标签：#Analysis

By the end of this page you can look at a folder of eight finished sheets and say which two or three belong on the dashboard, which one goes in the upper-left corner, and which of Tableau's three sizing options to pick. You'll also have a one-sentence test that decides every one of those calls. It's about fifteen minutes. Here's the move to make today. Open your busiest dashboard and write the single question it answers, in one sentence, for one named person. Then remove every view that isn't part of answering it. Most people delete half, and the half that survives lands harder than the whole thing did. The short version: Tableau's own guidance is two or three views on a dashboard. Crowding is what happens when one dashboard is asked to serve several audiences at once. Where the surviving views sit is the second decision, and it has a known answer, so that gets the picture. The original carries a diagram here. In words: A single dashboard rectangle divided into three panes. One large pane occupies the whole upper-left area and spans most of the width. Two smaller panes sit below it, side by side. A curved arrow enters at the top-left corner of the large pane, travels right across it, then drops down and moves left to right across the two smaller panes, showing the order a reader takes them in. A small numeral one sits on the large pane, two and three on the smaller panes. The drawing shows that the first thing a reader meets is whatever occupies the upper left, so the most important view belongs there and the supporting views belong underneath. 1. Why two or three, and where that number comes from Before the explanation: you have eight finished sheets and one dashboard. How many of them would you put on it? Two or three. That's not a taste call, it's Tableau's published guidance: "In general, it's a good idea to limit the number of views you include in your dashboard to two or three." The reason is about attention rather than about screen space. A dashboard is read,

This guide gives you a test that takes ten seconds and tells you whether the thing you just built is a report or an analysis. Then it gives you four moves that turn one into the other. Every move has a worked SQL example and real numbers. The whole method is here. What you actually do: take the number you just produced, and ask what someone would do differently because of it. If the honest answer is nothing, you have a report. Then you run the four moves below, in order, until the answer is a specific action a specific person can take on Monday. The short version. Data analysis is looking at records of things that already happened and finding a pattern that changes what someone does next. If nothing changes, it was not analysis. It was a report. The same starting number, two endings. The test: what would someone do differently? Before you read the answer, look at the last thing you built and try it yourself. Who was going to act on it, and what were they going to do? Take any number you have produced and finish this sentence out loud: "Because of this, someone should do a specific thing ." Both blanks have to fill in with something real. A named person or team, and an action they control. Here is a real one. "Churn was 4.1% in Q3." Who acts, and how? Nobody can act on that. It is a true, correctly calculated, carefully formatted number, and it changes nothing. That is a report, and reports are useful. A dashboard that tells you the servers are up is doing its job. It is just not analysis. Now the same underlying data, worked further. "Monthly-plan accounts that never opened the import tool churn at 9.2%. Ones that did churn at 1.8%. The email introducing that tool goes out on day 14, and most cancellations happen on day 11." Who acts? The lifecycle marketing owner. What do they do? Move the email to day 3. That is analysis, and the only difference is that it ended somewhere a person can stand. The word "analysis" is doing a lot of quiet work in job descriptions, so

Recently, a man I was rock climbing with told me about how he'd used AI to make a motivational poster for himself, which he'd hung on his bedroom wall: a bear, walking a slackline over a canyon, holding a sign that said, "Do cool shit." I made what I hoped was a polite noise. What […]

By the end of this guide you will have a published Tableau dashboard and a three-point story, built on a real dataset. It lives on a public URL you can put in an application. You build four small sheets. Each one makes exactly one point. You arrange them on a single screen, then walk a reader through them in three steps that end with a recommendation. Every step says what to click and what you should see afterwards. Four small sheets, rather than a wall of charts, because a dashboard has to argue for something. A screen holding everything you could build leaves the reader to work out what matters. Most readers will not do that work. Dashboard vs Story, in one line. A dashboard puts several charts on one screen so someone can explore. A story is a sequence of views with captions, clicked through in order, so someone is walked to a conclusion. Build both: the dashboard is what a hiring manager glances at, the story is what proves you can think. The original carries a diagram here. In words: Four separate worksheets stack on the left: a big single number, a set of vertical bars, a set of horizontal bars, and a scatter of circles. An arrow points right to one dashboard panel that holds all four of them arranged on a single screen: the number across the top, the two bar charts side by side in the middle, the scatter along the bottom. A second arrow points right to three story cards numbered one, two and three, each showing one of those views with a caption line above it. The worked example. Every instruction below is written against a real, free dataset: the Telco Customer Churn file on Kaggle, 7,043 customers, one row each. A finished analysis of it, including the Python script that shapes the data, is public at telco-churn-analysis . Swap in your own dataset and the steps do not change, only the field names do. Step 1: Shape the data before you open Tableau Tableau is a display layer. Deriving something inside it takes longer than deriving it upstream in SQL, Python or

