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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#

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:

公司真实的业务数据不方便直接丢给在线 AI 🤡 又不想每次查报表都求人写 SQL？ Data-Analysis-Agent ：一个面向商业分析场景的 AI Agent 支持上传 Excel / CSV 或连接 MySQL、PostgreSQL 等数据库 通过自然语言生成 SQL、图表和分析结论 适合中小团队搭建轻量级查数入口

Since PRs at @openclaw are basically reverse entropy, I'm now using codex to run data analysis on Discord to filter out the most important pain points to see where to work on next.

Everybody is doing AI agents these days. Here's a great example of an application that gets it right: @genspark_ai AI Sheets lets you literally talk to your spreadsheets. Upload your files, ask any data analysis question, and it automatically analyzes everything, pulls the info, cleans it up, and builds reports and visualizations for you. Really has that "wow" magic effect.

New 2h11m YouTube video: How I Use LLMs This video continues my general audience series. The last one focused on how LLMs are trained, so I wanted to follow up with a more practical guide of the entire LLM ecosystem, including lots of examples of use in my own life. Chapters give a sense of content: 00:00:00 Intro into the growing LLM ecosystem 00:02:54 ChatGPT interaction under the hood 00:13:12 Basic LLM interactions examples 00:18:03 Be aware of the model you're using, pricing tiers 00:22:54 Thinking models and when to use them 00:31:00 Tool use: internet search 00:42:04 Tool use: deep research 00:50:57 File uploads, adding documents to context 00:59:00 Tool use: python interpreter, messiness of the ecosystem 01:04:35 ChatGPT Advanced Data Analysis, figures, plots 01:09:00 Claude Artifacts, apps, diagrams 01:14:02 Cursor: Composer, writing code 01:22:28 Audio (Speech) Input/Output 01:27:37 Advanced Voice Mode aka true audio inside the model 01:37:09 NotebookLM, podcast generation 01:40:20 Image input, OCR 01:47:02 Image output, DALL-E, Ideogram, etc. 01:49:14 Video input, point and talk on app 01:52:23 Video output, Sora, Veo 2, etc etc. 01:53:29 ChatGPT memory, custom instructions 01:58:38 Custom GPTs 02:06:30 Summary Link in the reply post 👇

ChatGPT "Advanced Data Analysis" (which doesn't really have anything to do with data specifically) is an awesome tool for creating diagrams. I could probably code these diagrams myself, but it's soo much better to just sit back, and iterate in English. In this example, I was experimenting with a possible diagram to explain Supervised Finetuning in LLMs. The "document" at the origin (0,0) is the empty document, and eminating outwards are token streams. Highlighted in black are the high probability token streams of the base model. In red are the token streams corresponding to the conversational finetuning data. When we finetune, we are increasing the probabilities of the red paths and suppressing the black paths. I like this view because it emphasizes LLMs as "token simulators", with their own kind of statistical physics backed by datasets, bouncing around in the discrete token space. The conversation where we built it in a few minutes: (Sadly I just remembered that ChatGPT sharing doesn't support images, but at least the text is there, of me iterating with the diagram in plain language, and needing to touch no code. Such a vibe of the future.) I had a similar experience yesterday, was trying to create a plot that shows smoothing in n-gram language models. Again I could just have coded this manually, but this was 10X faster and so easy. Conversation: Posting because during these chats I was struck again by that feeling of what must be the future, where you just sit back and say stuff, and the computer is doing the hard work. And in some narrow pockets of tasks, you can already get that feeling today.