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Open source WeatherNext model can make accurate predictions with lower-resolution weather data.

New AI toolbars and prompts are showing up in Google Docs and Gmail. If you don’t want Gemini’s help in writing documents and emails, here’s how to turn that stuff off.

Magento 2's full page cache is one of its strongest performance features — when it works. But every week, we see stores where a simple product save triggers a 30-second Varnish flush and subsequent cache stampede. The culprit is almost never Varnish itself. It's cache tags. This post covers how Magento 2 cache tags work, why broad tags destroy performance, and exactly how to audit and fix them. How Cache Tags Work in Magento 2 Every cached page, block, and data fragment in Magento is tagged with identifiers. When a product changes, Magento invalidates all cache entries tagged with that product's ID. The tag system is hierarchical: cat_p_123 — specific product cat_p — all products cat_c_5 — specific category cat_c — all categories cms_b_about_us — a CMS block cms_p — all CMS pages These tags are stored alongside cached content and used during invalidation. When you call $cache->clean(["cat_p_123"]) , every cache entry tagged with cat_p_123 is removed. This is elegant until someone tags a global block with cat_p , and saving any product flushes half your store. The Invalidation Storm Problem Here's what happens during a storm: Admin saves a simple product update (price change) Magento generates the invalidation list: cat_p_456 , cat_c (because the product is in categories), cat_p (from a badly written block) cat_p is too broad — it matches the product list page, layered navigation, homepage widgets, and every product detail page Varnish receives 50,000 BAN requests Store goes from sub-100ms response times to 2-5 seconds for the next 10 minutes while the cache rebuilds We've seen this on a store with 80,000 SKUs. A single product save dropped cache hit rate from 94% to 12%. Diagnosing Bad Cache Tags Check Your Current Tags Add this to any block template to inspect what tags are being applied: $block -> getCacheKeyInfo (); // Or for the full page: $block -> getIdentities (); For a full audit, intercept cache writes in development: // In di.xml: < type name = "Magento\Fr

A new software update builds on WhatsApp’s group chat chops by adding anonymous polls and introducing an easier way to start a smaller private chat away from the larger group.

Martin Spier explains how agentic workflows dramatically increase code change volume at OpenAI. He discusses the hidden systemic performance costs of rapid shipping beyond GPUs, and shares how deploying always-on AI agents automates profiling, regression detection, and continuous optimization to maintain product speed and scalability at massive global scale. By Martin Spier

A build note from Horizon Software , a one-person Android studio. WanderWallet is a travel budget app, and the whole thing runs on the phone: no account, no backend, no cloud. Here's how the parts that look like they need a server actually work without one. The one constraint that shaped everything WanderWallet has a single non-negotiable rule: it has to work with no signal. You're three countries into a trip, your phone's in airplane mode to dodge roaming charges, and you still need to know whether you're on budget. That one requirement quietly makes most of the architectural decisions for you — no login, no server round-trips, and every feature that would normally lean on a cloud API has to earn its keep another way. The stack is deliberately boring: .NET MAUI (Android-first), CommunityToolkit.Mvvm , sqlite-net-pcl for storage, and SkiaSharp for anything I draw myself. Everything the app records lives in a local SQLite database on the device and nowhere else. "Backup" is a file you export and keep — there's no server to back up to . The three features people assume need a backend turned out to be the most interesting to build, precisely because they don't. 1. Currency conversion that survives airplane mode A travel budget app that can't convert currencies offline is useless at exactly the moment you need it. So rates aren't fetched on demand. Whenever the app happens to have a connection it refreshes exchange rates for ~155 currencies and caches the whole table locally . From then on every conversion is local arithmetic — a connection only ever buys you a fresher table, never the ability to convert. The design decision that took me longest to get right: capture the conversion immutably, at entry time. Each expense stores the original amount, its original currency, the converted home-currency amount, and the exact rate used — and that rate is never recalculated: public class Expense { public double Amount { get ; set ; } // in OriginalCurrency public string Origina

Copernicus Browser adds wildfire visualization amid record wildfire season.

Researchers found common points of failure, like software used to organize and display web content, could have allowed hackers to run riot through government websites.

Cloudflare has introduced Cloudflare Computer, a new open-source runtime designed to give AI agents something closer to a real "computer" instead of just ephemeral containers. It leverages Cloudflare isolates for fast serverless execution, making agents cheaper, faster, and more scalable, according to the company. By Sergio De Simone

Yiyang Zhuge was already an intellectual celebrity in China. Her viral interview with Christopher Nolan made her famous in the US—and quickly turned her into a political Rorschach test.