This guide gives you a repeatable loop for the days when nothing works, and four true stories showing it used on real problems. Here is what a working day actually contains. A website's firewall blocks you for no reason. A table that visibly exists tells your script it does not. A query runs for thirty minutes with no end in sight. A fix you know is correct changes nothing at all. None of that means you are doing it wrong. That is the job. What separates people who ship analyses from people who stop is technical tenacity : staying methodical when the tools fight back. It is not a personality trait you either have or lack. It is a small procedure, and you can learn it in the next ten minutes. The diagnosis loop (tenacity is a method, not a mood) Think back to the last time a tool beat you for an hour. What was the first thing you did when it failed, and what did you do second? Most people can name the first move and not the second, and the second is where the method lives. Gritting your teeth and re-running the same thing harder is not tenacity; it's frustration with extra steps. What experienced people actually run is a loop: Step Move 1. Read the actual message Not "it's broken" — the words. Error messages name the symptom precisely, even when the cause is elsewhere. 2. Form ONE hypothesis "The table isn't in the file the script reads." Specific enough to be wrong. 3. Run the cheapest test of it Prefer checks that take seconds — list the tables, count the rows, print one value. 4. Verify from a second vantage point Don't ask the tool that's confusing you whether it's confused. Check the file from outside, the data from a different program, the value with a different query. 5. Change ONE thing, re-run Change three things and you'll never know which one mattered — or which one broke something new. 6. Timebox, then change strategy If the current approach has eaten 30 minutes with no progress, stopping is a decision, not a defeat. There's usually a second road. Four tr

This guide walks through five steps for working out which records are the same real thing, and merging them without wrecking your data. It runs on real chart data, and it includes the two times the rules came out wrong. Here is the problem in one example. Count the distinct artists in Billboard's public chart history and the number is wrong. "Elvis Presley" and "Elvis Presley With The Jordanaires" are the same man, and so are five other credit strings. One real-world entity , seven database strings . Every dataset with human-entered names has this. Customers who signed up twice. "IBM" against "I.B.M." against "International Business Machines". The same supplier in two systems, spelled two ways. The work of fixing it is called entity resolution . Matching across two datasets is record linkage . Removing duplicates inside one is deduplication . They are the same skill pointed at different situations, and it is one of the most common tasks an analyst actually gets handed. The vocabulary map Term Meaning Entity The real-world thing: one artist, one customer, one company Entity resolution Figuring out which records refer to the same entity Record linkage The same problem across two datasets. "Is row 5 in file A the same person as row 90 in file B?" Formalized by Fellegi & Sunter (1969) Deduplication The same problem inside one dataset Normalization / standardization Transforming values toward a canonical form (lowercasing, trimming, cutting suffixes) so equal things become equal strings Match key The cleaned column(s) you actually join on Match rate The share of records that found their counterpart. This is the number that keeps the whole exercise honest Clerical review Human eyes on the records the rules could not decide. This is a formal stage of the classic framework, not an admission of failure Step 1: measure the fragmentation before fixing anything The worked example is Billboard Hot 100 history, 1958 to present. The goal is one clean row per artist. Before writing

