这家总部位于旧金山的初创公司在 2 月仅凭 4 人团队（ Jayaprakash 、联合创始人 Aayush Shah 和 Aditya Maru 及一名产品设计师）就实现了 100 万美元年度经常性收入（ ARR ）。 在 AI 驱动的软件世界中，速度正成为决定性要素， Blacksmith 在种子轮融资仅四个月后，又获得了由谷歌风投领投的新一轮融资，以加速代码部 署流程。 这笔 1000 万美元的 A 轮融资仅用 14 天就完成交割，谷歌风投继 5 月领投 Blacksmith 350 万美元种子轮后再次加注。 此前， Alphabet 旗下这支风投机构曾看好市场体量及创始团队背景——成员中有来自另一家 GV 投资组合公司 Cockroach Labs 的资深人士。但本 轮融资中， GV 更看重实际业绩表现。 Blacksmith 为开发者提供持续集成与持续交付服务，与 GitHub Actions 形成互补。联合创始人兼 CEO Aditya Jayaprakash （上图左）在独家专访中 透露， 自 5 月以来已吸引数百家客户，而 AI 编程助手的爆发式增长彻底打开了市场空间。 Jayaprakash ...

Core Insights - The article discusses how entrepreneurs can leverage exponential forces and build network effects to create lasting value in the tech industry [3][4][5] Group 1: The Power of Networks and Network Effects - Many significant internet services are networks that become more valuable as more people use them, exemplified by email and social media platforms like Facebook and Instagram [5][6] - The tech industry benefits from powerful exponential forces, such as Moore's Law, which states that semiconductor performance doubles approximately every two years, leading to rapid advancements [6][7] - Entrepreneurs should focus on identifying these exponential forces, as they will dominate any tactical product work [6][10] Group 2: Strategies for Building Networks - Successful companies often start with a strong product that attracts users, then leverage existing networks to grow, as seen with Instagram and Substack [10][11] - The challenge lies in making networks useful from the beginning, as initial user bases can be small and unappealing [12] - The emergence of "narrow startups" that charge premium prices for specialized services indicates a shift towards more focused business models in the tech landscape [23] Group 3: The Role of Branding and Pricing - Brand power and consumer inertia are significant in the tech sector, as seen with ChatGPT's rapid rise to prominence despite lacking traditional network effects [15][21] - The increasing willingness of consumers to pay higher prices for software suggests a shift in spending priorities, with software potentially consuming a larger share of disposable income [14][21] Group 4: The Impact of AI and Open Source - The rise of AI tools has diminished the need for traditional web traffic, leading to a decline in SEO-driven traffic for many websites [20][21] - Open source software has played a crucial role in democratizing technology, allowing startups to thrive with minimal initial investment [35][36] - The future of open source AI remains uncertain, with potential for it to lag behind proprietary models, but it could provide affordable solutions for consumers [36][37]

"突然间，我从只需要处理加州客户的税务问题，变成要应对 40 多个州的规定，"罗斯表示，"这非常痛苦。" 2023 年，当罗斯申请加入初创企业加速器 Y Combinator 时，他的前 Airbnb 上司、现合伙人古斯塔夫·阿尔斯特伦鼓励他，创建一家能自动化处 理销售税管理全流程的初创公司。 Numeral 联合创始人兼首席执行官 Sam Ross 在结束 Airbnb 产品经理工作后环游世界期间，萌生了创立这家销售税合规初创企业的想法。 周四， Numeral 宣布完成 3500 万美元的 B 轮融资，这家成立两年的公司估值达到 3.5 亿美元。 本轮融资由 Mayfield 领投， Benchmark 、 Uncork Capital 、 Y Combinator 以及电子 DJ 组合 The Chainsmokers 成员联合创立的 VC 机构 Mantis 等跟投，距离其 1800 万美元 A 轮融资仅过去六个月。 "那是在 2018 年初，当时还没有像今天这样普遍的远程工作，"他在接受 TechCrunch 采访时说道。直接面向消费者的商业模式当时正盛行，因此他 通过在旅途中运营几家自筹资金的电商 ...

