RT s4mmy (@S4mmyEth)If you could build an AI agent for any industry, what problem would it solve? ...

Core Insights - The event highlighted the transformative impact of artificial intelligence (AI) on software development, emphasizing its evolution from a supportive tool to an intelligent collaborator [1][4][7] - AI-driven tools are enhancing productivity, reducing errors, and accelerating innovation across various stages of the software lifecycle [2][4] - The emergence of large language models (LLMs) is enabling more individuals to engage in programming, thus democratizing software development [3][5][6] Group 1: AI's Role in Software Development - AI is fundamentally changing software engineering by improving speed, accessibility, and reliability, making programming more mainstream [4][7] - Large language models, such as those developed by OpenAI, are capable of understanding and generating human language, which is now being applied to code generation and program development [2][3] - Code LLMs can assist developers in writing, debugging, and refactoring code, thereby enhancing the overall development process [3][4] Group 2: Future Trends in Programming - The future of programming is expected to be characterized by higher automation, stronger collaboration, and deeper integration of AI [4][7] - AI programming tools are evolving to become more intuitive, allowing developers to describe tasks in natural language and receive corresponding code outputs [5][6] - Multi-agent systems are anticipated to play a significant role in automating complex tasks and optimizing workflows in software development [6][7] Group 3: Innovations in AI Programming Tools - Cognition AI has introduced Devin, the first AI programmer capable of managing the entire software development lifecycle autonomously, outperforming existing models like GPT-4 in real-world problem-solving [6] - AI-driven integrated development environments (IDEs) like Cursor simplify the coding process by allowing natural language input to generate and modify code [5][6] - The rise of low-code and no-code platforms is enabling non-programmers to participate in software development, further broadening the scope of who can engage in coding [7]

RT s4mmy (@S4mmyEth)If you could build an AI agent for any industry, what problem would it solve? ...

Core Insights - Capgemini is set to acquire WNS for $76.50 per share, totaling $3.3 billion, which represents a premium of 28% over the last 90-day average share price [2][5] - The acquisition is expected to enhance Capgemini's normalized EPS by 4% in 2026 and 7% in 2027 post-synergies [2][20] - The transaction has been unanimously approved by the boards of both companies and is anticipated to close by the end of the year [5][22] Strategic Rationale - The acquisition aims to create a leader in Intelligent Operations, leveraging Agentic AI to transform business processes [3][12] - Capgemini will gain scale and vertical expertise to capitalize on the shift from traditional Business Process Services (BPS) to AI-powered operations [3][5] - WNS's high-growth Digital BPS will enhance Capgemini's service offerings and increase its presence in the US market [4][10] Financial Impact - The combined entities are projected to generate revenues of €1.9 billion in Digital BPS by 2024 [10] - WNS has shown a consistent revenue growth of approximately 9% over the last three fiscal years, reaching $1,266 million in fiscal year 2025 with an 18.7% operating margin [8][9] - Capgemini expects revenue synergies of €100 million to €140 million and cost synergies of €50 million to €70 million by the end of 2027 [20] Market Positioning - The acquisition positions Capgemini to meet the growing demand for AI-driven business process transformation, addressing client needs for efficiency and agility [3][12] - The combination of Capgemini and WNS will create a robust platform for innovation and growth, enhancing their competitive edge in the market [6][17] - Both companies share a vision for Intelligent Operations, which will facilitate a smooth integration and unlock new opportunities for clients [6][21]

这里正是虎嗅智库首期"AI落地研学营"的现场。当零售业面临同质化竞争和人力成本高企时，AI Agent 正从一个技术名词，变成能真正帮上忙的工具。 6月24日、25日，虎嗅智库首期AI落地研学营带队走进飞书和智谱。在为期一天半的研学营里，虎嗅智库邀请了来自飞书、来也科技、智谱的五位一线操盘 手及实战专家，聚焦零售业最让人头疼的三个老大难问题—库存积压、人力效率低、决策反应慢，通过剖析大量真实案例和技术讲解，展示了AI Agent如何 在这些环节带来的具体解法。 扎进业务深水区，瞄准痛点 在北京字节总部的一间会议室里，伊利、海底捞、小仙炖、Lola Rose等二十多家零售企业的高管们，正专注地看着屏幕上的两个案例：一家女装品牌借 助"AI买手系统"，让试点门店的售罄率提升17%；另一家大型医药企业，利用RPA+Agent处理全国5000家门店复杂的医保数据，把对账错误率从30%降到 10%。 "AI落地不是技术单兵突进，而是组织协同能力的升级。"首位分享的嘉宾，飞书大消费行业客户成功总监周虹宇开门见山。他提出的"五个关键问题"直指企 业痛点，比如"企业要不要赶紧上AI？"、"AI和人脑思维到底差在哪？"、"数据 ...

重温《英伟达 GTC 2025》：挖掘 AI 算力需求预期差？ 20250706 摘要 美股算力领域表现突出，主要由推理和训练需求共振驱动，需关注大模 型和应用，而非仅依赖产业链数据。GTC 大会参会人数增加，AI 产业人 士占比提升，表明其对 AI 产业的重要性增强，蕴含大量信息差和预期差。 算力需求空间与 TOKEN 量密切相关，计算需求不断增加推动了这一趋 势的发展。海外算力公司的涨幅显著，仅靠传统业绩思路无法解释，需 深入分析 TOKEN 量如何影响计算需求，以及这些因素如何驱动未来趋 势。 Agentic AI 是推理模型衍生出的 AI 范式，强调任务分布执行和规划， 以完成某个任务为终极目标，通过拆解、分布、规划和执行来实现，能 够处理连续、多步骤的复杂或简单任务。 黄仁勋指出，现在不仅有预训练阶段，还有后训练（post training）和 测试时间（test time），这三个阶段都存在算力需求通胀，因此现在有 三条 skin law 曲线。 Q&A 全球 AI 算力跟踪的现状如何？与以往相比有哪些变化？ 当前全球 AI 算力的跟踪方式与以往有显著不同。过去主要通过产业链数据进行 跟踪，但这种 ...

RT s4mmy (@S4mmyEth)If you could build an AI agent for any industry, what problem would it solve? ...

RT s4mmy (@S4mmyEth)If you could build an AI agent for any industry, what problem would it solve? ...

If you could build an AI agent for any industry, what problem would it solve?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. ...

🤖🤖🤖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. ...