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By the end of this page you can read a budget vs actual table without being fooled by it, and build one in Excel that does not fool anyone else. You will know the variance formula, why analysts write F and U instead of trusting plus and minus, the two ways percent variance lies, and how to say the whole table in one sentence. It is about twenty minutes. Here is what to actually do today. Open the last variance table you were sent and find its biggest percentage. Then find its biggest dollar amount. If they are different rows, and they usually are, you now know which row deserved the attention, and it is probably not the one that got it. The short version: variance is actual minus budget. On a revenue line, positive is good. On a cost line, positive is bad. So analysts label every line F for favorable or U for unfavorable, rank by dollars, and flag by percent. The sign flip is the trap people fall into first, so it gets the picture. The original carries a diagram here. In words: Two panels, each showing a pair of vertical bars rising from a shared baseline. In the left panel, labeled revenue, a shorter bar marked budget stands next to a taller bar marked actual. The extra height of the actual bar above the budget level is shaded in the accent color and marked with the letter F and a check mark, because collecting more revenue than budgeted is favorable. In the right panel, labeled cost, the bars have the same shapes: a shorter budget bar next to a taller actual bar. But here the extra height above budget is shaded in the warning color and marked with the letter U and a cross, because spending more than budgeted is unfavorable. A dashed horizontal line runs across each panel at the budget height. The two panels are geometrically identical, and only the meaning of the line decides whether the overshoot is good or bad. That is why the sign of a variance cannot be read without knowing the line type. Every number on this page is verified. The worked example is a small dep

By the end of this page you can say, out loud and in your own words, what every core operations number does. What the unit of work is. Throughput, and why a count on its own answers nothing. Cycle time, and the rule that ties it to how much work is sitting open. Backlog. Utilization, and why aiming for 100 percent makes everything slower. Error rate, rework and first pass yield. Service levels, and why the average hides the customers you are failing. That list is most of what an operations analyst job, a technical screen, and a first real dataset will ask of you. Here is what to actually do with it. Go through once end to end without stopping, just for the shape. Then come back to the retrieval sheet near the bottom, cover the right-hand column, and try to say each answer before you read it. That second pass is where the learning happens, and there is measured evidence for it further down. The short version: operations analytics is the study of how work moves through a process. Every number in it is either how much, how fast, how much is stuck, or how much was wrong. One idea decides more of your operations work than any other, so it gets the picture. Work arrives, waits, gets done, and leaves. How much is in progress and how long each item takes are two different spans over that same picture, and they are locked to each other. The original carries a diagram here. In words: A left-to-right process diagram. On the far left an arrow labelled "arriving" points into a row of three small stacked boxes labelled "waiting", representing a queue. An arrow leads from the queue into a single larger rounded box labelled "working", representing the person or machine doing the job. A final arrow leads out of that box to the right and is labelled "done". Above the queue and the working box, a bracket in a strong accent colour spans both and is labelled "in progress", showing that work in progress includes everything waiting as well as everything actively being worked on. Below, a

When this workbook is finished, you can change one number and watch the whole thing follow. Move a cut-off from 65 to 70 and every row re-bands, every fill recolors, every count updates, and the legend still matches the map. Nobody can color a cell by hand, because no cell has a color of its own. That is the whole trick, and it takes about twenty minutes to build. The example here is a security risk index across twenty sites. The same shape works for vendor scoring, lead scoring, incident triage, or any list where a number has to turn into a label and a color. The fault, and where it actually comes from You have met this file. A scored list, colored by hand, that nobody quite trusts any more. Look closely and the same faults turn up every time: Two rows score 61.4. One is amber, one is yellow. The same band is drawn in two shades, because two people picked from the palette on two different days. A row sits below the cut-off and is colored red anyway, because somebody knew that site was a problem. A score lands exactly on 65, which appears in two bands, so the answer depends on who typed it. One row has no band at all. It quietly drops out of every count. These look like five separate mistakes. They are one mistake, five times. The rule lives in the formatting instead of in a column. A color is not a value you can test. You cannot write a formula that asks "is this row the right shade of amber," so nothing checks it, and it drifts. The test: can you sort by band? If the band is only a color, you cannot sort it, count it, or filter it, and neither can anybody else. That is the tell. The chain: score, then band, then color Everything below is one idea applied three times. Each thing is derived from the thing before it, and only the first one is typed. Layer Where it lives Who decides it Sub-scores Four columns, one per category Your source data. Typed once. Composite score A formula, from the sub-scores and the weights The weights row Band A formula, from the score The

Criminals are hacking into public Wi-Fi devices—at hotels, conference centers, and so on—around the world and changing their DNS settings. The goal is to redirect users to fake login pages and steal their credentials.

