标签：#an

Introduction Excel is a powerful spreadsheet program developed by Microsoft that is used to calculate, organize and analyze data. It provides a way of turning raw data into meaningful insights through handling large datasets more efficiently from tracking sales and expenses to analyzing trends. Various Excel applications. Decision making: One of the major ways Excel is used in real-world data analysis is to support decision making. Companies collect large volumes of raw data everyday ranging from customer information, sales records to log records. This data is organized and cleaned by Excel into tables, charts and reports making it easier to derive insights and identify trends that help in decision making. Financial reporting: Excel is also widely used for financial reporting and budgeting. Businesses use it to record income and expenses, calculate profit margins and create financial predictions. By analyzing financial data, organizations are able to monitor their performance over time and plan better for future growth. Marketing performance: In addition to that, Excel can be used in market analysis. Marketing teams utilize Excel to track campaign and social media performance, customer engagement and product popularity. Insights derived from this data helps companies improve their marketing strategies and better understand consumer behavior. This past week I was introduced to several data cleaning features and formulas used in Excel to make analysis less nerve-wracking. For example, in stead of editing data cell by cell in the case of duplicate values, you can use the Find and Replace filter. Also, conditional formatting makes it easier to highlight specific cell ranges and erase duplicate values. Functions and formulas make it easy to obtain statistical and mathematical data. Learning Excel helps you look at data differently. Instead of data being just a bunch of texts, numbers or logs, data becomes something you can use to gain insights, make decisions, reveal pat

Most merchants know they have unused files. Far fewer realize they're accumulating AI-generated media they never intended to keep. There's a problem quietly growing inside thousands of Shopify stores right now. It's not abandoned carts. It's not slow page speeds. It's not even the 400 unused product images you already know you should deal with. It's something newer, and most merchants have no idea it's happening. The Rise of AI-Generated Commerce Content Over the past two years, AI image tools have gone from novelty to routine. Shopify Magic. Canva AI. Midjourney. ChatGPT image generation. Adobe Firefly. Background removers. Lifestyle photo generators. Product shot enhancers. Merchants are using these tools constantly — to mock up new products, test background options, generate seasonal variants, create ad creatives, experiment with lifestyle photography. The workflow feels clean: generate a few options, pick the best one, move on. Here's what's actually happening on the backend. Every time you use Shopify's native AI tools to generate, edit, or enhance an image, Shopify quietly deposits files into your media library. Not just the one you kept. All of them. The rejected generations. The experimental edits. The "let me try one more variant" files. The abandoned attempts from six months ago when you were testing a new product that never launched. Every. Single. One. Most merchants assume the files they don't choose disappear. They don't. The lifecycle looks something like this: ┌─────────────────────┐ │ AI Image Generation │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Rejected Variants │ │ • Drafts │ │ • Test Images │ │ • AI Edits │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Hidden Media Files │ │ Accumulate Over Time│ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ AI Clutter │ │ Invisible Technical │ │ Debt │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Reduced Media │ │ Governance │ │ • More Noise │ │ • Less Visibility │ │ • Hard

Notion's head of product said he was "astonished" at “the amount of people RT-ing this."

Welcome back to TechCrunch Mobility — your central hub for news and insights on the future of transportation.

When I started learning Excel as part of my Data Science & Analytics course, I assumed it was just a tool for creating tables and performing basic calculations. After spending a week exploring its features, I quickly realized that Excel is much more powerful than I thought. Almost every organization generates data. Businesses track sales, schools monitor student performance, hospitals manage patient records, and marketers analyze campaign results. Before data can be analyzed, it needs to be organized, cleaned, and summarized—and that's where Excel comes in. In this article, I'll share some of the Excel concepts I've learned so far and how they're used in real-world data analysis. Understanding the Excel Workspace Before working with data, it's important to understand the basic structure of Excel. When you open Excel, you're working inside a workbook . A workbook can contain multiple worksheets (often called sheets), which help organize different sets of data. At the top of the screen is the Ribbon , which contains tabs such as Home, Insert, Page Layout, Formulas, Data, and View. The Ribbon acts like a control center where you can access Excel's tools and features. Rows run horizontally and are identified by numbers, while columns run vertically and are identified by letters. The intersection of a row and column is called a cell , where data is entered. At first, all these parts seemed overwhelming, but after using Excel regularly, navigating through them has become much easier. The Different Types of Data in Excel One of the first things I learned is that not all data is the same. Excel commonly works with: Text data (names, product categories, locations) Numeric data (sales figures, quantities, prices) Date and time data (order dates, deadlines) Logical data (TRUE or FALSE values) Understanding data types is important because Excel treats each type differently when performing calculations and analysis. Number Formats Matter More Than I Expected Another concept that