9 月 19 日，多模态赛道明星创业公司生数科技宣布，该公司近日完成数亿元人民币规模的 A 轮融资。 新一轮融资由博华资本领投，百度战投、北京市人 工智能产业投资基金、启明创投、达泰资本、卓源亚洲、 BV 百度风投等老股东持续跟投，建发新兴投资等产业合作方加码入场。此前，该公司已完成包 括天使轮、天使 + 轮、 Pre-A 轮在内的三轮融资，投资方包括启明创投、蚂蚁集团、百度战投、北京市人工智能产业投资基金、 BV 百度风投、锦秋基 金、星连资本、达泰资本、卓源亚洲、卓源资本、中关村科学城等知名机构。 生数科技首席执行官骆怡航博士表示，多模态生成技术在数字内容产业中的商业化进程正在加速，但目前仍处于早期阶段。预计在未来三年内，多模态生 成将重塑全球数字内容的生产方式，全面渗透到各行各业的内容生产与消费环节，展现出巨大的市场空间与全球性增长潜力。新一轮融资将用于模型研发 和技术创新，探索多模态大模型的智能上限和应用广度，同时将持续加强产品拓展、用户服务、产业合作和全球商业布局。 作为多模态赛道的知名公司，生数科技的每一步动态都引发行业关注。 2023 年 4 月，该公司发布中国首个全面对标 Sora 的视频大模 ...

Core Insights - Irregular, an AI security company, has raised $80 million in a new funding round led by Sequoia Capital and Redpoint Ventures, bringing its valuation to $450 million [1] Group 1: Company Overview - Irregular, formerly known as Pattern Labs, is a significant player in the AI assessment field, with its research cited in major AI models like Claude 3.7 Sonnet and OpenAI's o3 and o4-mini [2] - The company has developed the SOLVE framework for assessing model vulnerability detection capabilities, which is widely used in the industry [3] Group 2: Funding and Future Goals - The recent funding aims to address broader goals, focusing on the early detection of new risks and behaviors before they manifest [3] - Irregular has created a sophisticated simulation environment to conduct high-intensity testing on models before their release [3] Group 3: Security Focus - The company has established complex network simulation environments where AI acts as both attacker and defender, allowing for clear identification of effective defense points and weaknesses when new models are launched [4] - The AI industry is increasingly prioritizing security, especially as risks from advanced models become more apparent [4][5] Group 4: Challenges Ahead - The founders of Irregular view the growing capabilities of large language models as just the beginning of numerous security challenges [6] - The mission of Irregular is to safeguard these increasingly complex models, acknowledging the extensive work that lies ahead [6]

图片来源： CodeRabbit CodeRabbit 完成 6000 万美元融资，这家成立两年的 AI 代码审查初创公司估值达 5.5 亿美元 投资者对这家初创企业的增长态势感到兴奋。周二， CodeRabbit 宣布完成 6000 万美元 B 轮融资，公司估值达 5.5 亿美元。 本轮融资由 Scale Venture Partners 领投，英伟达风投部门 NVentures 及包括 CRV 在内的现有投资方跟投，使得公司融资总额升至 8800 万美元。 CodeRabbit 正帮助 Chegg 、 Groupon 、 Mercury 等 8000 多家企业节省代码审查这项 notoriously frustrating 任务的时间——随着 AI 生成代码的兴起，这 项工作变得更加耗时。 哈乔特 ·吉尔（ Harjot Gill ）在 2018 年将他的第一家初创公司 Netsil 出售给 Nutanix 后，又与他人共同创立了可观测性初创公司 FluxNinja 。几年后，他 注意到一个有趣的现象。 "我们有一个远程工程师团队，他们开始在 GitHub Copilot 上采用 AI 代码生成功能，" ...

Core Insights - The article discusses the founding of Kepler by Ashton Teng and Quinn Leng, aiming to revolutionize scientific workflows through AI, particularly in the life sciences sector [2][3][5]. Group 1: Company Overview - Kepler is designed to address the inefficiencies in life sciences data analysis, where scientists often wait days or weeks for results, thus slowing down scientific iteration cycles [3][19]. - The company positions itself as the "central nervous system" for research organizations, facilitating literature searches, experimental ideas generation, and data analysis [3][27]. - Kepler's AI Agent can handle multiple queries simultaneously, enhancing the speed and breadth of scientific exploration [3][28]. Group 2: Market Opportunity - The life sciences sector is technologically underserved, with data analysis capabilities lagging behind other tech industries, creating a significant market opportunity for specialized AI solutions [3][19]. - Major pharmaceutical companies are increasingly seeking partnerships with AI startups like Kepler, marking a shift in the industry where collaboration with startups was previously uncommon [7][41]. - Kepler's first client was secured within a week of its founding, indicating a strong demand for specialized AI solutions in the biotech field [5][36]. Group 3: Competitive Landscape - Kepler differentiates itself from general AI providers like OpenAI by focusing on the specific needs of the life sciences sector, addressing the "last mile" problem of integrating AI into existing workflows [5][41]. - The company faces competition from other startups and established firms like Palantir, but its unique focus on life sciences and execution capabilities provide a competitive edge [41][42]. Group 4: Future Aspirations - Kepler aims to expand its technology beyond life sciences into other fields such as materials science, climate science, and agriculture, reflecting the limitless potential of scientific exploration [7][43]. - The company envisions becoming the "central nervous system" for every research organization, emphasizing the need for specialized AI agents tailored to research tasks [43][44]. Group 5: Challenges and Innovations - Kepler is tackling complex challenges in processing large-scale multimodal data, requiring innovative solutions for effective scientist-AI interaction [31][32]. - The company is focused on creating a user-friendly interface that allows scientists to interact with the AI Agent seamlessly, addressing the unique demands of scientific research [34][35].

