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Google AI Mode appears to be treating the passage, not the full web page , as a central unit of citation. A year-long Pillarbase analysis of 15.7 million AI Mode citations across 148 industries found that nearly half were scroll-to-text highlights. The study identified about 4.6 million unique highlighted passages from 2.7 million pages, with heavily reused passages appearing across hundreds of queries. That pattern matters because a citation can do more than point readers toward a domain. It can elevate a specific sentence or short section as the textual evidence behind an AI-generated answer. For publishers, the practical implication is that a strong page may not be enough on its own. Its individual passages need to be clear, self-contained, and useful in the context of a query. The evidence does not mean Google has publicly disclosed a new formal citation policy or changed the underlying mechanism in a documented way. Google’s May 2026 discussion of AI Mode focused on expanding usage and changing user behavior. But the available third-party research consistently indicates that AI Mode visibility is substantially shaped by extractable sections of content. What the research says about AI Mode citations Pillarbase’s findings provide the broadest view of the pattern. Scroll-to-text highlighting is designed to take a reader to a particular part of a page, rather than merely opening the page at its top. In the study dataset, the prevalence of these highlights suggests that AI Mode is frequently associating an answer with a discrete source fragment. A separate December 2025 analysis from SALT.agency reached a compatible conclusion from a content-structure perspective . The agency found that AI Mode citations often depend on descriptive subheadings and opening sentences , while finding no simple advantage for content positioned above the fold. Its AI Mode content-structure study is particularly relevant for teams trying to understand how a page’s organization can affect

ElevenLabs now offers a Dubbing Translation API designed to turn multilingual video and audio localization into a single automated workflow. The service accepts a video, audio file, or source URL, lets developers choose one or more target languages, and coordinates transcription, translation, voice generation, speaker identity preservation, and timing alignment before returning dubbed assets. The central change is not simply another voice feature. ElevenLabs is presenting the pipeline as an integrated API operation rather than a set of services developers must orchestrate independently. Its Dubbing Translation API product page says users can dub and translate video in 90+ languages in one call , with translation, voice cloning, and timing synchronization handled server-side. For localization teams, that approach could reduce the application logic required to move from an original asset to versions for multiple language audiences. It does not remove the need for teams to assess translation quality, brand requirements, and appropriate use of cloned voices, but it consolidates the underlying production stages into one API surface. What the unified dubbing workflow does ElevenLabs' dubbing documentation describes an end-to-end sequence comprising transcription, translation, voice generation, and video synchronization. The Dubbing Translation API places those stages behind a single request flow, with the aim of retaining the original speaker's identity and the timing of the source material in the resulting dubbed output. That matters because dubbing is more than text translation. A usable localized video or audio asset needs speech that fits the surrounding media, while the voice and delivery should remain coherent for the intended audience. ElevenLabs describes its synchronization capability in terms of preserving timing and tone, positioning the API for workflows where the final media asset, rather than a translated script alone, is the required output. The documented

ElevenLabs has introduced Dubbing v2 , a rearchitected AI dubbing model built to retain a speaker's emotion, delivery, and timing while translating content across more than 90 languages. The update expands the company’s localization proposition beyond a basic language replacement: its documented controls cover dialect-specific accents, multi-speaker material, and background audio management for more complex video and audio scenes. According to ElevenLabs’ Dubbing v2 announcement , the model is designed for creators, marketers, studios, and broadcasters that need to localize video at production scale. It is integrated with ElevenCreative for one-click video localization and ElevenProductions, the company’s professional localization service. The central goal is preserving the original performance rather than simply generating translated speech. That matters for material in which pacing, vocal emphasis, and emotional delivery are part of the message, including marketing campaigns, creator videos, and professionally produced programming. Dubbing v2 is intended to synchronize translated dialogue with the source speaker’s timing and delivery across its supported languages. What Dubbing v2 changes for localization workflows The most practical additions are the controls documented for ElevenLabs' dubbing workflow. They give teams more ways to shape a dub around the source material and the intended audience, particularly when a project includes regional language variation or a mix of dialogue and sound. Localization need Dubbing v2 capability Documented control or workflow Regional language variation Dialect-specific accent selection target_accent , marked experimental Scenes with several voices Multi-speaker dubbing num_speakers Music, effects, or ambient sound Background audio management foreground_audio_file , background_audio_file , and drop_background_audio End-to-end video localization Integrated production workflows ElevenCreative and ElevenProductions For Spanish-lan