May 25, 2026. This is not just another date on my calendar. This marks the beginning of one of the biggest professional transitions of my life. After nearly a decade working in the world of IT infrastructure, technical support, networking, field engineering, and systems operations, I’ve made a decision that has been building in my mind for some time: I am transitioning into Data Analytics. And this is where I document that journey—publicly, honestly, and in real time. Not when I become an expert. Not when I feel “ready.” Not when everything looks polished. I’m starting now. Because real growth is rarely clean, predictable, or perfectly planned. Sometimes it starts with one uncomfortable decision: To leave what you already know… and step into what your future requires. Where My Journey Started Before data, before dashboards, before writing my first SQL query or building my first analytics project—my career started in the trenches of IT. For the past 10 years, I’ve built my career solving real technical problems across businesses, organizations, schools, offices, and field operations. My world has been cables, routers, networks, system failures, installations, troubleshooting, and making technology work where others saw complexity. Over the years, I’ve worked deeply in: Computer troubleshooting and hardware diagnostics Printer setup, configuration, and enterprise support Wi-Fi deployment and hotspot installations LAN design and structured network deployment Fiber optic installations and network termination Data cabling and structured cabling systems CCTV surveillance installation and maintenance Alarm systems and electronic security integration Intelligent security systems Electric fence installations and perimeter protection systems Router, switch, and access point configuration End-user support and enterprise technical troubleshooting Systems maintenance and operational support I’ve spent years on ladders, in server rooms, inside offices, on construction sites, insi

There's a pattern I've noticed with every new AI coding tool that comes out: they all want you to switch editors. Or open a new terminal. Or context-switch into some standalone app. I DON'T WANT TO DO THAT My entire dev workflow lives in VS Code. My keybindings, my split panes, my snippets, my extensions — all of it. When Google released the Antigravity CLI ( agy ), an agentic coding assistant, I genuinely liked what it could do. But to use it properly, I had to live in a terminal window, manually managing sessions, typing slash commands from memory, and losing my editor context entirely. So I built a VS Code extension for it instead. What is Antigravity? Google Antigravity is Google's agentic coding CLI — think of it as a Gemini-powered dev assistant that can read your project, run tools, execute terminal commands, and help you build. It's the kind of tool that can handle complex multi-step tasks, not just autocomplete. The CLI is called agy , and it's genuinely capable. The problem was the workflow: terminal-first, session management by hand, and no visual layer over the context you're already in. The Extension: Antigravity for VS Code Install it on the VS Code Marketplace Source on GitHub The core idea is simple: the extension is a UI layer. It never bundles or replaces the agy binary — it shells out to whichever version you have installed locally. Same philosophy as the Claude Code VS Code extension: the editor provides the surface, the CLI does the work. Here's what it actually does: Sessions List The sidebar panel opens to all your saved sessions. You can open an existing one, delete it, or start fresh. New sessions can be launched in sandboxed mode or with permissions bypassed — accessible right from the "New Session" overflow menu, without memorizing CLI flags. Any session with an active turn shows a loading indicator in its row, so you always know what's in flight. Chat Panel (Material 3 Expressive) This is the main surface. Each session runs its own live,

