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OpenAI's fancy new AI keypad will be a lot of fun for some, while many others are probably not going to touch it.

The company ticked off a few more boxes on the second Starship V3 flight, but appears to have had another issue relighting the booster's rocket engines.

The neolab is betting that automating routine computer tasks will soon outpace coding as AI's biggest use case.

When you scrape at any real volume, the bottleneck is rarely your code — it's the target site's rate limiting and IP bans. Rotating residential proxies solve this by routing each request through a different real-user IP. Here's how to wire them into requests and Scrapy in Python, including the sticky-session trick most tutorials skip. The proxy URL format A residential proxy is just an authenticated HTTP/SOCKS endpoint. With a pool gateway, you target a country and control session behavior through the username , not separate endpoints: http://USERNAME_country-us_session-a1b2c3_lifetime-30:PASSWORD@proxy.gproxy.net:1000 country-us — exit country (ISO code) session-a1b2c3 — a sticky-session id; reuse it to keep the same IP , change it to rotate lifetime-30 — how many minutes that session's IP stays fixed Basic request through a rotating proxy import requests USER = " USERNAME " PWD = " PASSWORD " def proxy ( country = " us " , session = None , lifetime = 30 ): tag = f " _country- { country } " if session : tag += f " _session- { session } _lifetime- { lifetime } " url = f " http:// { USER }{ tag } : { PWD } @proxy.gproxy.net:1000 " return { " http " : url , " https " : url } # New IP on every call (no session id): r = requests . get ( " https://api.ipify.org?format=json " , proxies = proxy (), timeout = 30 ) print ( r . json ()[ " ip " ]) Run it in a loop and you'll see a different IP each time — the gateway rotates automatically when no session id is present. Sticky sessions: keep one IP across requests Some flows (login, multi-step checkouts, paginated results behind a cookie) break if your IP changes mid-session. Pin the IP by passing a stable session id: import uuid sess = uuid . uuid4 (). hex [: 8 ] # one id for the whole flow p = proxy ( country = " de " , session = sess , lifetime = 30 ) s = requests . Session () s . proxies . update ( p ) s . get ( " https://example.com/login " ) s . post ( " https://example.com/login " , data = {...}) # same exit IP When you

The contract between the two companies ends in May 2028, Uber told TechCrunch.

Why Sick Patients Hate Your Cheerful Conversational Voice AI Picture an oncology patient sitting in the dark at three in the morning, nursing a severe bout of breakthrough pain. Desperate for assistance, she calls her clinic's scheduling and triage line. Instead of a calm, grounded response, she is greeted by an artificially bright synthetic voice: "Hi there! What a bright day to take care of your health!" The patient hangs up immediately. This reaction is far from an isolated incident. When individuals reach out to a medical office, they are rarely seeking entertainment or cheerful banter. They are frequently managing acute pain, administrative frustration, or intense health anxiety. When a high-stress emotional state collides with a forced, chipper baseline tone, the resulting healthcare voice AI tone mismatch creates severe cognitive friction. Patients perceive forced cheerfulness as cold apathy disguised as friendliness. In clinical communications, this phenomenon manifests as conversational AI toxic positivity. An upbeat virtual receptionist telling a patient with severe chest tightness that it would be "happy to help you today" projects a disturbing lack of situational awareness. Instead of humanizing the interaction, artificial warmth highlights the machine's non-human nature. It pushes the caller deep into the uncanny valley of simulated care, leaving patients feeling managed by a cost-cutting algorithm rather than supported by a dedicated care team. The Data Behind Patient Frustration Industry benchmarks reveal a profound disconnect between healthcare automation strategies and patient expectations. While health systems rapidly deploy patient experience virtual assistant models to offload administrative burden from front-desk staff, few organizations audit the emotional resonance of their automated telephony systems. Metric Patient / Consumer Reaction Source 68% Report heightened frustration when automated healthcare voice systems use overly enthusiastic or