Collagen and calcium work in tandem to achieve varying mechanical properties of different kinds of mucus.

Nota: en Lumora construimos libros infantiles personalizados con IA, y escribimos en diez idiomas. Este artículo cuenta un problema de i18n con el que chocamos de frente y cómo lo resolvimos. Lo contamos desde dentro, con nuestro nombre, porque el error nos costó reescribir una campaña entera. Casi todos los equipos tratamos el idioma como si fuera el país. Ponemos es en el selector, sacamos las cadenas a un JSON y damos el problema por cerrado. Funciona hasta el día en que tu producto tiene algo que ver con una fecha. Y entonces descubres que es no es un mercado, son varios calendarios distintos que comparten vocabulario. El día en que el regalo no llega el 25 Teníamos una campaña de regalos escrita en español. Correcta gramaticalmente, revisada, sin errores de traducción. Y aun así estaba mal para una parte grande de quien la leía: En España , el gran momento de regalo infantil no es el 25 de diciembre: son los Reyes Magos, el 6 de enero . Un mensaje que dice "pídelo antes del 24" le está dando a una familia española una fecha límite equivocada por casi dos semanas. En México conviven las dos cosas: Navidad y Reyes, con la rosca del 6 de enero. En Argentina, Chile o Uruguay , la Navidad es el 25 de diciembre… en pleno verano . A 30 grados. Con las vacaciones escolares largas empezando, no terminando. Fíjate en lo incómodo del asunto: los tres casos hablan español. Los tres pasan el mismo test de traducción. Y los tres necesitan un mensaje distinto, una imagen distinta y una fecha límite distinta. El mismo golpe existe en portugués. En Brasil la Navidad también cae en pleno verano, enero es mes de vacaciones, y el día en que de verdad se reinicia la rutina familiar no es el 1 de enero: es la volta às aulas , en febrero. Si tu calendario de contenidos asume "año nuevo, hábitos nuevos" en enero, en Brasil llegas un mes antes de que a nadie le importe. Por qué el código de locale no te salva La respuesta obvia es "usa es-ES y es-AR ". Es correcta y casi nunca es sufic

While contributing to Grafana, I picked up a memory issue in the Time Comparison feature — a follow-up to earlier performance work I had done in the same area. A comparison panel was consuming significantly more memory than expected. The interesting part: the extra memory wasn't coming from real data. This post covers how I traced it to the root cause and fixed it. Background Time Comparison overlays an earlier period onto the current one — for example, this week vs. last week. The comparison data is fetched from the earlier window and shifted forward before rendering: Query → DataFrame → Prepare frame → Shift → Render │ └─ Gap filling The important detail: gap filling ran before the comparison frame was shifted. The Problem I reproduced the issue with: Parameter Value Series 500 Window 6h Interval 20s Compare offset 24h A single-period panel contained roughly 540,000 points , so a comparison panel should be about 2× the baseline . Instead, the compare frame contained 3,240,500 points — ~6× the baseline — and consumed 76.4 MB . The question was: where did the extra points come from? Investigation I first verified the baseline to rule out the query returning unexpected data. It was correct. Then I used a reproducible browser harness and a heap snapshot to inspect the extra memory. Most of it was null rows introduced during gap filling — not real samples, not copies. Following the frame through the preparation pipeline revealed why. When gap filling ran, the compare frame still represented data 24 hours in the past , but the gap-filler was using the current time range as its reference: Compare frame Current range [===== 6h =====] [===== 6h =====] └─────────────── 24h ───────────────┘ gap-filler reads this offset as one gap At a 20-second interval, 24 hours is: 24 × 60 × 60 / 20 = 4,320 intervals So up to 4,320 null positions per series were introduced purely because the frame hadn't been shifted yet. The frame was then shifted forward, leaving most of that padding out

The mayor of New York City has assembled a crew of Silicon Valley and United States Digital Service veterans to overhaul city services with better software.

Trump determined to link life-saving shots to autism, despite science and political risk.

Airbnb will debut a new AI-powered search experience with a toggle.

The use of AI systems to create viruses opens up new possibilities for combating bacterial resistance. It also raises concerns about the pace at which technology is outstripping regulation.

Clinicians and researchers say AI companies need to open up their safety data.

TikTok owner training a model with 10 trillion parameters.

Jo Kelly-Fenton and Aleksandar Mitic explain how Spotify created "Honk," an AI coding agent, to handle complex fleet-wide codebase migrations. They share key architectural insights on decoupling CI verification runtimes from AI agents, dealing with automated pull request bottlenecks, and driving aggressive standardization across thousands of engineering repositories. By Jo Kelly-Fenton, Aleksandar Mitic