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:

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:

Another third-party Agent is now live on @AgentON_ . Meet the Scientific Research Analyst by @Powerdrillbloom. Built on Powerdrill’s AI data analysis infrastructure, this Agent helps turn complex research questions into structured, source-backed research briefs using large-scale open science and public-health datasets. It can work across: • 40M+ research papers from PubMed, arXiv, bioRxiv, and medRxiv • 500K+ clinical trial records • 2M+ compounds and drug-related datasets • WHO and CDC public-health statistics • Biomedical and scientific literature research Just describe your question in plain language. The Agent searches the connected datasets, organizes the findings, cites the relevant sources, and delivers a structured research brief with downloadable Markdown and CSV files. No coding. No manual digging through thousands of papers. Try it on AgentOn 👇 For research and informational purposes only, not medical advice. More specialized third-party Agents are coming to AgentOn. 🤖

A useful but little-known OpenAI API feature 🐍 What if you could say "analyze this data" and have the AI write and run the code for you? You can. OpenAI's "Code interpreter" is a hosted tool that executes Python in a sandbox for data analysis, computation, and file processing. It goes beyond generating code to actually running it end-to-end. 📌 Title: Code interpreter 🔗 URL: 🧩 Overview Regular LLMs can write code but can't execute it. Code interpreter runs Python inside a sandbox and returns actual results: computed values, charts, processed files. It shines on tasks where you need an answer, not just code: data analysis, numerical computation, file transformations. 🛠 How to use it Add code_interpreter to your tool definitions. The model generates and executes Python code as needed, including the results in its response. It supports file upload and download, so you can pass in a CSV or Excel file and get analysis results back. 🏗 Building it into production ・Data analysis dashboards: users ask questions in natural language, and Python runs behind the scenes to aggregate, compute, and visualize the answer. ・Automated report generation: analyze uploaded data files and produce reports with charts automatically. ・File conversion pipelines: automate CSV-to-JSON, image resizing, PDF text extraction, and other file processing tasks. ・Math and statistics services: handle complex calculations with code execution for accuracy. Avoids the LLM's tendency to make arithmetic mistakes. 💡 Use cases 📊 Natural language data analysis and visualization 📈 Automated reports from uploaded data 🔄 File format conversion and processing 🧮 Accurate numerical and statistical computation ⚠️ Watch out The sandbox has resource limits, so very large datasets or long-running processes may not work well. Packages not pre-installed in the sandbox aren't available, so check compatibility for specialized libraries upfront. If you're handling sensitive data, review the data handling policies as well. ✨ Delegate "write the code and run it" entirely to the AI. Start by uploading a CSV file and asking for an analysis. #OpenAI# #LLM#

🚨Breaking: Anthropic just dropped Opus 5. One of the biggest AI model releases of the year. But most people are barely scratching the surface of what it can actually do. So I spent hours testing it and compiled an Opus 5 Prompt Library with 50 powerful prompts + advanced techniques for: • Coding & debugging • Deep research • Writing & content creation • AI agents & automation • Business strategy • Data analysis • Marketing & growth • And much more. If you want the full prompt library, I'm giving it away for free. How to get it: 1️⃣ Follow me (so I can DM you) 2️⃣ Like + RT this post 3️⃣ Comment "opus" 🔥 I'll DM the document to everyone who completes the steps. Let's unlock the full potential of Opus 5.

TL;DR AI use is shifting from "chat Q&A" to "delegating whole tasks." OpenAI's usage data shows the unit of knowledge work moving from single interactions to long-horizon tasks. 📊 Title: How agents are transforming work URL: Highlights 🧑‍💼 The unit of knowledge work shifts from "interactions" to "delegated, long-horizon tasks" ⏱️ In May 2026, 80.6% of individuals made a Codex request >30 min of human work, 70.2% >1 hour, 25.6% >8 hours 🔀 By June, 99th-percentile users generated 60+ hours of agent turns per day across parallel agents 📈 Non-developer users exploded vs Aug 2025: 137x individuals, 189x organizations 👥 Codex now has 5M+ weekly active users (6x+ since the February desktop launch) 🚀 Fastest-growing tasks: data analysis +110%, research +37%, knowledge artifacts +36% (week over week) 🏢 Even non-technical departments like Legal and Recruiting use Codex as their primary AI tool The move to redesign work around "humans plus many parallel agents" is now visible in the numbers. #AIAgents# #FutureOfWork#

AI is our asymmetric answer to Russia's numbers. Our Defense AI Center "A1," launched in March, is building tools to make the Defence Forces an AI-driven army — AI at every level: data analysis, decision-making, and target engagement. The mission is to save the lives of Ukrainian soldiers and make their work on the front more effective. A1 is putting AI across the entire targeting chain — interceptor drones downing shaheds, autonomous turrets, last-mile targeting that holds through jamming, drone swarms.

A useful but little-known Gemini API feature 🐍 Say "calculate this" and Gemini writes Python, runs it, and hands you the exact result. Gemini's "Code execution" auto-generates and runs Python in a sandbox to perform accurate calculations and data processing. It patches the one thing LLMs are notoriously bad at: math. 📌 Title: Code execution 🔗 URL: 🧩 Overview LLMs handle natural language well but struggle with precise numerical computation and data manipulation. With code execution enabled, Gemini generates Python code in response to your query and runs it in a Google-hosted sandbox. You get exact computational results and data outputs. No Python environment needed on your side. 🛠 How to use it Enable the code execution tool in your request. Send prompts like "find the average of this series" or "aggregate this CSV," and Gemini generates code, executes it, and returns the result. The generated code is visible too, giving you transparency into what was actually computed. 🏗 Building it into production ・Data analysis assistants: users give analysis instructions in natural language, the assistant writes code to aggregate and visualize. ・Education platforms: explain programming concepts by executing code live in response to student questions. ・Finance/accounting tools: handle complex formulas and currency conversions with actual computation, not LLM guessing. ・Automated report generation: pass data and say "create a monthly summary," get both the aggregation and the narrative. 💡 Use cases 📊 Natural-language data analysis and aggregation 🎓 Code execution-backed programming education 💹 Precise numerical processing for finance 📈 Data-driven automated report generation ⚠️ Watch out The sandbox limits available libraries, execution time, and memory, so large-scale data processing won't work. External network access is also blocked, meaning code that calls APIs can't run. Stay within the sandbox's capabilities. ✨ This fundamentally solves the "LLMs can't do math" problem. For any task involving numbers, just enabling code execution can dramatically improve answer accuracy. #Gemini# #LLM#

🚨 Abacus AI Agent Now Has Claude Fable Mode Route to the best model based on your agentic task > Fable - hard coding > GPT 5.5 - data analysis > Gemini - research > Opus 4.8 - design and ppt > DeepSeek - Agent Swarms > GPT 5.5 + Opus 4.8 - mobile apps BUILD, CREATE AND DEPLOY ANY THING IN MINUTES