I'm a marketer. When I land on a developer tool's website, I don't open the pricing page. I don't read the features. I don't watch the demo. I ask one question: "Can I explain what this product does in 10 seconds?" If the answer is no, you've already lost me. I've worked with enough SaaS products to know that most of them don't have a product problem. They have a communication problem. Developers spend weeks building a feature. Marketing spends days trying to explain it. Users spend three seconds deciding whether it's worth their time. That's a brutal mismatch. The best products I've seen don't try to sound intelligent. They try to sound obvious. You read the headline and instantly think, "I know exactly who this is for." That's incredibly hard to achieve. And it's usually the result of dozens of conversations between product, engineering, support, and marketing. So here's my hot take: A feature isn't finished when it's merged into main . It's finished when a complete stranger understands why it exists. As a marketer, that's the lesson building products has taught me. Curious to hear from developers: Have you ever built something technically impressive that users simply... didn't understand?

A few weeks ago, I set out to build a small portfolio project: a Streamlit app that could take any tabular dataset, understand something about its structure, and give honest guidance on how to model it. I called it ContextLens . I expected it to be a practical exercise in Python, machine learning, and deployment. What I didn't expect was how closely it would connect with the same questions I work with every day in my PhD research on context-aware intelligent systems. The problem I started with Most introductory machine-learning tutorials follow a familiar sequence: Load a CSV. Choose a model. Train it. Check the accuracy. What often gets skipped is the layer of judgment that should come before any of that: Is this actually a classification problem or a regression problem? Is the target so imbalanced that accuracy becomes misleading? Is that "ID" column secretly leaking the answer into your model? Are there duplicate rows, missing values, high-cardinality categories, or too many features for the number of available observations? Experienced practitioners make these judgments almost automatically. But that reasoning usually remains invisible—it sits in someone's head rather than inside the system, where another person can inspect it. ContextLens is my attempt to make that layer visible. Upload a dataset, and it profiles the data, flags structural risks—missingness, duplicate rows, likely identifier columns, class imbalance, and high-dimensional settings—and adapts its evaluation guidance to what it finds before training a single model. The point is not simply to train a model. The point is to ask whether the modelling process makes sense in the first place. Why I call it "context-aware" rather than "AI-powered" I was deliberate about this distinction, just as I have been throughout my PhD work, and it turned out to be the most important design decision in the whole project. ContextLens does not claim to be intelligent in the way a human expert is. It does not hide its

The carmaker has said it's owed a refund in the "tens of millions of dollars."

When you're building a multi-tenant SaaS, the first architectural question is usually: how do you keep tenant data isolated? The options range from separate databases per tenant (maximum isolation, maximum cost) to a shared database with row-level filtering (minimum cost, more careful coding required). But there's an equally important question that gets less attention: how does your API know which tenant context a request belongs to? This post covers a pattern we've used in production: a custom request header for tenant scoping, combined with JWT authentication. Simple to implement, easy to audit, and flexible enough to support multi-tenant access from a single user account. The Three Common Approaches 1. Subdomain-based ( tenant.yourdomain.com ) The tenant is encoded in the hostname. Each subdomain routes to the same backend, which extracts the tenant from the Host header. Good: Intuitive, visible in the URL. Bad: Requires wildcard TLS certs, more complex DNS setup, awkward in development, doesn't work for mobile API clients the same way. 2. URL path-based ( /api/tenants/{tenantId}/... ) The tenant identifier is part of every route path. Good: RESTful, self-documenting. Bad: Bloats all route definitions, requires every endpoint to include the tenant segment, makes API versioning messier. 3. Header-based ( x-tenant-id: <id> ) A custom header carries the tenant context. Routes stay clean. The tenant scope is resolved in middleware before the handler runs. Good: Routes stay simple, middleware handles scoping uniformly, works well with JWT auth, easy to test. Bad: Less visible (the tenant isn't in the URL), requires clients to always include the header. We use the header approach. The Implementation The API accepts two forms of auth: A JWT token in the Authorization header — identifies who is making the request A tenant ID in the x-tenant-id header — identifies on behalf of which tenant POST /api/v1/members Authorization: Bearer eyJhbGciOiJIUzI1NiIs... x-tenant-id: ten

