The number 1 appears as the first digit far more often than 9 in many naturally occurring datasets. That sounds wrong. If numbers were distributed uniformly, each digit should appear first about 11.1% of the time. But in many real world datasets, 1 appears first roughly 30% of the time. This is Benford’s Law. It appears in places like populations, financial figures, physical measurements, and accounting data where numbers span multiple orders of magnitude. The important part isn’t the 30%. It’s the question Why does the distribution look so unnatural? Because many real world quantities grow multiplicatively rather than being randomly selected from a fixed range. That makes the first digit carry information about the underlying process. For traders, there’s a useful implication Data can contain structure that isn’t visible in averages, charts, or headline statistics. Sometimes the strange thing isn’t the number. It’s the distribution hiding behind it. And a dataset can tell you something about how it was generated before you analyse what it says. #Statistics# #QuantTrading# #Trading# #DataAnalysis#

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#

🔺Revealing "Tiger King" Chase Coleman's Latest Holdings! Technology 💼 Managing $266 billion, who is Chase Coleman? Chase Coleman is the founder and investment manager of the hedge fund Tiger Global Management, renowned for heavily investing in high-growth technology companies. This chart showcases his U.S. stock portfolio as of the first quarter of 2025, with total assets reaching $266 billion. 🔍 Portfolio structure revealed: Tech giants remain the core configuration His largest position is in Meta (Facebook's parent company), accounting for 16.18%, demonstrating his firm confidence in social media and the metaverse. Next are Microsoft (8.81%) and Sea Ltd (7.87%), the latter being a Southeast Asian internet giant. 📈 Other key holdings include: •Google (Alphabet): 5.99%, continuously optimistic about search and cloud computing •Amazon: 4.71%, the e-commerce leader remains heavily invested •Take-Two (TTWO): 4.55%, parent company of the popular game GTA •Nvidia: 4.47%, the most sought-after chip giant in the AI era •Eli Lilly: 4.15%, GLP-1 weight-loss drugs driving a surge in pharmaceutical stocks 🌱 Growth and high-potential companies densely populate the "Others" sector 37.3% of the portfolio is distributed among small and medium-sized companies and emerging unicorns, including Reddit, Zillow, DoorDash, TSMC, Grab, NuBank, ServiceTitan, etc. Most of these enterprises possess explosive growth potential, reflecting his continued preference for an "early-stage + high-growth" strategy. 🌐 Global layout, with tech, healthcare, and consumer sectors flourishing In addition to U.S. internet giants, he has also invested in emerging market representatives such as Southeast Asian e-commerce company Sea, Brazilian digital bank Nubank, Taiwanese semiconductor company TSMC, and Pinduoduo (not marked in the puzzle but commonly seen in Tiger's historical holdings). Moreover, companies like Uber, Zscaler, Apollo, and UHC are also included, highlighting a "global tech + basic services" approach. 🧠 Summary of investment strategy: Belief in growth, embracing the technological future Chase Coleman continues Tiger's consistent high-growth style, heavily investing in technology, internet, cloud services, and AI-related enterprises, while using some pharmaceutical, financial, and platform companies as defensive and potential supplements. His portfolio is more geared towards "medium to long-term growth" investors, rather than those seeking short-term volatility. 🚀 Would you follow the "tech hunter" and copy his homework? Welcome to the comments section to analyze his investment logic together! #unitedstates# #nasdaq# #sp# #dataanalysis# #businessthinking# #spend60dayslearningfinancia#... #worshipingthegodofwealthisn#... #littletycoonsjourneytowealth# #elegantsavingscampaign#

. @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

Grok Bot is incredible at data analysis I gave Grok @bot a task to analyze data and it worked for 12 hours straight. When I woke up this morning, I had hundreds of thousands of data points and a full analysis. It did not stop, it did not get lazy, it just kept going 🤯 When rate limited by the API, it figured out how long to wait and kept trying until all data was processed.

gemini 4 dominates reasoning and data analysis google models are often overlooked because they don't focus as much on coding, but they've always been strong in other areas that matter for real-world use. i think that's one of the reasons behind their massive user base fast, reliable, and affordable models

5 GITHUB REPOS FOR AI DATA ANALYSIS: 1. PandasAI 2. Data Formulator 3. Open Interpreter 4. MindsDB 5. Vanna This is the only stack you need

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:

From 17.5% to 99.7% accuracy on hard data analysis questions. That’s the difference Guides made in our DABStep benchmark evaluation. Gemini 3 Flash's gain with Guides across 419 held-out questions: > Hard: 17.5% → 99.7% (+82.2 percentage points) > Easy: 78.9% → 100% > Overall: 27.9% → 99.8% The difference comes down to Guides - the agent could retrieve the business context it needed while working through each question. It works because your schema doesn’t tell the whole story. Metric definitions, join logic, deduplication rules, and business caveats all shape the right answer. Your agents 𝘯𝘦𝘦𝘥 that information if they're going to provide correct answers. Guides puts that knowledge in MotherDuck, alongside your data, for your agents. Write it once as markdown, and agents connected through our MCP server can find and load the context each question needs. Give your agents a Guide: