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Finding reveals an overlooked way that water can damage surfaces.

“One more warning sign here of what we're facing in a warmer world.”

"We would need over half a million 4K TVs to fully display the single Roman image from our largest survey."

The app isn't cracking down on other kinds of AI slop, though.

Instagram is finally taking steps to address the rise of fake AI-influencer accounts that have gotten harder to spot. It's also renaming the "AI creator" label to "AI-generated profile" to make it clear when a profile features an AI-generated person that's not a real human being. "We've heard that people don't like seeing a profile […]

Circleback is also introducing new pricing plans starting from $14 per month

While Trump administration cuts have contributed to the epidemic of foodborne illnesses this summer, there are also deeper issues that make the American food supply chain susceptible to parasites and bacteria outbreaks.

In this episode, Scott Jenson, a veteran UX designer known for his work on the Macintosh, Google Maps, and Chrome examines the long-term stagnation of desktop operating systems and the limitations of current mobile and cloud-centric models. By Scott Jenson

Of the Glassdoor reviews from claims adjusters that mentioned AI, a staggering 98 percent were negative. “AI is just a tool,” one person tells WIRED. “It should never be given the keys.”

If you've done LeetCode's Design HashMap , you've implemented put , get , and remove . What that exercise usually skips is the part that actually breaks in production: what happens when someone mutates the map while another piece of code is iterating over it. I ran into this directly while building MyHashMap , a from-scratch single-threaded HashMap (separate chaining, resize on load factor). Getting put / get / remove right was the easy 80%. Getting entrySet().iterator() to correctly detect concurrent mutation — including the case where a second, completely separate iterator is the one that should notice — took three wrong turns before landing on the pattern the JDK actually uses. The problem, concretely Iterator < Entry < K , V >> it = map . entrySet (). iterator (); it . next (); map . put ( someNewKey , someValue ); // structural change, mid-iteration it . next (); // ??? — undefined behavior if we don't guard against this Without a guard, next() might return a stale entry, skip entries entirely, or throw an unrelated exception depending on internal bucket-array state. Java's real collections handle this with ConcurrentModificationException (CME) — but the interesting part isn't the exception, it's the mechanism that detects when to throw it. First idea: a boolean "dirty" flag Obvious first attempt: a boolean modified field on the map, flipped to true on any put / remove , checked by the iterator. This works for exactly one iterator. It falls apart the moment two iterators are alive at once: Iterator A calls next() , sees modified == false , proceeds. Something else mutates the map. modified flips to true . Iterator B — created after that mutation — checks the same shared modified flag, sees true , and incorrectly throws, even though nothing has changed since B was created. A single shared boolean can't represent "changed since this specific iterator was created" for more than one iterator at a time. Resetting it on read doesn't help either — now the other iterat

After funding struggles and a name change, the Nancy Grace Roman Space Telescope has been successfully launched. It will now make a three-month, one-million-mile journey to its orbit at the second Sun-Earth Lagrange point (L2) beyond the Moon. From this vantage point, Roman will conduct an unprecedented survey of the universe. Its field of view […]

As I have started my Data analysis and science curriculum, Microsoft excel is the first tool we have learnt. In my life before getting into this course, I used to underestimate the power of Microsoft excel. Definition of Microsoft Excel This is a spreadsheet software developed by Microsoft that allows you to collect, organize, analyze, calculate and visualize data efficiently. The Excel Interface When open Microsoft excel, you first an interface made of various tools We start with the ribbon. The ribbon contains a toolbar that contains all the commands organized into tabs such, Home, Insert, Page Layout , Formulas, Charts and Data There is a formula Bar that content and formulas appear. In addition, we have Quick access toolbar that contains icons for Save, Undo and Redo. A row is a horizontal line of cells that runs from left to right across the grid while a column is a vertical line of cells that runs from top to bottom down the grid. A cell is where row and column intersect and form a single box. Cleaning A Dirty Dataset As we learnt about Data cleaning with Excel on dataset that was a bit very disorganized. We first started with sorting that data which basically means arranging the data in specific order. The sorting can be Text sorting which is A-Z or Z-A, Number sorting which is from largest to small and vice versa, Date sorting which is Oldest to Newest and vice vera. Next was filtering which allows you display data that meet certain criteria and hide the rest temporarily. Next, Number formatting changes how numbers display without altering their actual value. Conditional Formatting highlights cells automatically based on rules or criteria, helping spot trends or outliers. The dataset we were cleaning had a lot of duplicates, so it was important for us to remove the duplicates. Text functions for Cleaning Data Text functions are used to remove extra spaces , standardize text ,extract parts of strings or find and replace sub strings. text functions include UPP

