Claude Whiteboards, Claude Sheets, and Claude Data
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Anthropic 推出了 Claude Whiteboards 和 Claude Sheets，目前已进入测试版；同时将 Claude Dashboards 更名为 Claude Data，现已全面可用。这些更新的目标是让工作流程始终留在 Claude 环境中，避免反复把输出搬到外部程序。通过让团队能够在平台内直接完成头脑风暴、构建与数据分析，用户现在可以在不丢失工作背景的前提下保持连续性，并实现实时协作。
Claude Whiteboards 提供了团队头脑风暴的共享空间。用户可以和 Claude 一起梳理初步方案，随后 Claude 会把想法整理成结构化看板，包含时间线、负责人和具体日期。该工具是可交互的：团队成员可以添加备注，Claude 再根据主题对备注进行分组，或将其转化为可执行事项。会议结束后，这些看板可导出到 Lucid 、 Miro 或 Vani by Zoho 等外部工具，以继续推进协作。
Claude Sheets 可根据简单的自然语言提示生成带有公式、格式和下拉选项的电子表格。它面向团队协作而设计，支持用户与 Claude 之间进行共同编辑。当前功能已覆盖常见需求；若用户需要更接近 Excel 的强大能力，可将内容导出为 .xlsx 或 CSV 格式，或迁移到 Airtable 、 Asana 和 Smartsheet 等合作平台以继续扩展。
Claude Data（原 Claude Dashboards）是一套扩展的工具，用于与来自 Amazon Redshift 、 BigQuery 和 ClickHouse 等数据源的公司数据进行交互。本次更新包含经认证的指标定义，用于避免口径冲突；支持创作数据驱动的故事；并提供实时共享，让同事无需直接访问底层连接器也能查看最新数字。此外，平台还加入了设计系统，改进缓存以加快加载速度，并记录变更以支持版本控制。
在这些特定工具之外，Claude 还支持交互式连接器：连接器会以完整应用的形式在对话旁边打开，用户可直接在 Claude 界面中使用 Adobe 、 Notion 或 Atlassian 等工具。这些功能适用于所有付费计划，并为团队与企业管理员提供相应设置以管理访问权限。通过这一系列更新，平台力求带来更紧密的一体化体验，让用户能够以"所想即所得"的效率完成各类专业任务。
Anthropic has introduced Claude Whiteboards and Claude Sheets, now available in beta, while rebranding Claude Dashboards as Claude Data, which is now generally available. These updates aim to keep workflows within the Claude environment, eliminating the need to constantly move outputs to external programs. By allowing teams to brainstorm, build, and analyze data directly inside the platform, users can now maintain continuity and real-time collaboration without losing the context of their work.
Claude Whiteboards functions as a shared space for team brainstorming. Users can walk through rough plans with Claude, which then organizes ideas into structured boards featuring timelines, owners, and specific dates. The tool is interactive, enabling team members to add notes that Claude can subsequently group by theme or turn into actionable items. Once a session is complete, these boards can be exported to external tools such as Lucid, Miro, or Vani by Zoho to continue the collaboration process.
Claude Sheets enables the generation of spreadsheets equipped with formulas, formatting, and drop-downs based on simple natural language prompts. Designed for team use, these sheets allow for collaborative editing between users and Claude. While the current functionality covers standard needs, users can export their work to .xlsx or CSV formats if they require the more robust capabilities of Excel, or they can transition into partner platforms like Airtable, Asana, and Smartsheet for further development.
Claude Data, formerly known as Claude Dashboards, serves as an expanded suite for interacting with company data from sources like Amazon Redshift, BigQuery, and ClickHouse. This update includes features such as certified metric definitions to prevent conflicting data, the ability to craft data-driven stories, and live sharing so colleagues can view up-to-date numbers without needing direct access to underlying connectors. The platform also now supports design systems, includes improved caching to speed up loading times, and tracks changes to allow for version control.
Beyond these specific tools, Claude now supports interactive connectors that open as full applications alongside conversations, allowing users to work in tools like Adobe, Notion, or Atlassian directly from the Claude interface. These features are available on all paid plans, with specific settings for team and enterprise administrators to manage access. Through these updates, the platform aims to provide a more integrated experience that allows users to work at the speed of thought across various professional tasks.
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• Claude Design 以及类似的"Claude-native"工具，作为面向 Agent 的精简型替代方案，尽管缺少高级功能，但在快速原型和个人项目中仍展现出高效性。
• 用户群体意见分化明显：有人把这类工具当作省事的"后备"界面；也有人已经把工作流完全迁移过来，他们认为基于 Agent 的设计或基于代码的交互，胜过传统 UI 的操作方式。
• 新一代 AI-native 生产力套件延续了 Google Docs 对抗 Microsoft Office 的历史式颠覆路径：先凭借简洁性与协作性切入此前服务不足的场景，待其成熟后再逐步扩展到更广泛的竞争领域。
• 行业观察人士指出，AI labs 正在努力打造"围墙花园"，以提升"粘性"、降低流失率，让商业模式从售卖原始智能转向提供集成的端到端办公空间。
• 由 AI providers 直接开发独立的生产力应用，使其能够掌控呈现层和数据生命周期，从而形成相对优势；而通用工具往往需要频繁切换上下文。
• 对厂商锁定的担忧正在加剧：当专有业务逻辑与公司数据被纳入 AI provider 的生态系统时，转换成本会变得高昂，未来可能因此限制模型与系统的互操作性。
• 怀疑论者对长期可行性并不乐观，认为 AI labs 通过推出大量"副项目"，把可能迁移到其 API 之上的开发者分流出去，正面临类似"Anthropic Graveyard"的风险。
• 尽管像 Excel 这样的传统软件因复杂需求和组织惯性而在企业中根深蒂固，但 AI 生成的代码与自定义应用正越来越多地取代过去依赖电子表格公式来完成的手动工作。
• 这些工具的实际效果很大程度取决于它们能否提供比基于提示词的交互更直观的"第一方"体验；不过现阶段仍存在数据幻觉、格式不一致等问题。
• 更广泛的市场策略正在转向"所有能力都在一处"的模式，使得普通用户眼中，AI assistant 与生产力应用之间的边界变得越来越模糊。
核心争议在于：专业软件与直接构建在 AI platforms 上的新式 Agent 驱动界面之间的张力。尽管怀疑者强调厂商锁定、资源分散，以及取代像 Excel 或 Figma 这类深植企业的工具难度很大，仍有不少用户表示，在处理快速任务时使用这些 AI-native 替代方案，能带来显著的生产力提升。归根结底，这场讨论指向一个趋势：未来的竞争格局可能不再围绕功能丰富的软件套件展开，而是转向那些最擅长管理数据与 agentic workflows 的平台——在这种平台上，"应用程序"或许会变成按需生成的即时产物。 • Claude Design and similar "Claude-native" tools function as streamlined, agent-centric alternatives to professional software like Figma or Excel, proving highly effective for rapid prototyping and solo projects despite their lack of advanced features.
• A significant divide exists between users who view these tools as convenient "fallback" interfaces and those who have fully migrated their workflows, arguing that agent-driven design or code-based interaction is superior to traditional UI manipulation.
• New AI-native productivity suites mirror the historical disruption pattern of Google Docs against Microsoft Office, where simplicity and collaboration initially capture underserved use cases before maturing into broader competition.
• Industry observers note that AI labs are aggressively building walled gardens to increase "stickiness" and reduce churn, shifting the business model from selling raw intelligence to providing integrated, end-to-end workspaces.
• Developing standalone productivity apps allows AI providers to control the entire presentation layer and data lifecycle, which offers a competitive advantage over general-purpose tools that require constant context switching.
• Concerns regarding vendor lock-in are mounting, as storing proprietary business logic and company data within an AI provider's ecosystem creates high switching costs that may eventually limit model interoperability.
• Skeptics question the long-term viability of these efforts, suggesting that AI labs risk "Anthropic Graveyard" scenarios by launching numerous side-projects that could alienate developers building on their APIs.
• While legacy software like Excel remains entrenched in enterprise environments due to complex requirements and institutional habits, AI-generated code and custom apps are increasingly replacing manual tasks previously handled by spreadsheet formulas.
• The effectiveness of these tools relies heavily on whether they offer a "first-party" experience that is more intuitive than prompt-based interaction, though current implementations still struggle with issues like hallucinated data and inconsistent formatting.
• The broader market strategy is transitioning toward an "everything-in-one-place" model, where the distinction between an AI assistant and a productivity application becomes increasingly blurred for the average user.
The conversation centers on the tension between specialized professional software and new, agent-driven interfaces built directly into AI platforms. While skeptics highlight the risks of vendor lock-in, resource dispersion, and the difficulty of displacing deeply embedded enterprise tools like Excel or Figma, many users report significant productivity gains from adopting these AI-native alternatives for quick tasks. Ultimately, the discussion suggests that we are moving toward a future where "apps" may become transient artifacts generated on demand, potentially shifting the competitive landscape from feature-rich software suites to platforms that best manage data and agentic workflows.