AI Practical Use #3#: Let AI help you with Excel data analysis. AI 实用玩法第 3 个： 让 AI 帮你做 Excel 数据分析。 Here is a very common office situation: You have an Excel file with sales data, costs, profit, regions, products, and dates. Normally, you may spend 2 hours writing formulas, checking data, making summaries, and building charts. But with AI, you can finish the first draft in about 10 minutes. 一个很常见的办公场景： 你手里有一份 Excel 数据， 里面有销售额、成本、利润、区域、产品、日期。 以前你可能要花 2 小时： 写公式、查数据、做汇总、看趋势、做图表。 现在可以先交给 AI， 10 分钟生成初步分析结果。 You don’t need to manually type every complex formula. Let AI help you: Build formulas Summarize key findings Find abnormal data Compare trends Suggest chart formats Create a report structure 你不需要自己一个个输入复杂函数。 可以让 AI 帮你： 生成公式 总结关键结论 找出异常数据 对比趋势变化 建议图表形式 生成汇报框架 Here is a simple prompt: 这里有一个简单提示词： Please analyze this Excel data. Help me build the right formulas, summarize the key findings, find possible errors or abnormal values, and suggest the best chart or report format. I will review and verify the final results. 中文版本： 请分析这份 Excel 数据。 帮我生成合适的公式，总结关键结论，找出可能的错误或异常值，并建议最适合的图表或汇报格式。 最终结果由我来审核确认。 The key idea is simple: AI does the heavy first draft. You review the logic and final result. 核心思路很简单： AI 负责先把复杂工作做出来， 你负责审核逻辑和最终结果。 Before: 2 hours manually writing formulas. After: 10 minutes with AI assistance. 以前： 手动写公式、做分析，可能要 2 小时。 现在： 借助 AI，10 分钟先完成初稿。 AI is not here to replace your judgment. It helps you save time on repetitive work, so you can focus on checking, thinking, and making better decisions. AI 不是替代你的判断力。 它是帮你节省重复劳动的时间， 让你把精力放在审核、思考和决策上。 Let AI write the formulas. You review the results. 让 AI 写公式， 你负责审核结果。 That is a smarter way to work. 这才是更聪明的办公方式。 #ChatGPT# #AI# #AITools# #Excel# #ExcelTips# #DataAnalysis# #Productivity# #WorkSmarter# #OfficeWork# #BusinessTools# #Automation# #DigitalTools# #TechTips# #FutureOfWork# #PromptEngineering#

. @Silicon_Data and @computeexchange were both built after the ChatGPT moment. But I still wouldn’t call either company truly AI-native—yet. Being founded in the AI era doesn’t automatically make an organization AI-native. Giving every employee access to ChatGPT certainly doesn’t. I’ve been thinking about the organizational structures of both companies, and the exercise has made me realize that AI-native organizations will not all look the same. @Silicon_Data is organized around building the independent reference layer for the compute economy: data infrastructure, indices, benchmarking, research, product commercialization and market adoption. @computeexchange is organized around creating liquidity: sourcing, verification, pricing, matching, contracting and settlement. Agents can transform both companies—but differently. At @Silicon_Data, agents can accelerate data analysis, research, product development, content production and customer intelligence. At @computeexchange, they can automate inventory normalization, provider onboarding, RFQs, matching and transaction workflows. This has also changed how I think about organizational design. Traditional companies are built around people, roles and reporting lines. Knowledge is distributed across individual brains, inboxes, documents, Slack channels and meetings. In that sense, a human organization is web-based: every person is a node, and work moves through the relationships connecting those nodes. An agent organization may be fundamentally different. It is Brain-based. Instead of every agent holding a fragmented version of the company, agents can operate from a centralized institutional Brain containing shared knowledge, history, decisions, priorities, permissions and real-time operating context. Each Brain sits a task-ownership system. Instead of asking, “Whose job is this?” the organization asks: What needs to be accomplished? What context and authority does it require? Should a human, an agent or a human-agent team own it? What constitutes completion? Who remains accountable? Humans continue to operate through networks of relationships, judgment, negotiation and trust. Agents operate through centralized knowledge, shared context and structured task ownership. The task layer tells you what needs to happen, who—or what—owns it, and whether it has actually been completed. To me, becoming AI-native means continuously redesigning this boundary between people, agents, knowledge and work. We are still experimenting. I’ll share what works, what fails, and how the two organizations evolve.

Our channel data analysis tool compares the number of small boat crossings & English channel conditions in present and past years. Despite the nice weather, we can see from the data that 2025 YTD had the more favourable conditions for small boats. More calm days, less rough days

While AI is enabling more sophisticated data analysis in the quantitative investment space, it is also creating new inefficiencies. On the Goldman Sachs Exchanges podcast, Osman Ali, global co-head of Quantitative Investment Strategies in Asset Management, discussed his observations:

In 2013, data analysis at Wayfair was trapped on on-premise SQL Server boxes that couldn't even join across databases. Over the following decade, cloud data warehouses and the modern data stack tore down constraint after constraint, turning data scientists from reporters into operational decision-makers. 📈 Now AI has made producing an analysis nearly free. But in "The Shape and Feel of the Post-AI Data Stack," Ian Macomber argues that agreeing on reality never gets cheaper. The more dashboards proliferate, the more likely different interfaces return different answers to the same question — a growing "consensus divergence" problem. 🤖 The post-AI data stack Macomber describes puts an agent harness between infrastructure and people: agents parse data products and reassemble insights for humans, not the other way around. That demands four things — artifacts agents can read, tools agents can operate via API, context that stays agent-agnostic across vendors, and consensus that can be systematically tested. Ramp, he notes, fires identical questions through every interface and measures how often the answers diverge, aiming for zero. ✍️ Macomber's conclusion: a data scientist's worth will be measured not by any single analysis, but by how well their judgment gets encoded into infrastructure so every future agent, decision, and employee inherits what's true and why. URL: #DataStack# #AIAgents#

The @MercedesAMGF1 team relies on AMD to advance data analysis, simulations, and engineering decisions that shape every race. From split-second strategy to long-term performance gains, AMD helps keep Mercedes-AMG F1 ahead of the competition. See how the most trusted companies trust AMD:

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NEW: Russian analyst Alexei Rogozin warns that Palantir has helped Ukraine integrate AI-driven data analysis with long-range UAV strike systems. He argues the real challenge is not just air defense, but the intelligence chain behind the strikes: satellite imagery, reconnaissance, digital traces, target analysis, and strike assessment. Palantir $PLTR does not select targets itself. Its role is to fuse fragmented data into a unified operational picture. Ukraine’s Delta system then links UAVs, sensors, units, and strike assets into one network. The result: faster targeting cycles and more precise long-range attacks. Tuapse oil terminal burns.