Hello, I'm Maneshwar, and I'm building LiveReview — a blast-radius aware AI code review built for...

Detecta y neutraliza estafas telefónicas con voces deep‑fake: guía práctica y herramientas de clonación vocal Introducción Imagina que recibes una llamada de tu jefe pidiéndote una transferencia urgente… pero la voz es una réplica perfecta generada por IA. En 2024 esa escena ya no es ficción: las estafas con voces deep‑fake están a la orden del día en EE. UU., Europa y América Latina. Gracias a plataformas como ElevenLabs , iSpeech o a modelos de código abierto como Coqui‑TTS y VITS‑OpenAI , crear una copia casi idéntica de la voz de cualquier persona cuesta menos de 100 USD y se hace en cuestión de minutos. En este artículo verás cómo funciona la tecnología , cómo identificar una voz sintética y qué medidas tomar tanto si eres usuario particular como si gestionas la seguridad de una empresa. 1. Tecnologías de clonación vocal: panorama rápido Tipo Ejemplo Precio (mensual) Necesita GPU? Privacidad Comentario SaaS (API) ElevenLabs, iSpeech, Resemble AI $0‑$49 (plan básico) No Los datos se envían a la nube Fácil de integrar, ideal para pruebas rápidas OSS (auto‑alojado) Coqui‑TTS, Mozilla TTS, VITS‑OpenAI Gratis (coste de infraestructura) Sí (GPU recomendada) 100 % bajo tu control Requiere instalación y ajuste fino 2. Detecta una voz deep‑fake en 3 pasos (y sin ser ingeniero) Paso 1 – Escucha los “errores humanos” Señal auditiva Qué indica Respiraciones muy cortas o inexistentes Síntesis sin modelo de respiración Entonación monótona en frases largas Falta de variabilidad prosódica “Cortes” o chasquidos en palabras compuestas Artefactos de concatenación de fonemas Falta de “cierre” de consonantes (p. ej. “s” muy suave) Modelo TTS de baja calidad Paso 2 – Analiza el espectro con una herramienta gratuita # Instala DeepSpeech-detect (Python) pip install deepvoice-detect # Analiza la llamada guardada como audio.wav deepvoice-detect audio.wav --output report.json El archivo report.json contiene un score de probabilidad (0‑1). Valores > 0.7 suelen corresponder a voces generad

The discovery redefines how large atoms need to be to produce the extreme state of matter found in the early universe.

A practical validation view for metric intervals, dimensions, notifications, suppression, and operator action An OCI alarm can be created correctly and still fail as an operating control. The issue may not be the alarm object itself. It may be the metric interval, a wrong dimension, an unconfirmed notification subscription, an absence query that fires for the wrong reason, or a message that reaches the team without telling them what action to take. This article is written from an OCI readiness and validation point of view. It is not a step-by-step Console walkthrough. The aim is to identify checks that should be confirmed before an alarm is treated as Production-ready. 1. The metric interval does not match the metric emission frequency This is one of the easiest alarm issues to miss. OCI documents a troubleshooting case where an alarm fires and then clears at the next interval even though the last data point exceeded the threshold. The cause is that the alarm interval is shorter than the metric's emission frequency. For example, if a metric is emitted hourly, an alarm interval that evaluates every minute may not behave the way the owner expects. The readiness check is to confirm the metric's documented emission frequency and set the alarm interval at the same frequency or longer when needed. Validate: Metric namespace and metric name Emission frequency for the selected metric Alarm interval in the MQL query Statistic used, such as mean, max, sum, or percentile Expected FIRING and OK behavior during testing Evidence can come from a metric-documentation or chart review, the alarm query, and a test record showing expected state behavior. The question for the owner is direct: does the alarm evaluate data at a rhythm that the metric can actually provide? 2. Dimensions include the wrong resource, or exclude the right one A metric query can be technically valid and still point at the wrong scope. In OCI Monitoring, dimensions help narrow metric streams. That is useful, but

