标签：#ci

On today’s Uncanny Valley, we unpack OpenAI’s ongoing drama, both legal and reputational, and whether these developments could further hurt the company—particularly in its fight against Anthropic.

As wildfire smoke threatens air quality and safety for millions, here’s your playbook to keep your family safe from the damaging effects of smoke.

Cyclosporiasis isn’t the only thing spreading across the US. So is anxiety about getting hit with it.

The company endorsed landmark AI transparency laws in California and New York last year, but its head of US state and local policy says they may already be outdated.

SpaceX deorbiting satellites isn't a cause for worry, but scientists are still looking into the environmental impacts of frequent satellite deorbits.

If the large, rocky planet is losing helium, then we can infer what is left behind.

X will use Grok AI to better detect stolen content, redirect payouts to original creators, and crack down on engagement bait.

"We will likely have similar concerns and discussions when China fields its Starlink-like constellation."

The regulator is assessing whether the platform is doing enough to protect children from harmful content.

Ninja has added everything from ice cream makers to double-decker toaster ovens to its extensive lineup of counter-clogging appliances, but it's only now introducing its first microwave. What makes the new Ninja Crispi Microwave stand out among its many competitors is that it doubles as an air fryer. After heating your food using microwaves, you […]

* When a project first starts, CI/CD pipelines are usually simple. * Build the application. Run a few tests. Deploy somewhere. Done. Then six months pass. Another test gets added. Then another deployment step. A security scan. Performance tests. Notifications. More environments. Before long, a pipeline that once took five minutes now takes forty-five. I've seen this happen more than once, and it's rarely because Azure DevOps is the problem. It's usually because nobody ever stops to ask one simple question: Does this step still belong here? Everything Ends Up in the Same Pipeline One of the most common mistakes I see is trying to make a single pipeline do everything. Every Pull Request ends up running: Every unit test Every API test Hundreds of UI tests Security scans Deployment steps Report generation The result? Developers wait longer for feedback, releases become slower, and people eventually start ignoring failed pipelines because they happen too often. Fast Feedback Wins Not every test needs to run on every commit. A better approach is to think about the purpose of each pipeline. For a Pull Request, I want answers quickly. That usually means: Build the application Run unit tests Run a small smoke test suite Stop if something important fails Everything else can happen later. Long-running regression tests, cross-browser testing and other expensive checks are often better suited to scheduled or nightly pipelines. Pipelines Should Evolve A pipeline isn't something you build once and forget about. Every few months it's worth reviewing it. Ask yourself: Which step takes the longest? Which tests fail most often? Are there any tasks nobody remembers adding? Are we getting useful feedback, or just more output? Removing unnecessary work is just as valuable as adding new automation. Final Thoughts Azure DevOps is an incredibly powerful platform, but even the best tools become frustrating if they're overloaded with unnecessary work. The goal isn't to build the biggest pipel

Despite positioning itself as an anti-hookup app, users tell WIRED that Goose has fake profiles, harsh acceptance standards, and problems with inclusivity.

The updates come as Meta and other tech companies are facing scrutiny from regulators and parents around how AI chatbots respond to users in crisis, particularly teenagers.

Ruth Linehan explains how migrating high-performance caching services from Kotlin to Rust shattered internal preconceptions around delivery velocity and engineering overhead. She discusses the ergonomics of the Rust borrow checker, shares how compile-time safety shortens the developer feedback loop, and profiles how tools like Criterion and flamegraphs optimize concurrent code paths. By Ruth Linehan

Colocar los muebles de una habitación es un problema de optimización con muchas restricciones: la cama no va delante de la puerta, el escritorio quiere luz natural, hay que poder circular. Hay un número enorme de disposiciones posibles. RoomCraft AI las explora automáticamente a partir de una descripción en lenguaje natural. El pipeline de tres etapas Parser con LLM: el usuario describe su habitación en texto libre ("un dormitorio de 4x3 con la puerta al norte y una ventana al este"). Un LLM ( Llama 3.1 vía Groq ) lo convierte en una estructura de datos validada con Pydantic : dimensiones, aberturas, muebles deseados. Latencia: <1s. Optimizador con Simulated Annealing: aquí está el corazón del proyecto. Visualización y export: los layouts se renderizan en 3D en el navegador con Three.js y se exportan como plano técnico en PDF con ReportLab . Por qué Simulated Annealing El espacio de disposiciones posibles es combinatorio y lleno de óptimos locales. Una búsqueda voraz se queda atascada en la primera solución "decente". El Simulated Annealing imita el enfriamiento de un metal: al principio acepta movimientos malos con cierta probabilidad (alta "temperatura"), lo que le permite escapar de óptimos locales; según baja la temperatura, se vuelve cada vez más exigente y converge. Es una metaheurística ideal cuando el espacio de soluciones es irregular y no tienes gradiente. La función objetivo puntúa cada disposición de 0 a 100 según ergonomía: espacio de circulación, relaciones entre muebles, acceso a luz y aberturas. El sistema devuelve el top 5 de layouts, no solo el mejor, para dar opciones. Rendimiento Parse: <1s . Optimización: 2–5s . Export PDF: <1s . Footprint en reposo: ~100 MB de RAM. Qué aprendí Que combinar un LLM (para entender lenguaje) con una metaheurística clásica (para optimizar de verdad) es un patrón potentísimo: el LLM traduce el problema humano a uno formal, y un algoritmo determinista y barato lo resuelve mejor —y de forma más explicable— que pedirle

The group is trying to extend its influence beyond Meta.

I’m sick of “opt-out” toggles for automatically enabled generative AI features. It’s past time to make “opt in” the default setting for sensitive features.

Researchers identified traces of erythrulose, a monosaccharide, thousands of light-years away.

Roughly 100 fires are raging out of control in Ontario, sending smoke streaming 1,000 miles to the south and east.

While an impressive sale, the pen and switch did not break records.