द इस्लामिक रिपब्लिक ऑफ ईरान कंडेम्स इन द स्ट्रांगेस्ट टर्म्स द यूनाइटेड स्टेट्स ब्रूटल मिलिट्री अग्रेशन अगेंस्ट ईरान्स पीसफुल न्यूक्लियर फैसिलिटीज इट इज़ एन आउटस ग्रेव एंड अनप्रेसिडेंटेड वलेशन ऑफ द फंडामेंटल प्रिंसिपल्स ऑफ द चार्टर ऑफ द यूनाइटेड नेशंस एंड इंटरनेशनल लॉ द वॉर मगरिंग लॉलेस एडमिनिस्ट्रेशन इन वाशिंगटन सोली एंड फुली रिससिबल फॉर द डेंजरस एंड फॉर रिचिंग इंप्लिकेशंस ऑफ इट्स एक्ट ऑफ अग्रेशन देयर इज़ नो रेड लाइन दैट दे हैव नॉट क्रस्ट एंड द लास्ट वन एंड द मोस्ट डेंजरस वन वाज़ हैपेंड ओनली लास्ट नाइट व्हेन ...

The Islamic Republic of Iran condemns in the strongest terms the United States brutal military aggression against Iran's peaceful nuclear facilities. It is an outrageous, grave and unprecedented violation of the fundamental principles of the charter of the United Nations and international law. The warmongering and lawless administration in Washington.Is solely and fully responsible for the dangerous consequences and far reaching implications of its act of aggression. The U.S. military attack on the territor ...

Pigment's Business and Technology - Pigment is an enterprise planning and performance management platform that helps companies build strategic plans and adapt to changing market conditions [1] - Pigment AI consists of conversational AI and autonomous agents that accelerates insight generation and scenario creation across the organization [2] - Pigment's autonomous agents framework allows users to schedule and automate reports and scenario creation, saving hundreds of hours of manual work [3] Challenges with Previous AI Architecture - Linear chain pipelines limited flexibility and made experimentation with agent-based workflows complex and cumbersome [4] - Managing graphs, memory, state transitions, and interruptions for custom agents was too complex [5] - Strong control over tools and agents, simple state management, and asynchronous processing were critical needs for financial use cases [5] Benefits of Long Graph - Long Graph offers graph-based orchestration, long-term memory, streaming, and interrupt capabilities [6] - Graph orchestration is easy to set up, allowing easy definition and tweaking of agent iteration and collaboration [6] - Full visibility and control over message flow between agents enables building reliable and testable logic [7] - Agent topologies can be abstracted into configuration files, enabling rapid prototyping and deployment of new workflows [7] Impact of Long Graph - Reduced time to insight from hours to seconds using natural language search and agent analysis [8] - Faster decision-making by surfacing anomalies and key performance gaps in real time [8] - Users can focus on higher value work by automating routine analysis and planning tasks [9] - Engineering team has more time to experiment and innovate, focusing on higher impact features [9] - Significantly less time is spent implementing key site capabilities like persistent, long-term memory [9]

So, uh, for people who are watching who want to know, uh, what they might do with their money, let's suppose people don't know about private wealth management and they're not really that financially sophisticated. What do you think the average person, middle class person should do with his or her money. So, I think uh, if you're an average person, depending on your age, you should always keep in cash or something safe as much money as you need for a couple of months of your expenses, 2, three, four months.A ...

[Music] I'm Isis Julian and I'm a senior software engineer at Morning Star and I work on the intelligence engine. Morning Star is a global leader in providing investment research data and analysis and we pride ourselves in empowering investor success by serving transparent, accessible, and reliable investment information. So, with AI gaining increasing popularity nearing the end of 2022 and early 2023, with a scrappy team of just five engineers, we were able to launch our first ever AI research assistant na ...

Company Overview - 11X is building digital workers, including Alice, an AI SDR, and Julian, an AI voice agent [1] - The company relocated from London to San Francisco and rebuilt its core product, Alice, from the ground up [2] Alice Rebuild & Vision - The rebuild of Alice was driven by the belief that agents are the future [3] - The new vision for Alice centers on seven agentic capabilities, including chat-based interaction, knowledge base training via document uploads, AI-driven lead sourcing, deep lead research, personalized emails, automatic handling of inbound messages, and self-learning [11][12][13] Development Process & Tech Stack - The rebuild of Alice 2 took only 3 months from the first commit to migrating the last business customer [3][14] - The company chose a vanilla tech stack and leveraged vendors like Langchain to move quickly [15][16][17] - Langchain was chosen as a key partner due to its AI dev tools, agent framework, cloud hosting, observability, Typescript support, and customer support [18][19] Agent Architecture Evolution - The company experimented with three different architectures for campaign creation: React, workflow, and multi-agent systems [21] - The final architecture was a multi-agent system with a supervisor and specialized sub-agents for research, positioning, LinkedIn messaging, and email writing [44][45][46] Results & Future Plans - Alice 2 went live in January and has sourced close to 2 million leads and sent close to 3 million messages [52] - Alice 2 has generated about 21,000 replies, with a reply rate of around 2%, on par with a human SDR [52] - Future plans include integrating Alice and Julian, implementing self-learning, and exploring new technologies like computer use, memory, and reinforcement learning [53][54]

