AI 资讯

AI bias mirrors human bias; both stem from our language and lived experiences. Ethics and AI are inseparable, but AI changes affordances, making harmful actions easier to carry out. The EU regulations apply to AI, since digital products are products. The ultimate goal is accountability: companies must ensure transparency, and laws should favor using the simplest AI that gets the job done. By Ben Linders

Google Home is rolling out a new Gemini-powered automation feature that can trigger smart home routines based on what your security cameras can see. This is one of several updates announced yesterday for Gemini for Home, including enhanced voice command support and general stability improvements, following its early access launch in October. "We are introducing […]

Created from fabric scraps that would otherwise end up in a landfill, this thoughtfully constructed bag looks great and can carry everything you need for a weeklong trip.

New mothers working in software development are staring down an AI-pilled workplace they barely recognize.

After releasing a feature to detect and label AI-generated videos, YouTube has released a new feature that will let you curate videos using... AI.

Forecasters say expected El Niño should temper hurricanes in Atlantic, urge preparedness.

Apache Fory is a blazingly fast multi-language serialization framework for idiomatic domain objects, schema IDL, and cross-language data exchange. Key Features For 1.0 Release: Unified xlang type system and xlang is default serialization mode now across java/python/c++/rust/go/c#/swift/javascript/dart/kotlin/scala. Decimal, bfloat16, dense array support for xlang serialization. Android serialization and Java annotation processor support Kotlin xlang, KSP, and schema IDL support Scala schema IDL support and scala3 macro derived serializer Serialization performance improvements submitted by /u/Shawn-Yang25 [link] [留言]

It is one thing to say AI will change the world. It is another to expect the class of 2026 to applaud it. In fact, when former Google CEO Eric Schmidt told University of Arizona graduates that their task is to help shape AI, he was met with a resounding chorus of boos. “I can…

Demystifying WebP to PNG: How to Secure Serverless Edge Routing Configurations Safely We have all been there. You are building a high-performance, modern web application and you decide to store all user-generated assets in modern, ultra-compressed WebP formats. It is a smart move for your Google Lighthouse scores. Then, the legacy enterprise integration request hits your inbox. A major client needs to pull these same assets dynamically, but their internal 15-year-old reporting engine only supports PNG. Suddenly, you need to configure a runtime conversion pipeline that handles complex input schemas, transforms formats on the fly, and manages edge routes without exposing your internal database claims or API secrets. Setting up secure serverless edge routing configurations to convert images on-demand can quickly turn into a security nightmare. If you do not handle incoming credential tokens correctly, you risk forwarding sensitive OAuth scopes or database keys directly to downstream image-processing worker nodes. In this guide, we will break down exactly how to architect a lightweight, secure, and fast edge routing pipeline that validates incoming image request schemas and converts WebP to PNG without leaking sensitive backend credentials. The Problem Modern edge runtimes like Cloudflare Workers, Vercel Edge Functions, or AWS CloudFront Functions are incredibly fast, but they have strict execution limits. They run on V8 isolates, meaning you do not have a full Node.js environment with unlimited memory and access to heavy C++ binaries like sharp or canvas without paying a massive cold-start penalty. If you want to support legacy clients by converting WebP to PNG on the fly, you are faced with three major challenges: Bundle Size Restrictions : Edge functions typically restrict your code size to 1MB or 2MB. Bundling heavy native libraries to parse image bytes is a recipe for deployment failures. Credential Leakage : Edge routers often intercept incoming JWT authorization

Why your age-gating algorithm is probably doomed to fail in the wild For developers building in the computer vision and biometrics space, there is a massive gap between a model that passes a NIST benchmark and a system that survives the "child-with-a-VPN" test. Recent data indicates that roughly 32% of children are successfully bypassing age-gating tech. As engineers, our first instinct is often to blame the model—to tweak the weights, gather more training data, or tighten the threshold. But the technical reality is more sobering: the failure isn't in the algorithm; it's in the deployment architecture. The Problem with Probabilistic Logic in Binary Workflows Most age estimation models rely on analyzing biometric markers—skin texture, bone structure ratios, and periocular geometry. They produce a probabilistic age range. However, according to NIST's evaluation of age estimation software, to maintain a low false-positive rate, systems often need to set a "challenge age" between 29 and 33 years. If you are a dev tasked with keeping 17-year-olds off a platform, you are essentially forced to build a "buffer zone" of over a decade. If the system flags anyone who might be under 30, the UX becomes a nightmare. If you lower the threshold to 18, the false-negative rate skyrockets. This is the fundamental trade-off of probabilistic facial analysis: precision and recall are at constant war, and in a high-traffic production environment, the "noise" of real-world variables (poor lighting, low-res sensors, off-axis angles) makes consistency nearly impossible. The Breakdown of the Identity Handoff Beyond the model, there are three technical failure points that no amount of Euclidean distance analysis can fix if the pipeline is broken: The Signal-to-Noise Ratio at Source: Evaluation datasets are clean. Production images are taken on scratched lenses in low-light bedrooms. The delta between training distribution and inference-time reality is where the first 10% of accuracy vanishes.

Microsoft announced Azure Linux 4.0 and Azure Container Linux at Open Source Summit. Azure Linux 4.0 is a Fedora-based general-purpose server distribution for Azure VMs, the first time Microsoft has offered a supported Linux beyond container hosting. Azure Container Linux is an immutable container-optimized host built on Flatcar. By Steef-Jan Wiggers

Tabla de Contenidos Objetivo Restricciones de los Errores Basados en Texto Errores Personalizados en Go Conceptos Clave Implementar e Inspeccionar Errores Casos de Uso Comunes Para no morir en el intento y consejos que no pediste Conclusiones del Tema Objetivo Aprender a diseñar tipos de errores personalizados con metadatos de negocio en Go y a propagarlos correctamente a través del flujo de la aplicación sin perder su tipo ni su información de origen. Restricciones de los Errores Basados en Texto En programas sencillos, el uso de errors.New es perfecto. Sin embargo, en aplicaciones empresariales los errores necesitan transportar datos estructurados. Por ejemplo, si una validación de entrada falla, el frontend no solo quiere saber que "hubo un error", necesita saber qué campo exacto falló (ej. email ) y qué regla se violó (ej. formato inválido ). Si solo devolvemos una cadena de texto, la capa superior de nuestra aplicación tendría que parsear el texto del error con expresiones regulares para extraer los metadatos. Esto es extremadamente ineficiente, propenso a errores de formato y acopla la lógica interna a los mensajes de texto visibles al usuario. Tipos de Errores Comunes en Go Antes de analizar cómo propagar y envolver errores, es fundamental comprender los dos patrones principales que se utilizan en Go: Sentinel Errors : Son variables globales predefinidas que representan un estado de error estático y específico. Se definen a nivel de paquete y se comparan directamente por valor. Ejemplos estándar: io.EOF , sql.ErrNoRows . Creación: Generalmente declarados con errors.New (como var ErrNotFound = errors.New("not found") ). Custom Structs (Errores estructurados personalizados): Son estructuras que implementan la interfaz error y contienen campos adicionales para transportar metadatos dinámicos del fallo en tiempo de ejecución (como códigos de estado o parámetros de entrada). Creación: Un struct personalizado que implementa el método Error() (ej. type ValidationErr

Over the past decade, the core evolution of data engineering has been the deconstruction and reconstruction of traditional data warehouse architectures through the Modern Data Stack. We separated data ingestion from databases, forming the Data Ingestion layer, using tools like FiveTran, Airbyte, and Apache SeaTunnel to solve ELT / CDC / Reverse ETL problems; We separated compute from storage, forming cloud data warehouse and lakehouse systems such as Snowflake, Databricks, Iceberg, and Hive; We separated orchestration from scripts, leading to orchestration systems like Apache Airflow and Apache DolphinScheduler; SQL development, data modeling, lineage, data quality, BI, and AI analytics were further split into independent tools. This architecture was undoubtedly progress. It moved data engineering away from the primitive era of “a bunch of scripts + Crontab” toward cloud-native infrastructure, elastic computing, engineering governance, and open ecosystems. The greatest contribution of the Modern Data Stack was “decoupling,” and its biggest side effect was also “decoupling.” Tools became more powerful, but data engineers were forced to switch between more systems than ever before: datasources in one place, synchronization configs in another, DAGs somewhere else, logs elsewhere, SQL stored in Git, and Snowflake / Iceberg / cloud warehouse execution results living in yet another environment. As a result, many data engineers spend less time on data modeling, business understanding, metric definitions, architecture design, and cost optimization — and far more time configuring datasources, setting field mappings, dragging DAG nodes, modifying SQL, checking logs, and rerunning tasks. This is the hidden pain created by the Modern Data Stack: data engineers became trapped inside tools. The emergence of engineering-focused AI systems like Codex and Claude Code is now changing the entire software engineering workflow. But how can data engineers truly achieve Vibe Coding? That

Amazon is moving packages in Japan on the Shinkansen as part of its efforts to have neutral carbon emissions.

Modern QA automation is extremely effective when testing traditional web applications. APIs can be validated reliably, user flows can be scripted with precision, and infrastructure monitoring has become mature enough to detect most operational problems before users even notice them. Browser-based casino games are different. Over the last few years, many iGaming teams have invested heavily in automation frameworks, synthetic monitoring and infrastructure observability. On paper, this should make production environments safer and more stable than ever before. Yet operators still encounter situations where everything appears healthy internally while players experience visibly broken gameplay sessions in production. This creates one of the most frustrating categories of bugs in modern iGaming. The backend responds correctly. Monitoring dashboards remain green. APIs continue returning valid responses. Error logs show nothing alarming. Meanwhile, players encounter frozen reels, broken bonus transitions, stuck autoplay sessions or visual states that no longer match the actual game logic running underneath. From the platform perspective, the game is technically alive. From the player perspective, the game is unusable. This gap exists because most traditional QA systems were designed around structured applications rather than visually driven environments. Frameworks such as Selenium, Playwright or Appium work extremely well when the application exposes predictable UI structures and accessible states. In browser games, however, much of the real application state exists visually rather than structurally. Modern slot games often rely on canvas rendering, WebGL pipelines, animation-heavy transitions and custom rendering engines. A scripted automation flow may successfully click buttons and validate network responses while the actual gameplay session is visually frozen for the player. In many cases, the DOM itself provides little meaningful information about what is truly happeni

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built I built So Lai , a local-first profit tracker for small online shops in Vietnam. The goal is simple: help a seller answer the question they often cannot answer from platform revenue alone: Is my shop actually profitable after product cost, marketplace fees, shipping subsidies, discounts, ad spend, returns, and pending COD? So Lai is not trying to be a full POS, CRM, or inventory suite. It focuses on the painful middle layer that many small sellers still manage through scattered spreadsheets: Product cost by SKU Orders from Shopee, TikTok Shop, Facebook, Zalo, or livestream sales Platform fees, shipping cost, vouchers, and discounts Ad spend by channel and SKU Return/cancellation status COD received vs. pending Net profit by order, product, and sales channel The app runs locally with Node.js and JSON storage, so it does not require paid APIs or cloud setup. GitHub repo: https://github.com/klauski24/so-lai Demo Run locally: git clone https://github.com/klauski24/so-lai.git cd so-lai npm start Open: http://127.0.0.1:4182 What the demo shows: A Vietnamese-language dashboard called Sổ Lãi Shop profile setup Clear explanation of where the numbers come from CSV import for products, orders, and ad costs Manual order entry Profit analysis by channel and SKU Alerts for loss-making products, high COD pending, and high return rate CSV and Markdown report export Screenshots are included in the repository: so-lai-desktop.png so-lai-mobile.png The Comeback Story The first version was too vague. It started as an English-named dashboard called ProfitLens . It had some useful calculations, but it did not feel practical yet. The biggest problems were: The app used Vietnamese currency but had an English product name. There was no place to define the shop. It was not clear where the data should come from. The dashboard looked like a demo, not something a seller could actually use. I reworked the project into So