Before this week, I thought Excel was just a fancy calculator with boxes. But after three days of my Data Science & Analytics course, I realise I was wrong. Really wrong. Excel is a spreadsheet tool used by millions of people from small business owners to data analysts at giant companies. And the best part? You don’t need to be a programmer to use it. You just need to know a few tricks. Here’s how Excel helps solve real-world problems using exactly what I learned in Week 1. 3 Real-World Ways Excel Is Used Business decisions with logic Managers use IF() statements to answer yes/no questions. Example: =IF(Sales>1000, "Bonus", "Needs Improvement"). One cell can decide who gets paid more. Cleaning messy data Real data is never clean. Marketing teams use Remove Duplicates, Find & Replace, and Text to Columns to fix hundreds of messy rows in seconds. No manual typing. Tracking deadlines and ages HR teams use DATEDIF() to calculate employee ages or years of service. TODAY() and NOW() keep reports automatically updated. No more “oh, I forgot to update the date.” 3 Excel Features I Learned This Week Remove Duplicates – One click, and Excel deletes repeated rows. Saved me from sending the same customer email twice. IFERROR() – Hides ugly errors like #DIV/0! and shows something friendly instead (e.g., “Check data”). Your boss will thank you. Sort & Filter – With AutoFilter, I can find all sales above $500 in one second. Then Custom Sort lets me sort by date and region together. My personal reflection Honestly? Learning Excel has changed how I see data. I used to look at a messy spreadsheet and feel lost. Now I see Remove Duplicates, Text to Columns, and TRIM() as tiny tools that bring order to chaos. Data isn’t scary anymore. It’s just a puzzle and Excel gives me the pieces. I’m only one week in. But I already feel like a junior data analyst in training.

These 5- to 6-inch blades are the secret workhorses of the kitchen, whether you’re a smaller person, have smaller hands, or just desire a smaller but still high-functioning knife.

The annual developer event is where Apple announces operating system updates—like iOS 27—and maybe even teases future hardware. Here’s how to see it all.

For the past year I worked on a remote contract with a US tech company. Paid in USD, ultimately needing Korean won. Simple, right? Then a year in, I actually reconciled what landed in my account. The exchange rate had gone up — and yet my real received amount was lower than I'd expected. I traced it, and money was leaking at every step of the transfer path I hadn't been watching. This is what I learned switching routes over that year: from a direct bank wire to Wise, the real cost difference, and one right buried in my contract. If you're a freelancer or contractor in any country earning USD from abroad, this should save you something. Money leaks in more than one place Getting USD from overseas into local currency looks like one step. It's actually at least four: The wire fee from the US bank, through correspondent banks, to the receiving bank. The exchange rate the receiving bank applies — this is the big one. The receiving fee on the destination side. A hidden "lifting charge" some correspondent banks skim. The largest is the rate. Banks quote two rates, and the "buyer rate" applied when an individual sells dollars is worse than the mid-market reference — typically a 1.5–2% spread . On $1,000, that's $15–20 gone to the rate alone. That number looks small. Accumulated over a year, it stops looking small. Route A — receiving directly through a major US bank My first setup was the simplest: the company wired USD to my US bank account, and I wired it on to my Korean bank. I picked this at contract start without much thought, assuming the client would conventionally cover fees anyway. (Lesson one: specify the transfer method, route, and who pays in the contract. ) The problem was the bank's exchange rate. It applied the buyer rate straight up, with a wider-than-usual spread versus mid-market — plus a send fee, plus the Korean receiving bank's fee. I only noticed months in. Comparing statements, there was a steady 2–3% gap between the won I'd expect at mid-market and t

Start here This post assumes you have a project and you're trying to pick a tool. It skips the marketing and goes to the technical and economic decisions that actually matter when you're doing the evaluation. All three of these platforms — Contentful, Sanity, and SleekCMS — are used in production by serious teams. The comparison is not going to tell you one of them is bad. It's going to tell you which scenarios each one is genuinely suited for, so you can match your project to the right tool. The three platforms at a glance Contentful is an enterprise-grade headless CMS with a long history of production deployments, a large ecosystem of integrations, and pricing that becomes significant at scale. It's the established choice for large organisations with existing Contentful investment. Sanity is a developer-first headless CMS with a flexible schema system, real-time collaborative editing, and GROQ — a query language purpose-built for content graphs. It has a strong developer community and a genuinely modern developer experience. SleekCMS is a headless CMS with an integrated static site builder. Structured content with REST and GraphQL APIs, a TypeScript-native client library, and the option to generate and deploy a static site from the same content models without maintaining an external frontend stack. Content modeling All three support structured content modeling. The differences are in how you define models and what the system supports natively. Contentful uses a GUI-based model builder in the dashboard. You create content types and add fields through a point-and-click interface. Solid for teams who prefer a visual setup; less ideal if you want models version-controlled as code. Sanity defines schemas in TypeScript or JavaScript config files — committed to your repository, version-controlled, and composable like code. This is Sanity's strongest differentiator for teams who think in code-first workflows. The schema definition is expressive and the TypeScript integrat