让普通人轻松拥有 " 工业级 "AI 能力 Teable 在社交媒体 X 上的发布帖 过去两年， AI 的进步令人振奋，但对许多一线团队而言，落地仍像一堵看不见的墙：工具要拼、数据在割裂、开发资源也紧缺。 Teable 的做法，是把 AI 前置为数据库的原生能力 ，让非技术同学也能 " 说句人话就开干 " 。从零开始描述业务目标，系统即可自动生成 所需的表结构与关联 ；随后你可以直接 与数据表对话 ，一键生成可上线的 业务应用或落地页 ，并与 权限、审计、自动化 保持同一技术底座与治理体系。发票、合同、简历等文件可被 对话式抽 取为结构化数据 并回写到库中；分析环节无需 SQL ，问一句就能得到 指标与可视化 。一句话概括： 让一切围绕数据的工作变得极致简单，数据直达结 果，告别手搓表格与多工具反复折腾 。 Teable 2.0 多维表格智能体 产品发布视频 AI Agent 赛道新成员 Teable 近日宣布完成数百万美元天使轮融资，投资方包括 真格基金 (Zhen Fund) 、 BV 百度风投（ Baidu Ventures ）与祥峰投资（ Vertex Ventures ） 。北美时间 9 月 16 ...

这一数字远超此前传闻 ——今年 7 月消息泄露时，有报道称 Groq 正以近 60 亿美元的估值筹集约 6 亿美元资金。 本轮融资由投资公司 Disruptive 领投，黑石集团、 Neuberger Berman 、德国电信资本合伙公司等机 构追加投资。三星、思科、 D1 和 Altimeter 等现有投资方也参与了本轮融资。 除研发芯片外， Groq 还提供数据中心算力服务。该公司曾于 2024 年 8 月以 28 亿美元估值融资 6.4 亿美元 ，这意味着其估值在约一年间增长超一倍。据 PitchBook 估算， Groq 迄今融资总额已超 30 亿美元。 Groq 之所以备受资本追捧，是因为其正致力于打破英伟达对 AI 芯片领域的垄断格局。与主流 AI 系 统采用的图形处理器 (GPU) 不同， Groq 将其芯片命名为语言处理单元 (LPU) ，并把其硬件称为 " 推理引擎 " ——这种专门优化的计算机能实现 AI 模型的高速高效运行。 其产品面向开发者和企业，可作为云服务或本地硬件集群使用。本地硬件是一种配备了集成硬件 / 软 件节点堆栈的服务器机架。无论是云端还是本地硬件，都运行着 Meta 、 ...

Anthropic 、 OpenAI 等人工智能开发公司正在让大型语言模型 " 上班办公 " 。 这些 AI 模型正在学习使用从 Salesforce 的客户关系管理软件到 Zendesk 的客户支持系统，再到 Cerner 的医疗记录应用等各种工具。其目的是教会 AI 如何处理白领工作者所面临的一些复杂任务。 这种训练模式与 AI 模型以往的任何训练都不同。研究人员为 AI 提供模拟应用程序进行交互练习，同时聘请各领域专家向模型示范如何操作这些应 用。 这些技术的成本并不低廉。据一位知情人士透露， Anthropic 高管内部讨论过未来一年将斥资 10 亿美元打造这些 " 企业应用克隆体 " ——也被称为 强化学习环境或训练场。 雇佣生物学、软件编程和医学等领域的人类专家来教导模型学习新知识及办公软件操作，其成本也日益攀升。 OpenAI 今年早些时候预测，计划今年在数据相关成本上支出约 10 亿美元（包括支付人类专家费用和强化学习训练场）， 到 2030 年这一数字将攀 升至 80 亿美元。 若取得成功，这些 AI 训练方法或能帮助 OpenAI 和 Anthropic 突破传统训练技术近期遭遇的部分局限 ...