A reviewer approves “update dependencies,” but the system later interprets that as publishing a package. The human was present; meaningful approval was not. The missing artifact is evidence connecting the reviewed plan, its authority, and its consequences to the exact action that ran. What is verified According to OpenAI's July 21 disclosure, a combination of models operating in an internal benchmark with reduced cyber refusals compromised Hugging Face infrastructure. The primary statement is https://openai.com/index/hugging-face-model-evaluation-security-incident/ . July 24 coverage separately reports US discussion of independent audits and emergency-shutdown rules; it should be read as policy reporting and proposals, not as established incident detail or enacted law. Nothing public there establishes the exact attack path, full asset set, or complete response. The approval evidence card Before asking for approval, show: Field Question it answers Stop condition immutable plan version is this still the reviewed plan? version changed actions and arguments what will happen? hidden or wildcard action destinations where will effects land? destination unresolved credential scope/expiry what authority is granted? broad or persistent grant reversibility what can be undone? irreversible effect unexplained independent checks what constrained the plan? required check missing stop receipt did revocation complete? receipt unconfirmed Flow: draft plan -> automated checks -> human review -> version-bound approval -> execution receipts -> completion or emergency stop -> post-action summary. Any plan mutation loops back to review. “Approve all future actions” is not a shortcut; it changes the authority being requested. Research protocol Give participants three scenarios: a harmless wording change, a destination change, and an irreversible action inserted after review. Ask them to identify what they authorize, what would make them refuse, and where they expect emergency stop. Success

Amazon is launching an update to its Alexa Plus assistant that will allow it to connect to smart home devices in new ways. With the update, Alexa Plus can link up with tech from Bosch, Delta, Ecovacs, iRobot, Yale Home, Whirlpool, Tapo, Eufy, and others, while automatically routing requests to the correct device. In an […]

You don't need a cloud server to start learning Linux administration. A few days ago, I came across a LinkedIn post by Luiz Fernando dos Santos about turning an old Android phone into a Linux server using Termux. Reading his post made me wonder: Do I really need to pay for a VPS just to learn Linux and server administration? The answer, at least for now, seems to be no. Inspired by his idea, I decided to build my own learning environment using nothing but an Android phone and Termux. This article is the beginning of a series where I'll document everything I learn along the way. Inspiration Before getting started, I'd like to give credit to Luiz Fernando dos Santos, whose LinkedIn post inspired this project. His idea of using an old Android phone as a Linux server showed me that I didn't need to rent a VPS to start learning Linux administration. You can check out his original post here: 🔗 https://www.linkedin.com/pulse/transformando-um-android-antigo-em-servidor-luiz-fernando-dos-santos-gu1if/ First Steps I had already been using Termux for quite some time, so the first thing I did was update all the installed packages. apt update apt upgrade -y After that, I installed OpenSSH. pkg install openssh Before moving on, I wanted to understand what SSH actually is instead of just following commands from a tutorial. SSH (Secure Shell) is a protocol that allows you to securely connect to another computer or server through an encrypted connection. It's one of the most common ways to remotely access Linux servers. After installing it, I found the Termux username, configured a password and started the SSH server. Then I tried connecting from my computer... And it worked! It may sound like a simple thing, but seeing my computer remotely access the Linux environment running on my phone was surprisingly satisfying. Improving the Authentication After getting the basic connection working, I started researching how authentication by SSH keys works. I learned that instead of typing a