Agent Adoption & Scalability - LinkedIn aims to scale agentic adoption within the organization to enable broader idea generation [2] - LinkedIn built the Hiring Assistant, its first production agent, to automate recruiter tasks and free up time for candidate interaction [3] - The Hiring Assistant follows an ambient agent pattern, operating in the background and notifying recruiters upon completion [4][5] - LinkedIn adopted a supervisor multi-agent architecture, with a supervisor agent coordinating sub-agents that interact with LinkedIn services [6] Technology Stack & Framework - LinkedIn standardized on Python for GenAI development, moving away from its traditional Java-centric approach [7][8] - The company built a service framework using Python, gRPC, Langchain, and Langraph to streamline the creation of production-ready Python services [9][19] - Over 20 teams have used this framework to create over 30 services supporting Generative AI product experiences [9][10] - Langchain and Langraph were chosen for their ease of use and sensible interfaces, enabling rapid development and integration with internal infrastructure [22][23] Infrastructure & Architecture - LinkedIn invested in a distributed architecture to support agentic communication modes [10] - The company modeled long-running asynchronous flows as a messaging problem, leveraging its existing messaging service for agent-to-agent and user-to-agent communication [26][27] - LinkedIn developed agentic memory with scoped and layered memory types (working, long-term, collective) [29][30] - LinkedIn implemented a centralized skill registry, allowing agents to discover and access skills developed by different teams [34][35]

[Music] I'm Paul Rasgitis and I'm the tech lead for AI products at Modern Treasury. Modern Treasury is the payment operations platform built for the instant economy. At Modern Treasury, our goal is to transform how teams track and move money.Recently, we launched Modern Treasury AI, a horizontal platform that includes chat, task management, monitoring, and an AI agent designed specifically for financial operations. The agent is just one part of this broader experience, but it plays a key role in how users i ...

All right. Hello everyone. Uh, thanks for being here and joining us on this nice Wednesday afternoon.Uh, my name is Matasanis and this is my colleague. Hey folks, I'm Sorup Sherhhati. And today we're going to present how we built AI developer tools at Uber uh, using Langraph.So to start off, a little bit of context. Um Uber is a massive company serving 33 million trips a day across 15,000 cities. And this is enabled enabled by a massive code base with hundreds of millions of lines of code.And it is our job ...

Summary of Dell Technologies (DELL) 2025 Conference Call Company Overview - **Company**: Dell Technologies (DELL) - **Event**: Bank of America's Global Tech Conference - **Date**: June 03, 2025 Key Points Financial Performance - **Revenue**: $10.3 billion in the Infrastructure Solutions Group (ISG), representing a 12% year-over-year growth, marking the fifth consecutive quarter of double-digit revenue growth [6] - **Operating Margins**: Approximately $1 billion, growing 36%, which is three times faster than revenue growth [6] - **Record Orders**: Bookings reached $12.1 billion, exceeding total shipments from the previous fiscal year in just the first quarter [7] - **Backlog**: A record backlog of $14.4 billion, indicating strong future demand [8] AI and Technology Trends - **AI Demand**: The company experienced a "blockbuster AI quarter," with significant growth in AI-related orders and a robust pipeline for the next five quarters [7][8] - **Server Business Growth**: The overall server networking business grew by 16%, with six consecutive quarters of demand growth [9] - **PowerStore Performance**: PowerStore saw its highest growth rate in 12 quarters, with 15% of new buyers being new to Dell [10] Market Dynamics - **Product Transition**: The company is adept at managing complex product transitions, having successfully deployed new technologies like Hopper and NVL 72 ahead of competitors [27][28] - **Supply Chain Management**: Dell has effectively navigated geopolitical issues and tariffs, maintaining price stability and agility in operations [34] Strategic Focus Areas - **Disaggregated Infrastructure**: There is a significant trend towards disaggregated infrastructure, driven by the need for flexibility and efficiency in cloud operations [38][41] - **Customer Segmentation**: The company identifies three main customer segments: tier two cloud service providers (CSPs), sovereign entities, and enterprises, with varying levels of maturity in AI adoption [44][50] Future Outlook - **Revenue Guidance**: The company anticipates revenue growth of over $15 billion for the year, with a focus on AI and related technologies [15][73] - **Materiality of AI Business**: The AI segment is still nascent, but expected to ramp up quickly, with significant future contributions to revenue [75] Innovation and Product Development - **Product Announcements**: Dell has made 40 major product announcements, showcasing its commitment to innovation in AI and infrastructure [56] - **Integration of Systems**: Dell positions itself as a unique integrator of compute, network, and storage solutions, enhancing performance for AI applications [65][66] Customer Engagement - **Strategic Partnerships**: The company is actively engaging with customers to define their future data strategies and optimize their AI deployments [51][55] - **Use Cases for AI**: Examples of AI applications include content generation, coding assistance, and customer service enhancements, demonstrating clear ROI for enterprises [59][62] Additional Insights - **Market Conditions**: A slight slowdown in demand was noted around April 2, attributed to macroeconomic uncertainties, but the overall market remains growing [78] - **Installed Base**: 75% of Dell's installed base is on servers 14 generations or older, indicating a significant opportunity for upgrades [79] This summary encapsulates the key insights and data points from the Dell Technologies conference call, highlighting the company's strong performance, strategic focus on AI, and innovative capabilities in the tech industry.