Company Overview & Vision - Aliv is building consumer software products aiming to improve users' lives [3] - The company's vision is to create a portfolio of "one person billion-dollar companies" [34] - Aliv emphasizes a lean startup approach, focusing on small teams and early profitability [1][2] Key Achievements & Metrics - Aliv scaled a portfolio of products to $6 million in ARR (Annual Recurring Revenue) profitably [3] - The company has generated over 500 million views across social media [3] - One product, Unstuck AI, reached 1 million users in under nine weeks [8] - Another product launch saw 10,000 users in less than 30 hours [4] Lean Operating Principles - Prioritizes hiring "10xer generalists" with complementary skills [10][11] - Emphasizes a "profit-first mentality" to guide decision-making [11][12] - Focuses on continuous process refinement and learning from failures [13] - Leverages "super tools" by reinventing the ways to use old tools and consolidating workflows [14][15] - Believes in building compounding benefits through technical playbooks and operational blueprints [14][15] Organizational Structure - Adopts a "harvester and cultivator" model for its engineering organization, inspired by Palantir [21][22] - Harvesters are product engineers who own and manage their products end-to-end [22][23] - Cultivators are AI software engineers focused on building the company's agentic operating system and automation [24] AI Tooling & Automation - Uses AI tooling to augment existing talent, not to compensate for shortcomings [25] - Implements a three-stage automation strategy: human-led tooling, workflow automation, and autonomous decision-making systems [28][29][30] - Aims to build a company where strategic insights are provided by people, but operations are run by AI agents [30] - Explores using AI agents for market research, acquisition target scoring, and growth system automation [30][31]

[Music] My name is Zach. I'm a Genai software engineer at Prosper Marketplace. We're a financial services company that connects people with the financial solutions they need.Everything from personal loans to credit cards, HELOC, and so on. The goal of the AI team is to free up time as much as possible by automating very manual and tedious work and hopefully cut down on costs at the same time, which involves building a very flexible and modular AI agents platform. One of the bigger problems we tackled recent ...

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如果一个技术用3年时间，从默默无闻到改变世界，我们称其为第N次工业革命； 如果这个技术从论文到落地的过程中，头部玩家如走马灯更迭，百亿流量砸下去只听一声响就陷入沉寂，我们称其为资本绞肉机； 如果这个技术集齐了以上所有特质，还能以常规技术乘十的速度进化，让互联网时代的生态护城河、资金壁垒、规模效应全部失效，创业公司也能站在世 界舞台中心，那么，它是大模型。 而这一趋势，自今年年初DeepSeek"掀桌"以来，变得尤为明显。时至今日，在大模型行业，能留在桌子上的企业唯一的生存法则就是——创新至上。 那么，如何理解大模型行业的创新至上？为什么传统互联网的玩法在大模型时代会彻底失效？为什么大模型的淘汰，能以季度为单位发生？ 刚刚结束的MiniMax Week，或许正是我们审视这些问题的最佳切入点。 以此为切口，你会看见一个创新行业的创业公司如何挣脱巨头的射程；可以看到大模型从技术创新到改变世界之间的桥梁如何搭建；以及，一群"偏执"的 人，如何在这个一切被按下加速键的行业中破浪前行。 一只跳水猫咪，如何让世界Aha 一开始，MiniMax Week的关注度，还局限在大模型行业内的讨论：这次MiniMax会拿到多少SOT ...

Core Viewpoint - The article discusses the transformative impact of large models in the tech industry, emphasizing that innovation is the key survival strategy for companies in this space, especially in light of the rapid evolution and competition among startups and tech giants [2][3][14]. Group 1: Industry Trends - The large model industry is experiencing a significant shift towards innovation, with traditional internet business models becoming obsolete [3][4]. - The recent "Aha Moment" in the industry, exemplified by viral videos of animals performing complex actions, highlights the advancements in video AI technology and its potential [7][8]. - The MiniMax Week event serves as a critical point for examining how startups can thrive amidst competition from larger firms [4][6]. Group 2: Technological Innovations - MiniMax's Hailuo 02 model has seen a threefold increase in parameters compared to its predecessor, achieving native 1080P resolution and generating 10 seconds of high-definition content [9][10]. - The model's innovative NCR architecture allows for efficient resource allocation, significantly reducing memory read/write by over 70% and improving training and inference efficiency by 2.5 times [12][23]. - MiniMax's M1 model, with 456 billion parameters, supports the longest context length in the industry, enhancing its performance in complex tasks [16][18]. Group 3: Competitive Landscape - Despite the initial dominance of tech giants in the large model space, startups like MiniMax have captured significant market share and achieved top rankings in performance benchmarks [15][16]. - The article notes that the rapid evolution of large models requires companies to continuously innovate to maintain a competitive edge, as capital alone is insufficient for success [14][15]. - MiniMax's innovative approaches, such as the use of mixed attention mechanisms and the CISPO training method, have allowed it to outperform competitors while reducing costs [20][21][23]. Group 4: Agent Applications - The emergence of agent applications, such as MiniMax Agent, represents a new frontier in AI, enabling more complex task execution and planning capabilities [30][32]. - MiniMax Agent has been integrated into daily operations, demonstrating its effectiveness in various tasks, including programming and content creation [31][32]. - The synergy between large model innovations and agent applications is expected to drive further growth and development in the AI ecosystem [32][34].