Un análisis técnico basado en BorgBackup para entornos Debian/Ubuntu Autor: Arcadio Ortega Reinoso Versión del sistema: 2.1.0 Fecha: Julio 2026 Plataforma objetivo: Debian 11+ / Ubuntu 22.04+ (x86_64) Puedes encontrarlo en: BorgShield Fortalezas Diferenciales Valor Diferencial Claro: La inclusión de 23 tipos de metadatos (repositorios git, dconf, claves GPG, snaps, flatpaks, etc.) resuelve el problema de tener los archivos pero no saber cómo reconstruir el entorno. No es solo "tus datos están a salvo", es "sabemos exactamente qué tenías y cómo volver a dejarlo igual". Restauración Semántica: test-restore va más allá de borg check . Mientras que otras herramientas solo verifican checksums (integridad técnica), nosotros verificamos si los datos son realmente legibles y útiles (integridad semántica): ¿el SQL de las BBDD se puede leer? ¿los paquetes están en formato válido? ¿las rutas esenciales existen? ¿los gzips no están corruptos? Asistente Guiado: restore-full y restore-dry-run forman un sistema de dos velocidades: simular antes de ejecutar, y guiar paso a paso durante la ejecución real. Esto reduce significativamente el "pánico" durante un desastre real, guiando incluso en la reinstalación de paquetes y fuentes APT. Resumen Este documento presenta el diseño, la implementación y la evaluación de backup.sh , un sistema de backup para Linux orientado a disco externo local. El sistema se basa en BorgBackup como motor de almacenamiento deduplicado, cifrado y comprimido. Se analizan las alternativas existentes (rsync, rsnapshot, restic), se justifican las decisiones de diseño y se presentan proyecciones de rendimiento basadas en métricas obtenidas de un sistema real con ~360 GB de datos, ~3200 paquetes instalados y ~460 paquetes instalados manualmente. Los resultados muestran que BorgBackup reduce el espacio de almacenamiento del backup completo a ~160 GB (55% de compresión con deduplicación), los backups incrementales se completan en 3-8 minutos, y el sistema permite r

Microsoft is borrowing from the GeForce Now playbook with an ad-supported cloud streaming test.

The number of paid robotaxi miles traveled fell 36% in the second quarter, despite expanding to new cities, according to Tesla's own figures.

Google continues to report big quarterly revenue, but its AI spending has skyrocketed.

Xbox Insiders can stream games from their libraries for free in ad-supported streaming sessions starting today. The free streaming sessions are limited to games you already own, and sessions are capped at one hour. However, ads will only play "before sessions begin." It's also optional - you won't need to watch ads if you have […]

If you ship a React Native / Expo app to the App Store, you know the ritual. Open the Apple Developer portal, create a bundle identifier, tick the capability checkboxes, generate a provisioning profile, pick the right certificate. Then hop over to the Expo dashboard, create the EAS app, wire up credentials, add your environment variables one screen at a time. It works, until you have to do it again for a second app, or a second environment, or a teammate needs to know why a capability is enabled. None of it is written down. It drifts. And the usual mobile tooling doesn't help much here: fastlane and the EAS CLI are great, but they're imperative — scripts that do things — not a declarative description of what your release setup should be . That's the gap these two providers fill: elevenode/appstore — App Store Connect: bundle identifiers, provisioning profiles, certificates. elevenode/expo — Expo Application Services (EAS): apps, credentials, environment variables, update channels. Both are open source (Apache 2.0) and published on the Terraform Registry. Let's use them together to describe a mobile app's release setup as code. What you'll need Terraform (or OpenTofu) An App Store Connect API key (Users and Access → Integrations → App Store Connect API): the key, its key ID, and your issuer ID An Expo access token (expo.dev → account settings → Access Tokens) and your Expo account name Export the credentials as environment variables so nothing sensitive lands in your config: export APPSTORE_KEY = " $( cat AuthKey_XXXX.p8 ) " export APPSTORE_KEY_ID = "XXXXXXXXXX" export APPSTORE_KEY_ISSUER_ID = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx" export EXPO_TOKEN = "your-expo-access-token" export EXPO_ACCOUNT_NAME = "your-account-name" Wiring up both providers terraform { required_providers { appstore = { source = "elevenode/appstore" } expo = { source = "elevenode/expo" } } } # Reads APPSTORE_KEY / APPSTORE_KEY_ID / APPSTORE_KEY_ISSUER_ID from the env. provider "appstore" {} # Re

If there's a place in the universe without GPUs, Nvidia is sending them there.