In this podcast, Michael Stiefel spoke to Tracy Bannon about the role of artificial intelligence in software and the attendant risks in the areas of security, software development, and society at large. While it might be reasonable to assume a certain amount of trust within a software ecosystem, the risks escalate when the boundary between two software ecosystems is crossed. By Tracy Bannon

Deterministic rules safeguard hard metrics, but what about architectural intent? Discover how agentic fitness functions combine AI agents and versioned rubrics to evaluate complex, judgment-heavy concerns—such as boundary fidelity, semantic contract drift, and stale ADR assumptions. Elevate evolutionary architecture governance with continuous, calibrated feedback loops. By Hemant Kumar Mahato, Łukasz Sieczkowski, Vijayasenthilkumar Kuppusamy

Researchers found that a metabolite produced by gut bacteria can weaken the barrier that protects the brain and promote changes associated with the cognitive disease.

When Xander first met Moxie, she taught him that when he was anxious, he could calm down by exhaling through his lips so that he buzzed like a bee. They practiced breathing like dragons to manage feeling mad and sniffing like bunnies to boost his energy. But in the six years they’ve known each other,…

How to Host a Website Using GitLab Pages If you have a website made with HTML and CSS, you can host it for free using GitLab Pages . GitLab Pages takes the files from your GitLab repository and publishes them as a website. For this, you need to create a .gitlab-ci.yml file. This file tells GitLab how to deploy your website. After pushing the file to your repository, GitLab creates a pipeline. When the pipeline finishes successfully, GitLab Pages gives you a URL which you can open in a browser to see your live website. Understanding the Pipeline A pipeline is the process GitLab uses to run the instructions written in .gitlab-ci.yml . If the pipeline fails, the website will not be deployed correctly. Sometimes the pipeline can fail because of an invalid YAML file, incorrect indentation, or a problem in the deployment commands. Another common problem is trying to create a public folder when the folder already exists. For a simple HTML and CSS website, the important thing is that the public folder contains your website files and index.html should be directly inside it. For example, the structure should look like this: public/ ├── index.html ├── style.css └── images/ The index.html file is important because it is the main page GitLab Pages looks for when someone opens the website. Hosting More Than One Website You can host multiple websites using GitLab Pages, but if the websites are completely different projects, it is better to create a separate GitLab project for each website . For example, you can have one project called youtube-clone and another project called portfolio . Each project can have its own HTML, CSS, .gitlab-ci.yml , pipeline and Pages deployment. This makes the projects easier to manage and prevents one website from affecting another website. So, GitLab is not only a place to store your code. With GitLab Pages and CI/CD pipelines, you can also use it to turn your HTML and CSS project into a live website that can be accessed through the internet.

Most social-media automation tutorials stop at “call the API on a cron job.” That works, but it leaves the hard questions unanswered. Which account is the automation using? How does it avoid posting the same story twice? What happens when an API request times out after X has already accepted the post? And where should an AI agent’s editorial freedom end? I recently built a scheduled X publishing workflow with Codex and xurl , the official command-line client for the X API. The result is not just a timer attached to an AI prompt. It is a small publishing system with four distinct layers: An X developer application with read-and-write user authentication. xurl , which stores the credentials and communicates with the X API. A fixed-account Codex skill that verifies the identity before every write. A Codex scheduled task that researches, checks history, drafts, and publishes. That separation is the important part. Codex can make editorial decisions, but it cannot casually choose an account or improvise the publishing command. The skill owns the deterministic write boundary, while the scheduled task owns timing and editorial policy. In this article, I’ll show you how to build the same architecture. X developer settings, API packages, Codex features, and command-line options can change. The workflow below was verified in August 2026, but you should check the current upstream documentation before using it in production. What You Will Need Before starting, you will need: Codex on a Mac with access to Scheduled tasks. An X developer account and an application with read-and-write permissions. Homebrew. A dedicated or clearly identified X account for the automation. A local project containing the source material or editorial context the agent should use. You should also decide what the automation is allowed to publish before you give it access to an account. A good editorial policy is specific enough to reject a story, not merely broad enough to describe a topic. For example,