we compared 8 web hosts in common crawl's domain-level link graph. 35 non-platform domains link to all 8, while 72% of linking domains appear for only one subject. how we pulled this for each subject domain, we pulled the top 2,000 referring domains by authority from common crawl release Apr-Jun 2026 (cc-main-2026-apr-may-jun). we intersected those lists, then removed platform, cdn, social, and other non-editorial domains from the clean overlap counts. the filter matters. hosting providers, cdns, url shorteners and the big social networks link to almost everything, so leaving them in would produce a universal list that is technically correct and useless for outreach. the counts below are after that filter unless a column says otherwise. everything here comes from the open common crawl webgraph, so you can reproduce it without a paid backlink tool. subject domains domain referring domains cg authority hostinger.com 36,900 61 siteground.com 13,882 60 bluehost.com 48,747 61 dreamhost.com 36,470 61 wpengine.com 100,000 64 cloudways.com 13,795 58 kinsta.com 13,963 62 namecheap.com 16,701 62 the overlap across the 8 subjects, we found 10,077 unique linking domains. the clean universal set contains 35 domains. metric value unique linking domains 10,077 link to one subject 72% link to all 8 35 overlap distribution overlap all domains non-platform link to all 8 42 35 link to 7 89 86 link to 6 133 131 link to 5 185 182 link to 4 336 335 link to 3 614 608 link to 2 1,432 1,430 link to just 1 7,246 7,240 the distribution is the interesting part. most linking domains sit in the bottom row: they mention one product and never come back. the rows above it are where outreach lives, because a site that already covers several products in a category has an editorial reason to cover another one. a short universal list usually means the category is covered by a handful of directories, review sites and integration hubs rather than by a broad press base. a long one means the category has r

You Only Hold Four Thoughts Try to multiply 47 by 83 in your head. The answer is not the point. Watch what happens while you reach for it. You hold 47, you hold 83, you start on the partial products, and somewhere around the third one the first number goes soft. You reach for a pen, because the problem outgrew the place you were keeping it. That ceiling is real and it is low. The cognitive scientist Nelson Cowan spent years measuring it and put the number at about four. Not the seven you half-remember from an old paper, but three to five distinct things held in mind at once. 1 Four. That is the working capacity of the most sophisticated object in the known universe. Everything we call getting smarter has been a way around that four. The history of human intelligence is the history of putting thoughts somewhere other than the head, and it runs as a stack, each layer holding what the one below it cannot. The first rung is paper Reaching for the pen looks like a small surrender. It is the oldest cognitive upgrade there is. The moment you write 47 above 83 and start stacking partial products, you are thinking about six or seven things at once, because the paper is holding all but the one you are working on. Justin Sung, who teaches learning for a living, puts it more sharply. Writing is not the thing you do after you have reached clarity. Writing is what produces the clarity. 2 The page becomes the workspace where the thought turns real, because your four slots are freed to do the actual reasoning while the page remembers the rest. This is also why handwriting beats typing. It is far slower than thinking, and that slowness forces you to compress, to decide what is worth the stroke. The friction is not a tax on the process. The friction is the process. A page of notes you struggled to write holds more than a page you copied without resistance. The page is not a transcript of a finished thought. It is the workspace where the thought becomes possible. The rung most people

The Stable Liar The dashboard was green for eight quarters The most dangerous number on a dashboard is the one that has stayed green the longest, and the way it fails has a shape you have probably watched up close. For eight straight quarters the dashboard holds green. Revenue up and to the right. Retention flat and healthy. NPS in the fifties. Every board meeting opens on the same slide and closes on the same nod. The plan is working. Then, six months after the eighth green quarter, the business the dashboard was supposed to describe nearly falls over. Pull the post-mortem apart and the easy story is that the numbers lied. They did not. Every quarter the dashboard reports something true: customers are still paying, logins are still happening, the survey scores are still fine. All of it accurate. The failure is quieter and worse than a lie. The words behind the numbers change meaning while the numbers stand still. “Retention” still counts the same logins, but a login has stopped predicting a customer who will renew. The metric keeps its shape long after the thing it measured has walked out of the room. Anyone who has run a team has felt a smaller version of this. The number you trusted most became the number that surprised you most. You were not lied to. You were tracking something that used to mean one thing and quietly came to mean another, and the dashboard had no way to tell you the meaning had moved. This is the stable liar: a number that goes on looking right long after it stopped being right. It is a structural property of measurement under pressure, and it has a law underneath it. Why every optimised metric drifts A metric is a substitution: you replace the thing you care about with something you can count, and the gap between them is where the trouble lives. Start with the substitution. You cannot measure value, loyalty, insight, or health directly, so you pick a proxy you can count. Revenue stands in for value. NPS stands in for loyalty. Citations stand in

For roughly an hour this week, Telegram vanished from Apple's App Store. Even during that blip, it was a stunning absence for such a major app: an avenue of communication for more than 1 billion users around the world, used widely as a secure platform for people who live in countries under censorship. Apple later […]