SQL tuning is the process of making a database query run faster and cheaper — cutting response time while minimizing the system resources it burns. Here's the playbook I actually use, from "this query is slow" to "this query is fixed," with the traps that bite people in the middle. The loop Tuning is iterative. The shape is always the same: Identify the problem. Find the slow query (logs, profiler, or user feedback) and measure a baseline — execution time and resource usage. You can't claim an improvement you didn't measure. Analyze & rewrite. Review the SQL for redundant joins, unnecessary work, and complex subqueries. Tighten the WHERE , select only the columns you need, convert subqueries to joins where it helps. Read the execution plan. Understand how the engine actually runs the query; find inefficient join orders and needless full scans. Revisit indexes. Evaluate whether existing indexes help; add or restructure as needed. Consider schema changes. If a column is updated so often that indexing it hurts, split it out. Sometimes the model is the bottleneck. Tune settings/hardware if it comes to that. Re-test and repeat. Apply changes, re-check the plan, confirm the gain, monitor. Reading an execution plan The execution plan shows how the DB will run your query — table scans, index access, join methods. Read it well and you can pinpoint where the time goes. Most engines expose it: EXPLAIN (MySQL/PostgreSQL), EXPLAIN PLAN FOR (Oracle), SET SHOWPLAN_ALL ON (SQL Server). Operators to know: Full Table Scan — reads every row. Happens when there's no suitable index, or the query can't use one. Index Scan — scans via an index; usually cheaper than a table scan. Index Seek — jumps to specific key values; very efficient, reads only the rows it needs. Nested Loops / Hash Join / Merge Join — the three ways to join two tables (more below). Sort — orders data; excessive sorting is a common performance drag. Three numbers that matter: Cost — estimated resources a step will cons

I was diagnosed with stage 3 cancer. Surgery is scheduled for December 31st. Last day of 2020. Fitting. I'm not going to use this space for medical details or false optimism. What I want to think through is what this does to how I approach work. Mortality as a Constraint Time might be finite. It was always finite, technically, but now the bound is tighter and less abstract. This changes the optimization problem. Before: maximize long-term impact. Now: maximize impact given uncertainty about time remaining. Concretely, that changes which projects I start, how I document things, what I publish vs. keep private, and how much I think about whether my work outlasts me. The Archive Problem I keep thinking about durability: Will my code still build in 10 years? Have I documented my reasoning, not just my results? Are my repositories structured so someone else could pick them up? Is it clear why I built these things, not just what they do? This isn't morbid. It's practical engineering applied to an uncertain timeline. What I'm Focusing On Cancer is good at clarifying priorities. Finish meaningful work. Complete the projects that contribute something real. Document obsessively. Write as if I won't be here to explain. Open source everything. Make my work reproducible and continuable. Think clearly about hard problems. Use whatever time there is to engage honestly with questions that matter. The Paradox Cancer has made me more productive. Not because I'm racing, but because the filter got sharper. I care less about publications for their own sake. I care more about whether what I've built will be legible and useful to someone else. Continuing I'm still in my math degree. Still building tools. Still writing. The diagnosis doesn't change the work. It changes the framing. Every project is now: "If this is my last contribution to this area, is it worth the time?" Most things fail that test. A few pass. I'm working on those.

The benchmark is the wrong story Anthropic shipped Claude Opus 4.8 this week. You probably saw the announcement post on Tuesday, the swarm of benchmarks on X by Wednesday, and somebody's curated leaderboard of "the new SOTA on SWE-bench Verified" by Thursday morning. By Friday everyone had moved on. That is the normal shape of a model release in 2026. It is also the wrong story. The benchmark delta from 4.7 to 4.8 is real but not load-bearing. The load-bearing story is the calendar. Opus 4.6 shipped late February. Opus 4.7 shipped in April. Opus 4.8 shipped this week, in early June. Three Opus generations inside four months. Whatever the headline numbers say about coding, agentic reasoning, or long-horizon tool use, the operating reality has already changed underneath you: if you run a production agent on a fixed model pin, you are now eating a migration tax every six to ten weeks. You can either notice that now and refactor, or notice it in late August when Opus 4.9 lands and your customer-facing agent regresses for the third time this year. This post is the second story. I am going to skip the benchmark recap — go read the model card — and tell you what to do before the next release lands. What Anthropic shipped The announcement post on anthropic.com confirmed three things and implied a fourth. The three confirmed: Opus 4.8 is the new default Opus tier model, ID claude-opus-4-8 . The previous defaults (4.7 and 4.6) remain accessible by explicit pin for at least 90 days. Fast mode is available on 4.8 the same way it shipped on 4.7 — same model weights, higher-throughput inference path, no quality downgrade. That matters because the practical difference between Opus and Sonnet for many workloads now comes down to fast-mode availability, not raw capability. The model card claims meaningful improvement on long-context coherence, agentic tool dispatch, and refusal calibration. The benchmarks back this up to roughly the degree we expect from a 6-week cycle — modest but

