Since we started using Llama Cloud, our data ingestion process uh moved from taking around 3 weeks up to less than a day. My name is Daniel Garcia Zapata. I'm a senior data scientist at Se, a global building material company, one of the largest rhythmics producers in the world.We use AI to improve our maintenance, supply chain optimization, smart operations, health and safety, and for commercial efforts, we use it to uh help our salespeople improve uh their engagement with our customers. We didn't have a st ...

Core Insights - The article discusses the challenges faced by Go developers in creating AI applications, highlighting the need for a native AI development experience tailored for Go language [1][2] - It introduces a new framework, Eino, aimed at enabling Go developers to build AI agents and applications more efficiently [4][5] Group 1: Event Overview - A live demonstration will be organized for backend developers, focusing on the Deep Research application, Deerflow, which utilizes LangChain and LangGraph [4] - The goal of the live session is to build a complete AI application from scratch using the Eino framework, showcasing the architecture and design principles [4][5] Group 2: Expert Involvement - Two engineers from ByteDance will participate in the event, with one acting as the "architect decoder" to explain the design of Deerflow, and the other as the "Go AI application master" to demonstrate the implementation using Eino [5] - This collaboration aims to provide insights into defining powerful AI agents and the practical application of the Eino framework [5][7] Group 3: Target Audience - The event is targeted at Go developers looking to enhance their competitive edge in AI, AI/LLM application developers seeking efficient frameworks, and backend engineers curious about AI technology [7] - Participants are encouraged to engage with the content if they are passionate about creating intelligent solutions through code [7] Group 4: Event Details - The live session is scheduled for July 9, 2025, at 7:30 PM, with opportunities for participants to win custom prizes [8] - Registration is available through a QR code for reminders and exclusive materials [8] Group 5: Conclusion - The article emphasizes the potential for a significant shift in the Go language's capabilities in the AI agent domain, promising an exciting event for attendees [9]

Core Viewpoint - The article discusses the transformation of Google's search business towards AI-driven search modes, highlighting the challenges faced by traditional search engines in the face of emerging AI technologies and competition from Chatbot-integrated platforms [4][24]. Group 1: Google's AI Search Transformation - Google announced the launch of its AI Mode powered by Gemini, which allows for natural language interaction and structured answers, moving away from traditional keyword-based searches [2][4]. - In 2024, Google's search business is projected to generate $175 billion, accounting for over half of its total revenue, indicating the significant financial stakes involved in this transition [4]. - Research suggests that Google's search market share has dropped from over 90% to between 65% and 70% due to the rise of AI Chatbots, prompting the need for a strategic shift [4][24]. Group 2: Challenges for AI Search Engines - Perplexity, an AI search engine, saw its user visits increase from 45 million to 129 million, a growth of 186%, but faced a net loss of $68 million in 2024 due to high operational costs and reliance on discounts for subscription revenue [9][11]. - The overall funding for AI search products has decreased, with only 10 products raising a total of $893 million from August 2024 to April 2025, compared to 15 products raising $1.28 billion in the previous period [11][12]. - The competitive landscape for AI search engines has worsened, with many smaller players struggling to secure funding and differentiate themselves from larger companies [11][12][25]. Group 3: Shift Towards Niche Search Engines - The article notes a trend towards more specialized search engines, focusing on specific industries or use cases, as general AI search engines face increasing competition from integrated Chatbot functionalities [13][25]. - Examples of niche search engines include Consensus, a health and medical search engine, and Qura, a legal search engine, both of which cater to specific professional audiences [27][30]. - The overall direction for AI search engines is towards being smaller, more specialized, and focused on delivering unique value propositions to specific user groups [13][26]. Group 4: Commercialization Challenges - The commercialization of AI search remains a significant challenge, with Google exploring ways to integrate sponsored content into its AI responses while facing potential declines in click-through rates for traditional ads [43]. - The article emphasizes the need for AI search engines to deliver more reliable and usable results, either through specialized information or direct output capabilities, to remain competitive [43][24].