I have a problem with my social media feed: there are some people whose content I find really valuable, but on the other hand, there is too much trivial content, so I waste too much time watching low-value videos... BAD DEAL!!! So, let’s solve this issue using AI automation. We can solve this issue by using Telegram as our own social media platform and using automation to make our own feed, far from the algorithms of social media platforms. Choose the people whose content you find really valuable. Use AI automation to monitor them whenever they publish a new video. Once a new video is published, AI sends it to a Telegram bot where you can download it. The person who gave me this idea is Abo Ziad (the Egyptian version of Ali Abdaal), and he is one of my favorite YouTubers. Personally, I think you can add some entertaining channels to make it more balanced. I’ll try this solution for one month, and if you’re interested, I’ll share my experience with you, whether it succeeds or fails. In the end, it’s up to you. Tell us in the comments if you found the general idea useful or not, and whether AI automation is the best solution for this issue.

On the latest episode of Equity podcast, we discuss why not everyone is buying Zuckerberg’s vision.

Quieres que tu app hable español e inglés. Buscas cómo, y el ecosistema te empuja a gettext o Babel: ficheros .po , un paso de compilación a .mo , herramientas de extracción. Potente, sí. Pero para una app pequeña o mediana es un peaje que no querías pagar — solo necesitabas un t() honesto. Lo resolví tantas veces que lo empaqueté: dotkey-i18n , Python puro, sin dependencias. Tus traducciones son JSON que cualquiera puede editar: // locales/es.json { "login" : { "welcome" : "Hola, {name}" , "submit" : "Entrar" }, "menu" : { "reports" : "Informes" , "settings" : "Ajustes" } } from dotkey_i18n import Translator tr = Translator ( " locales " , default_lang = " es " ) tr . t ( " login.welcome " , name = " Juan " ) # "Hola, Juan" tr . t ( " menu.reports " , lang = " en " ) # "Reports" Tres detalles que marcan la diferencia Claves con notación de punto. t("login.submit") navega el JSON anidado. Agrupas las cadenas por pantalla o módulo sin claves planas kilométricas. Fallback al idioma por defecto. Si una clave falta en el idioma pedido, se busca en el idioma por defecto antes de rendirse. Tus traducciones pueden ir incompletas —la vida real— sin dejar huecos en blanco en la interfaz. Nunca revienta la interfaz. Una clave que no existe devuelve la propia clave (un marcador visible, no una excepción a mitad de render). Una interpolación con un campo que falta devuelve el texto sin formatear. Un JSON corrupto se trata como vacío. Nada de esto tumba la pantalla. Agnóstico del framework El idioma actual entra por un lang_getter inyectable, así el mismo Translator sirve en NiceGUI, Flask, FastAPI o un script suelto: # NiceGUI: idioma desde la sesión del usuario tr = Translator ( " locales " , default_lang = " es " , lang_getter = lambda : app . storage . user . get ( " idioma " )) # Flask tr = Translator ( " locales " , lang_getter = lambda : session . get ( " lang " )) La prioridad es clara: lang= explícito → lang_getter() → idioma por defecto. De dónde viene Salió del servic

Let's look at how to use the Retro app, whether it's free and whether it can compete with other social media apps.

Today I did something I've wanted to do for a while — I took an app running on my own laptop and put it "live" on the internet using AWS. It sounds scary when you read about it online, but once I actually did it, it was just a bunch of small, simple steps, one after another. This post is me writing down everything I did, in plain, easy words, so that if you're a beginner like me, you can follow along without getting confused by fancy tech terms. What is AWS, in simple words? AWS (Amazon Web Services) is basically Amazon renting out computers over the internet. Instead of buying your own physical server and keeping it running 24/7 at home, you "rent" a computer from Amazon. That computer runs your app, and anyone with the internet can visit it. The specific service I used is called EC2 . Think of EC2 as one virtual computer that lives in Amazon's data center, and you get to control it like it's your own. Step 1: Set up IAM first Before touching any servers, I went to IAM (Identity and Access Management). This is AWS's way of managing "who is allowed to do what" in your account. In simple words: instead of using your main AWS login for everything (which is risky), IAM lets you create a separate user with its own permissions. It's like giving someone a spare key instead of your master key. I set this up first so my account stays safer. Step 2: Launch an EC2 instance Next, I went to the EC2 section and launched a new instance (a fancy word for "a virtual computer"). During this step, AWS also lets you create a .pem file — this is basically a secret key file. It's like a digital key to a lock. Only someone with this file can get into the server. I downloaded it and kept it safe, because if you lose it, you can't easily get back in. Step 3: Login to the server using SSH Once the server (EC2 instance) was ready, I needed a way to "log in" to it from my own laptop. For that, I used something called SSH, along with the .pem key file I downloaded earlier. In simple words: SSH