1. Introduction Excel is much more than a spreadsheet for entering numbers. It can be used as a data-analysis tool that helps analysts inspect, validate, filter, summarize, and prepare raw data before deeper analysis begins. In typical analytics, the quality of the final work depends heavily on the quality of the data used; therefore, data cleaning is not an optional step—it is the foundation of effective data analysis. This article demonstrates key Week 1 Excel concepts _using an employee dataset containing _employee IDs, names, departments, gender, marital status, hire dates, salaries, educational level, performance score among others. The raw file intentionally contains common data-quality issues: inconsistent capitalization on the First and Last names, blank records, duplicate employee records, varying department names, currency and dates that need review. By working through these issues, the article shows how Excel’s formatting tools, text functions, filters, conditional formatting, numerical functions, conditional summaries, and date functions can turn a messy workbook into an analysis-ready dataset. 2. Why Data Cleaning Matters Data cleaning is more than just about removing errors. By standardizing formats and categories, we make datasets more transparent, usable, and valuable for management analysis and reporting purposes. Data analysis is simple – garbage in, garbage out. A dashboard or prediction can appear professional, but can be misleading if the underlying data has duplicates, blank values, inconsistent categories or incorrectly formatted text and dates. For example, “IT” “I.T.” and “Information Tech” can be viewed as different department values if naming is not standardized. Duplication of an employee ID can inflate employee counts and department totals. A blank performance score might mean that something is missing and should be looked into and dates saved as text cannot be reliably used in calculations such as employee tenure checks. A good practice

Line 267 of a 427-line Terraform plan: # aws_rds_cluster.reporting must be replaced - /+ resource "aws_rds_cluster" "reporting" { ~ arn = "arn:aws:rds:us-east-1:842910557412:cluster:reporting" - > ( known after apply ) ~ cluster_resource_id = "cluster-D85642F9611A" - > ( known after apply ) ~ engine_version = "14.9" - > "15.4" ~ id = "reporting" - > ( known after apply ) ~ storage_encrypted = false - > true # forces replacement # (29 unchanged attributes hidden) } The merge request says "bump reporting Postgres to 15.4." The plan does exactly that. It also destroys the reporting database and creates an empty one in its place. Underneath the known-after-apply churn, two attributes are changing. One is the version bump, the thing your MR is about. The other is storage_encrypted flipping from false to true , and it isn't yours. Someone on another team that shares this repo merged it earlier in the week. You're just the one deploying. You review other people's Terraform MRs and have a feel for what each stack normally does; most weeks someone else shepherds the deploy. Today it's you. Your change goes out next, so you're carrying everything merged since the last deploy, including work you never reviewed and had no reason to know about. Nobody was negligent. The queue simply had someone else's change in it. It's a good change, by the way. You want encrypted storage. But there's no in-place path from unencrypted to encrypted on an RDS cluster. Terraform's only move is destroy and create. That's what -/+ means, and the comment at the end of the line says it in plain English: forces replacement . And the version bump alone would have failed. Going from 14 to 15 is a major version upgrade, and Aurora refuses those unless the config sets allow_major_version_upgrade = true . This one doesn't. That MR by itself would have died at apply, loudly, with an error naming the exact problem. A replacement doesn't upgrade anything. It creates a new cluster at 15.4 from scratch, so the f

Astronomers will test equipment that, if it’s successful, will one day be crucial for discovering Earthlike planets.

Installing a large language model on your personal computer gives you a handy digital assistant that won’t compromise your data privacy.