近日，在 36氪WAVES 举办的大会上，一个有趣的环节引发了人们的热议：主办方让Kimi与Minimax两家的投资人进行了对谈。 随着 DeepSeek 的横空出世，整个中国大模型的牌局已天翻地覆。 行业龙头的格局，从原来的大模型六小龙，逐渐演变成了今天的基模五强。 当六小龙不再是市场的焦点时，安静很久的 Kimi 和 Minimax 在 前不久 不约而同有了新动作： Kimi 开源了编程模型 Kimi -Dev，它的第一个Agent Kimi - Researcher深度研究也开启小范围测试。而 Minimax 则开源了首个推理模型 Minimax -M1，并完成连续五天 包含大模型、视频生成、音频生成等多个方向 的更加 。 从产品侧来看，Kimi将重心聚焦到agent，以深度研究为主要方向，似乎有意向金融、学术等方向发力，这条路线虽然已经有了智谱等竞争者，但远离了 以生活服务为主的大厂射程，叠加原本不错的基础模型能力，Kimi似乎找到了自己的舒适区。 而另一边，Minimax则似乎想要弥补自身的遗憾，在没有接入DeepSeek之后，继续发力全方向的布局。 这似乎也意味着，大模型竞争进入下半场之后，更多的 ...

编译：zirong、haina 海外独角兽原创编译 转载请注明 AI Agent 最核心的价值，是能真正"上手"解决过去必须由人类处理的复杂业务问题。而在所有商业 场景中，客户服务无疑是这种价值最直接的试金石，每一次对话都直接关系到用户满意度、品牌声 誉乃至企业收入 。这也是 AI 客户服务赛道始终是兵家必争之地的原因。 近年来，从简单的聊天机器人到大型云平台（如 Amazon Lex、Google Dialogflow）提供的解决方案 层出不穷。然而，市场上的多数玩家提供的仍是"开箱即用"的标准化工具，难以应对真实世界中充 满噪音、打断和复杂逻辑的对话。这导致 AI 客服常常因机械、僵化而被诟病，同质化严重，真正能 为企业大规模解决核心问题的产品凤毛麟角。 而由前 Salesforce 联席 CEO Bret Taylor 创立的 Sierra AI，提供的不是辅助工具，而是能深度融入企 业流程、完全自主执行任务的"公司智能体"（Company Agents） ，Sierra 的理念是，AI 不应仅仅提 升生产力，更要成为企业的新劳动力。它通过高度定制化的方式，将企业的品牌、政策和工作流深 度整合，让 AI ...

Core Insights - Kimi's newly launched Deep Research feature is considered to be among the top three in the industry for its depth and efficiency in generating research reports [1][5][20] - The feature automates the process of information gathering and report generation, significantly reducing the time spent on research [4][18][17] Group 1: Functionality and Performance - Deep Research provides a structured framework for understanding complex questions and generates high-quality reports [5][7] - The feature utilizes both Chinese and English keywords, enhancing information coverage and accuracy [24][31] - Kimi's system plans and executes searches autonomously, correcting its strategies when necessary to ensure comprehensive data collection [36][38] Group 2: Technical Challenges and Innovations - Developing a Deep Research Agent involves overcoming significant technical challenges, particularly in managing real-world complexities and long-chain tasks [12][14][15] - Kimi's approach integrates coding capabilities, indicating that deep research and coding skills will be foundational for future general-purpose agents [22][45] Group 3: Market Position and Strategy - The current market environment favors companies like Kimi that focus on product quality and technical innovation rather than aggressive marketing tactics [48][50] - Kimi's strategy emphasizes long-term development of general intelligence, rather than short-term performance metrics [52]

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]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/AxraWu19RNAvi Chawla (@_avichawla):Postman's AI-readiness Playbook is one of the most important documents you can read today as a developer!We are headed into an era where every website must be "Agent-ready".- Agents will make purchases, not humans.- Agents will find the best options, not humans.- Agents https://t.co/ePqILv3UZX ...