Disclosure: I build Receiptful, the printing API used in this tutorial. The Node and Express parts apply whatever you print with. You have orders coming into your point of sale, and you want each one to print on the thermal printer at the counter. This is a complete walkthrough of a small Node service that does exactly that. By the end you will have an endpoint you can POST an order to and watch paper come out. There is nothing to install next to the printer for this tutorial to work, and no ESC/POS to write by hand. You send HTML, Receiptful prints it. Before you start You need two things from the console : A paired printer, which gives you a printer ID . If you have not done this yet, the getting started guide walks through it in a couple of minutes. An API key (the rf_live_… value), created under API keys and shown only once. On the code side you need Node 18 or newer, so that fetch is available globally with no extra dependency. We will use TypeScript, but the same code works in plain JavaScript if you drop the types. Put your credentials in the environment rather than in the source: export RECEIPTFUL_API_KEY = "rf_live_3f9c…" export RECEIPTFUL_PRINTER_ID = "42" Step 1: model the order Start with the shape of an order. Yours will have more fields, but this is enough to print a useful receipt: interface LineItem { name : string ; quantity : number ; unitPrice : number ; // in cents, to avoid float rounding } interface Order { id : number ; items : LineItem []; placedAt : Date ; } Keeping money in cents and formatting only at the edges saves you from the classic floating point rounding bugs that show up as a receipt total that is one cent off. Step 2: render the order as HTML This is the part that decides how the receipt looks. Receiptful converts the HTML you send into ESC/POS for your specific printer, so you get to lay a receipt out with tags you already know instead of byte codes. function money ( cents : number ): string { return " $ " + ( cents / 100 ). toFi

Cómo solucionar docker run con Exited (1) en Raspberry Pi ¿Por qué ocurre este error? El código de salida 1 indica que el proceso principal del contenedor terminó con un error genérico. En Raspberry Pi, los casos más comunes son: Arquitectura incompatible : La imagen fue construida para amd64 (x86_64), pero Raspberry Pi usa arm32v7 o arm64v8 . Falta de binarios compatibles : El ENTRYPOINT o CMD del contenedor intenta ejecutar un binario compilado para otra arquitectura. Problemas de permisos o dependencias faltantes en el entorno embebido (especialmente en Raspberry Pi OS Lite sin GUI). Uso incorrecto de --net=host : En algunas versiones de Docker en Raspberry Pi, el flag --net=host puede causar fallos si el sistema no lo soporta correctamente. 🔍 Nota crítica : En tu comando original docker run --net = host -d -t myimage , hay un error de sintaxis: --net = host tiene espacios alrededor del = . Docker lo interpreta como un nombre de red literal " = host" , lo que probablemente falla. Pasos para solucionarlo Paso 1: Corrige la sintaxis del comando # ❌ Incorrecto (con espacios en `--net`) docker run --net = host -d -t myimage # ✅ Correcto (sin espacios) docker run --net host -d -t myimage ⚠️ Importante : En Docker CLI, los flags con valores no deben tener espacios entre el = . Usa --net=host o --net host , pero nunca --net = host . Paso 2: Verifica la arquitectura de la imagen Ejecuta en tu Raspberry Pi: docker inspect myimage --format '{{.Architecture}}' Si el resultado es amd64 , la imagen no es compatible con Raspberry Pi . Solución: Reconstruir la imagen para ARM Si tienes el Dockerfile , usa multi-arch build: # Al inicio del Dockerfile (antes de FROM) # syntax=docker/dockerfile:1 FROM --platform=$BUILDPLATFORM golang:1.21-alpine AS builder ... O construye explícitamente para ARM: # En tu máquina de desarrollo (x86_64) docker buildx create --use docker buildx build --platform linux/arm/v7 -t myimage:armv7 . --push # o para Pi 4 (64-bit): docker buildx build --platf