2026 is shaping up to be an excellent year for Apple TV. Apple's streaming service has built out an impressive slate that spans returning favorites like Silo and Sugar to all-new hits including OnlyFans-inspired dramedies, terrifying comedies, and paranoid tech thrillers. But the most important release might be a feel-good sports sitcom. After what seemed […]

In less than a week, Spider-Man: Brand New Day raked in $1 billion worldwide and had the biggest box office opening weekend in Hollywood history. The feature has been a reminder of why Sony is probably never going to give up the Spider-Man film rights, and highlighted how Marvel Studios was smart to strike a […]

The brand trip is a right of passage for influencers. It's a mark of legitimacy that a sponsor wants to invite them on an all-expenses-paid vacation, often with luxurious freebies and activities. Trips can also spur hard feelings from uninvited influencers, trigger criticism from the public, and project a certain frivolousness. Usually it is fast […]

The next Xbox, Project Helix, could theoretically have the largest library of any home console. Not only will it play PC games, but we now know, courtesy of a leaked memo obtained by The Verge's Tom Warren, that it will run games from every generation of Xbox: the original 2001 Xbox, the 2005 Xbox 360, […]

No other gadget knows as much about your home as a robot vacuum. They map your space, learn your routines, and many now carry a camera and microphone into every room in your house. As AI gives them a better understanding of what they see, the privacy and security risks these autonomous robots pose are […]

Cape Fear has always been a thriller about paranoia. And in 2026 there are few things less trustworthy than technology. Apple TV's recent remake of Cape Fear is "inspired" by the 1991 film of the same name (Martin Scorsese and Steven Spielberg even returned as executive producers on the project), which itself is a remake […]

Over the last few years, I've gradually shifted away from ebooks. It's been part of a general return to physical media, an attempt to create a deeper sense of ownership over the art I love and collect. A Kindle is obviously more convenient than a paperback: It's tiny, holds a huge library, and you can […]

Apple's new Upgrade program is here, allowing you to lease select models of iPhones, iPads, Macs, and Watches with a relatively low monthly payment. The company promises you won't pay more than the full price of the device over the course of the one- to three-year lease, and in some cases, you'll pay hundreds of […]

There’s new benchmark measuring AI’s ability to perform mathematical cryptanalysis. Anthropic’s frontier model actually found new attacks. The benchmark: “ CryptanalysisBench: Can LLMs do Cryptanalysis? ” The idea is to benchmark the ability of LLMs to discover new mathematical cryptanalytic attacks against a series of historical algorithms. Abstract: Cryptanalysis—the task of finding attacks against cryptographic schemes—its at the intersection of mathematical reasoning and cybersecurity, two areas where LLMs have advanced fastest. Cryptanalysis represents both a clean testbed for frontier reasoning (as practical attacks can be automatically verified) and a domain with unusually high stakes, since the primitives under study underpin our digital security. In this paper we ask whether LLMs can do cryptanalysis, and find that the answer is increasingly yes. We introduce CryptanalysisBench, 191 tasks across six families of cryptographic primitives (block ciphers, hash functions, etc.) drawn primarily from four NIST standardization competitions. Our benchmark consists of three tiers: (i) primitives with known practical breaks; (ii) primitives with no known practical break, evaluated both at full strength and as scaled-down variants; and (iii) a challenge set of production primitives at the frontier of cryptanalysis. Five frontier models (Claude Opus 4.8, Sonnet 5, Mythos 5, GPT-5.5, and the open-weights GLM-5.2) break 65%­86% of Tier 1 schemes, 6­12 Tier-2 schemes at full strength, and 24­61 across all scaled-down variants. Beyond deriving known results, models produce novel cryptanalysis, such as a key-recovery attack that exploits a design flaw in the SpoC AEAD and an error in KINDI’s published CCA-security proof, both to the best of our knowledge not previously known...

Across New York City, London, and Washington, DC, ads for Meta's smart glasses have been plastered over with satirical posters from activist groups. One guerrilla ad calls these "the biggest advancement in pervert technology since the trenchcoat." Another transposes the words "mass surveillance predator glasses" over influencer Kylie Jenner's face, calling her out for partnering […]