Those ousted included ADA journal editor-in-chief Steven Kahn and former ADA president Desmond Schatz

I just launched Kotobato on Google Play after about two and a half years of solo development. It's a word puzzle RPG — you swipe connected letters on a board to form words, and those words become attacks. Longer words deal more damage. Rarer words hit harder. I want to share what I built, why I built it this way, and what surprised me most during development. The core mechanic The board is a grid of letters. You swipe a path through connected letters to form a word. When you submit the word, it becomes an attack against the enemy. The twist: word length isn't the only thing that matters . The game has six elemental types — Animal, Nature, Knowledge, Food, Life, and Fantasy — and each word is categorized into one of these elements. Enemies have elemental weaknesses, so the right word beats a long word if you're hitting a weakness. This created an interesting design problem. In most word games, you're just maximizing point value. In Kotobato, you're making tactical choices: do I use a short word that hits a weakness, or a long word that deals raw damage? Why hiragana and English both work The game runs in both Japanese (hiragana) and English. This wasn't a late addition — it was part of the original design. Japanese hiragana is a syllabic script with 46 base characters. Because each character represents a whole syllable rather than a single phoneme, even short hiragana words feel phonetically "weighty." A 4-character hiragana word might correspond to an 8-letter English word in spoken syllables. This means the game feels different in each language — not just translated, but genuinely different. Japanese mode rewards knowledge of vocabulary that uses phonetically distinctive combinations. English mode rewards knowledge of unusual high-value words (think quixotic , ephemeral ). What I actually built 100-floor tower with escalating bosses, including historical Japanese figures like Oda Nobunaga and Toyotomi Hideyoshi Gacha character system — collectible characters with d

Krishnan is reportedly starting a new institution to continue shaping Trump's AI policy.

President Donald Trump said he's discussing deals "where the American people can benefit from the success of AI."

Introduction In today's fast-paced business environments, data is considered the cornerstone of decision-making, policy formulation, and other organizational needs. MS Excel is a robust spreadsheet developed by Microsoft for organizing, analyzing, and visualizing data in rows and columns. In the data science and analytics domain, MS Excel is critical for analyzing and managing data to generate insights that enhance decision-making. Excel's polarity is characterized by its ease of use, flexibility, automation, and visualization. Ways Excel Is Used in Real-World Data Analysis Across the data science and analytics domain, MS Excel is frequently employed in the following ways; a) Data Cleaning and Preprocessing At the beginning of every data science and analytics project, data cleaning is required, and MS Excel is the primary tool. Typical Excel features and functions applied during data cleaning include Text to Columns, Remove Duplicates, Find and Replace, and Power Query. b) Exploratory Data Analysis Before performing data science and analytics activities, it is crucial to understand the dataset at hand, its structure, and trends. MS Excel features Pivot Tables, Pivot Charts, and Slicers that provide instant aggregation, sorting, and visualizations. c) Data Analysis and Reporting Modern organizations and businesses operate based on insights generated from data. MS Excel features such as pivot tables, charts, and conditional formatting help data analysts analyze and visualize data for clear, actionable insights that enhance decision-making. MS Excel Features or Formulas The typical MS Excel features and formulas employed in the data science and analytics domain include the following. Data Cleaning Functions Function Purpose Example Result UPPER() Converts text to uppercase =UPPER("john") JOHN LOWER() Converts text to lowercase =LOWER("JOHN") john PROPER() Capitalizes the first letter of each word =PROPER("john doe") John Doe TRIM() Removes extra spaces from text =TRIM(