I've watched too many TradingView strategies look great in the Strategy Tester and then fall apart the moment real money went live. Almost every time, the code compiled fine. The bug wasn't syntax. It was repainting, the script quietly using information it shouldn't have had yet. Repainting doesn't throw an error. It just quietly makes your backtest better than your live trading will ever be. Here are the four places it actually comes from, and how to catch each one before you trust a strategy. 1. request.security() with the wrong lookahead If you pull a higher-timeframe value with request.security() and don't handle the offset correctly, the current, still-forming HTF bar can leak into your calculation. The fix is barmerge.lookahead_off combined with offsetting the source by one bar, e.g. close[1]. lookahead_on is only safe when you've already offset the source yourself. Using it directly on a live value is the single most common repaint source in Pine scripts posted online. 2. Signals computed before the bar closes If your entry logic runs on close or ta.crossover() without a barstate.isconfirmed guard, the signal can appear, then disappear, then reappear as the candle's still-forming close price changes. What you saw fire in real time is not always what the finished bar actually did. Guard any entry/exit logic that matters with barstate.isconfirmed if you're evaluating it intrabar. 3. Same-bar stop/target ambiguity When your stop and your target could both have been hit inside the same bar's high-low range, the Strategy Tester has to guess which one happened first. It doesn't always tell you which assumption it made, and that one hidden assumption can flatter your win rate without you ever seeing it happen. 4. Bar Replay is the real manual test TradingView's Bar Replay tool is the closest thing to a repaint detector you already have. Step through history bar by bar and watch whether a signal that appeared in the past matches what you originally saw. If a signal m

Hello Guys!!! I am Kuldeep Gade. A final-year Computer Engineering student with a specialization in Cybersecurity. Currently, I am working on home lab automation so that it will help to encounter alerts (false positives). For practice, I have created a controlled environment for performing attacks and detecting them, such that the outside doesn't get affected. Working on projects which will enhance my cybersecurity skills. But I wasn't that obsessed with cybersecurity from the starting. I am a person who experimented with lots of domains by myself. When I was in the first year, I completed Full-Stack in MERN. For 1–1.5 years, I did that, but after some time, AI got so much power that within 3–4 months of the launch, they were able to create such stunning websites that needed a team of skilled people. And I thought it could be useless to go deep into MERN more, because if AI can do such things within months, then what is going to happen at the time of my graduation? And that's the reason I tried other things. So I realised that it could be better to gain the fundamental knowledge in the core of Domains that will automatcally get to implementation level with the help of the AI tools. So I started to learn Data Science and Machine Learning. Soon, I realised that I cannot keep up with it. Then I started with cybersecurity. And currently, I am going deep into it. As a result, I got my answer, and now I am a bit focused towards the cybersecurity domain. It was a tremendous feeling about knowing the root of the system on which we are working. How to troubleshoot the errors and problems. And I am loving it now. Gaining experience in this field is not just learning and watching tutorials. We have to perform hands-on practice. We have to learn by doing things, breaking systems, understanding workflows, rebuilding them. I am going to share my experience in the field as we go in upcoming blogs. Recently, I started my new goal to "read the books". And did some research on books.

Adapted from the SQL Essentials Companion Guide . You write a query to find every customer with no phone number on file. WHERE phone = NULL looks obviously correct — and it returns zero rows, even though you can see NULL sitting right there in the column. Nothing crashes. No error. The query just quietly lies to you about what's in the table. This isn't SQL being broken. It's SQL being consistent about something most languages don't force you to think about: NULL doesn't mean "nothing," it means "unknown." And you can't compare something to unknown with = and expect a real answer. What's actually happening Take this table: -- customers | id | name | phone | | ----|-------------|------------| | 1 | Jordan Lee | 555 - 0142 | | 2 | Sam Rivera | NULL | | 3 | Alex Chen | 555 - 0198 | SELECT name FROM customers WHERE phone = NULL ; -- returns 0 rows SQL doesn't evaluate conditions as just true or false — it has a third result: unknown . phone = NULL asks "does this unknown value equal this other unknown value?" There's no way to answer that, so SQL returns UNKNOWN for every single row, including Sam Rivera's. And WHERE only keeps rows where the condition is TRUE . UNKNOWN doesn't qualify, so the row gets filtered out — the exact same as if it had evaluated to FALSE . This is true even for the row that "should" match. NULL = NULL isn't TRUE — it's also UNKNOWN . NULL never equals anything, not even another NULL . That's the whole rule, and it applies uniformly, which is why = can't be patched into working here — it's not almost right, it's answering a different question than the one you're asking. The fix, step by step Recognize the symptom : a query that runs cleanly but returns fewer rows than it should — especially zero rows when you can see matching data — with a NULL column somewhere in the WHERE clause. Swap = for IS NULL (or != for IS NOT NULL ). These are dedicated operators built specifically to test for absence, not comparison operators being asked to do somethin