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#

Discover how prompt engineering is revolutionising #education# by turning #AI# into effective teaching assistants, enhancing learning with tailored, ethical support.  — @meisshaily #ArtificialIntelligence# #TechNews# #Tech# #Technology#

Scaling agents isn't a prompt engineering problem. It’s an orchestration problem. Jira automation is now an open control plane for your coding agents. Trigger @GitHub Copilot, @cursor_ai, or @claudeai Code the moment engineering conditions are met. Stop prompting. Start orchestrating.

New blog post 📝 "Buzzword Engineering" Prompt Engineering, Context Engineering, Harness Engineering, and now Loop Engineering — at least four "Engineerings" have been born in just a few years since LLMs arrived. Why does the next name keep arriving before the previous one has matured into a real methodology? 🤔 In this post, I name and dissect this phenomenon: Buzzword Engineering — a mode of knowledge production in which methodologies are named and shipped faster than verification can digest them. The root cause is an asymmetry of speed. LLMs drove the cost of proposing methodologies to nearly zero, and are even becoming proposers themselves. Verification, however, completes only when products are used by real users — it remains rate-limited by human behavior. Proposals move at machine speed; verification moves at human speed. Names pile up in the gap as a backlog ⚙️ But this is not a piece that sneers at buzzwords. As Schumpeter's "swarms" and the hype cycle show, proliferation is written into the standard timetable of every technological revolution — it is the first step of knowledge creation, coordinating the attention of engineers worldwide. What I propose instead is a gearbox connecting two clocks: the weekly clock of methodology and the yearly clock of product value. That gearbox is xOps. Inside it: evaluation assets that compound over time, an "autonomy budget" for operating agent delegation by observation, and one norm — if you coin a name, attach falsification conditions and an eval. Methodologies depreciate; evaluation assets compound 🚀 If you're tired of chasing new names, this one is for you. #BuzzwordEngineering# #TechTrends#

Longer prompts are not what image generators need. Text-to-image models seem less constrained by prompt length than by how clearly the prompt exposes the scene. This paper finds that text conditioning scales with image-grounded information, not token count. Across open-weight models, simply extending natural-language captions eventually made outputs worse than each model’s shortest-caption result. The authors replace prose with a structured prompt that separates the scene, individual objects, bounding boxes, depth, attributes, and relationships into named fields. The shift here is: prompt engineering for visual generation should optimize how explicitly visual variables are represented, then train the prompter to fill that representation well. The biggest prompt-engineering gain may come from how visual content is organized before it reaches the image model. – arxiv. org/abs/2607.29679 Title: "Scaling Properties of Text Conditioning in Visual Generation"