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The Model Context Protocol (MCP) is becoming a more consequential piece of enterprise AI infrastructure because useful AI assistants need more than reasoning ability. They need controlled access to customer records, marketing context, decisioning systems, and the tools that turn an answer into an action. MCP's 2026-07-28 release candidate advances that goal with a stateless core for standard HTTP infrastructure, formal extensions for interfaces and long-running work, and a stronger framework for authorization and conformance . For SEO and marketing teams, the change is not that an AI agent suddenly replaces strategy or governance. It is that the data-access layer connecting an agent to CRM-like and marketing systems is becoming more standardized. That can make AI-enabled workflows more practical to design, review, and operate, provided organizations define what data an agent may access and what it may do with it. The official MCP 2026-07-28 release candidate announcement describes a stateless core intended to scale over conventional HTTP infrastructure. It also introduces formal extensions: MCP Apps for server-rendered user interfaces, and Tasks for work that takes longer than a single request. Alongside those technical changes, the release candidate strengthens authorization alignment with OAuth and OpenID Connect practices, while establishing a formal deprecation policy and conformance framework. What the 2026-07-28 release candidate changes MCP is an open standard for connecting AI agents to external data sources and tools. In an enterprise setting, those connections can include CRM-like systems and marketing data, subject to the systems and permissions an organization exposes. Rather than building every connection as a bespoke integration, teams can use a common protocol layer for supplying an AI system with grounded organizational context. The 2026-07-28 release candidate matters because it addresses several requirements that become more important as AI workflo

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Hey everyone! 👋 I'm a developer who got tired of visiting 10 different websites to do simple tasks like formatting JSON, compressing images, or generating QR codes. So I built DevToolBox — a single place with 15+ free online tools, all running in your browser with no sign-up required. 👉 https://toolbox-site.asia Why I Built This Every time I needed a quick tool, I'd end up on a site full of ads, popups, or "create an account to continue" walls. I wanted something clean, fast, and respectful of users' time and privacy. The idea was simple: one website, all the tools you need, zero friction. What's Inside Here are some of the tools available: Developer Tools: JSON Formatter & Validator — Format, validate, minify JSON with syntax highlighting Base64 Encoder/Decoder — Encode and decode Base64 strings instantly UUID Generator — Generate v4 UUIDs in bulk 🔧 Unix Timestamp Converter — Convert between timestamps and human-readable dates 🔧 Regex Tester — Test regular expressions with real-time matching 🔧 Markdown Preview — Write Markdown and see the output live Hash Generator — MD5, SHA-1, SHA-256, SHA-512 🔧 Diff Checker — Compare two texts side by side Daily Tools: 🖼️ Image Compressor — Compress images right in your browser Image Format Converter — Convert between PNG, JPG, WebP Password Generator — Create strong, customizable passwords 📱 QR Code Generator — Generate QR codes with custom colors BMI Calculator — Calculate Body Mass Index 🎂 Age Calculator — Calculate exact age from birth date 📝 Word Counter — Count words, characters, sentences 📏 Unit Converter — Length, weight, temperature, and more How It's Built The whole site is a Vue 3 + TypeScript + Vite project with Tailwind CSS for styling. Everything runs client-side — no data is ever sent to a server, which means your data stays on your device. Key tech: Vue 3 with Composition API TypeScript for type safety Vite for blazing fast dev experience Tailwind CSS for styling Vue Router with history mode for clean URLs vue-i1

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title: How to Convert Images to Buildable Minecraft Pixel Art with Exact Materials published: true tags: minecraft, tutorial, gaming, opensource Originally published at blockartlab.com Disclosure: I built BlockArtLab, the free browser tool used in this guide. Most image-to-pixel-art tools stop at a preview. That is useful for seeing the idea, but it leaves the difficult questions unanswered: How large should the build be? Which real blocks should I collect? How many stacks of each color do I need? This tutorial covers the complete workflow from source image to a blueprint you can construct. 1. Pick an image that survives low resolution Minecraft pixel art works best when the source has one recognizable subject, a clear silhouette, and strong contrast. Logos, flags, game characters, and illustrated portraits usually survive conversion better than photographs with a busy background. Before uploading the image: Crop unused space around the subject Remove distracting background objects Make important features such as eyes or lettering larger Increase contrast if the subject blends into the background ## 2. Choose dimensions by material cost One converted pixel equals one placed block. The total block count is: width × height = total blocks | Size | Total blocks | Best use | |------|-------------|----------| | 16 × 16 | 256 | simple symbols and prototypes | | 32 × 32 | 1,024 | small survival logos and characters | | 64 × 64 | 4,096 | portraits, shading, and medium text | | 128 × 128 | 16,384 | a classic single-map-sized canvas | Doubling both sides multiplies the material count by four. For a first wall build, start between 32 and 64 blocks wide. ## 3. Choose a practical block palette I use three simple palette strategies: Concrete only for logos, flags, cartoons, and saturated colors Survival friendly for accessible concrete, wood, stone, sandstone, moss, and similar materials Full palette when a closer color match matters more than collection cost ## 4. Decide between

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