic retries for failures, such as LLM downtime or rate limits [36] - It supports polyglot programming, allowing functions written in different languages to be called seamlessly [39] - Temporal is available for demonstration and further discussion at the company's booth and Slack channel [44][45]