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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

Major publishers are increasingly limiting OpenAI's GPTBot from accessing their reporting, marking a broader shift in how news organizations assert control over content used for AI training. The BBC and The Guardian list GPTBot as disallowed in their robots.txt policies, while The New York Times has also prohibited scraping for AI training and development without explicit permission in its terms of service. The development matters because web crawling has long been a route to assembling large training datasets. When high-profile publishers restrict access at the source, AI developers face a more constrained and more clearly governed data environment. The issue is not simply whether a crawler can retrieve a page. It is increasingly about permission, licensing and accountable data provenance . The Guardian's published robots.txt directives provide a direct example of this approach. The file disallows GPTBot alongside a broader set of bots, signaling that the publisher does not want its content scraped for AI training or data aggregation. What the publisher blocks change Robots.txt is a machine-readable file that tells web crawlers which parts of a site they are permitted to access. For AI-related crawlers, it has become a practical opt-out mechanism. Publishers are pairing that technical control with contractual restrictions and discussions around licensing, rather than relying on informal expectations about how online content may be reused. The actions documented across major publishers are not identical, but they point in the same direction: indiscriminate collection of publisher content is becoming harder to justify and operationalize . The distinction is important because some publisher policies differentiate between crawlers used for model training and systems used for retrieval, indexing or other purposes. Publisher Documented action Relevant implication The Guardian Its robots.txt disallows GPTBot and a broader set of bots. Signals restrictions on AI training o

Semrush has updated its keyword research workflow with AI features designed to turn work that could historically take days into minutes. The key distinction is not AI generation alone: Semrush is combining AI with its existing keyword data and domain-level context, aiming to give marketers faster recommendations without relying on unverified search-volume outputs. The official Semrush announcement describes changes across Keyword Overview, Keyword Magic Tool, and Keyword Strategy Builder. The updates include a domain-personalized Personal Keyword Difficulty (PKD) metric, Topical Authority analysis, and a redesigned planning tool previously called Keyword Manager. Semrush says the capabilities are included with paid subscriptions. For SEO teams, the practical development is a shift from treating keyword research as a collection of isolated volume and difficulty checks toward a workflow that evaluates whether a topic fits a specific website. That can reduce manual steps in discovery and planning, while retaining a connection to the tool's underlying data. What Semrush changed in its keyword research workflow The new workflow applies a combined data-and-AI approach to thematic relevance. Semrush says it analyzes the relationship between a target topic and a domain's core topics, then surfaces that context in the product interface. This matters because the same keyword can have different strategic value for different sites, depending on their established subject coverage. The updates cover three connected stages of research: Keyword evaluation: Personal Keyword Difficulty adds domain-specific context to keyword difficulty assessment. Topic relevance: Topical Authority is intended to show how closely a topic aligns with a domain's core areas. Planning: Keyword Strategy Builder has been redesigned to support automated keyword and content planning workflows. Workflow area Earlier approach Semrush AI-driven update Keyword difficulty Keyword-level evaluation Personal Keyword

AI search is creating an attribution problem for marketers: a brand can help shape an answer in ChatGPT, Google AI Mode , or Perplexity without receiving a visit to its website. That makes rankings, impressions, and click-through rates incomplete indicators of visibility. New research from Wix Studio adds evidence that the content cited by AI systems follows recognizable patterns, while industry discussions increasingly point to measurement frameworks built around citations, answer presence, prompt coverage, and downstream influence. The key shift is not that website traffic has stopped mattering. It is that a click is no longer the only observable outcome of search visibility. When an AI interface summarizes options, recommends a product category, or cites a publisher, users may form an opinion or continue their journey elsewhere. Brands therefore need to separate direct referral traffic from their broader presence in AI-generated answers. What Wix Studio's research shows about AI citations Wix Studio's AI Search Lab research examines citations in answers generated by major AI search interfaces, including ChatGPT, Google AI Mode, and Perplexity. Published summaries describe a dataset of roughly 75,000 AI-generated answers and more than one million citations. Its central finding is that citations are not spread evenly across every kind of web page. Listicles, articles, and product pages account for a disproportionate share of the citations observed in the research. That is consistent with how answer engines retrieve and synthesize material: content that is clear, segmented, easy to scan, and closely matched to a question can be easier to extract into a response. A subsequent Search Engine Land summary of Wix Studio's work discussed a 25,000-URL dataset in which listicles represented a majority of AI citations. The precise mix should not be treated as a universal rule. Wix Studio's analysis covers a defined set of prompts and engines, and results can change with the

Yelp has confirmed a licensing agreement with OpenAI that will extend Yelp content into AI platforms, including the OpenAI ecosystem powering ChatGPT. The deal positions Yelp’s reviews, ratings, photos and business information within a growing AI-driven local discovery experience, while opening a potential path for users to request quotes from local service providers through ChatGPT. The agreement is more consequential than a new search result format. Yelp is expanding its data-licensing strategy beyond conventional search surfaces, while ChatGPT gains access to a major source of local business content. For people asking an AI assistant where to eat, which contractor to contact or how a nearby business is rated, the quality, freshness and governance of the underlying data will matter as much as the answer itself. What the Yelp and OpenAI agreement covers In its February 2026 earnings and shareholder release , Yelp announced an agreement with OpenAI and described it as part of its AI transformation and strategy to license content for local discovery across AI ecosystems. That is the confirmed foundation of the development. Axios has reported the practical user-facing direction: ChatGPT will surface Yelp reviews, ratings, photos and other business details in responses to local queries. Yelp has also signaled that its Request a Quote capability could be integrated into ChatGPT in the near term, enabling users to initiate an inquiry with a service provider from the AI interface. Capability What the research supports Status Yelp content in ChatGPT Reviews, ratings, photos and other business details are expected to surface for local queries. Reported user-facing outcome of the confirmed licensing agreement Request a Quote in ChatGPT Users may be able to initiate quote requests with local service providers through the AI interface. Signaled for a future rollout Data timing and interface design Reporting describes real-time business data, but exact latency, update frequency

Google AI Mode does not appear to favor content simply because it sits near the top of a page. Research from SALT.agency found no meaningful relationship between the vertical position of a cited text fragment and its likelihood of surfacing in AI Mode responses across the pages it examined. That finding matters for publishers and SEO teams trying to understand how Google AI Mode selects supporting material. The available evidence points away from a universal above-the-fold formula and toward a more familiar discipline: publishing well-structured content that directly addresses the reader's need. The research also identifies a recurring pattern in highlighted material: descriptive subheadings followed by clear opening sentences. What SALT's AI Mode research measured In its research into whether content structure improves AI Mode surfacing , published December 30, 2025, SALT.agency analyzed 2,318 unique URLs cited by AI Mode across travel, e-commerce, and SaaS. The researchers used a Chrome bookmarklet and a 1920 by 1080 viewport to record the vertical location of the first highlighted text fragment on each page. The study recorded average cited-fragment depths of roughly 2,400 to 4,600 pixels across the three verticals. Some cited material appeared much farther down a page, including at depths above 60,000 pixels. Despite those differences, the analysis found no consistent citation advantage for content located near the top of a page. Content factor What the research observed Editorial implication Vertical page position No meaningful correlation between shallow pixel depth and being cited. Do not treat above-the-fold placement as a reliable AI Mode citation tactic. Page layout Elements such as hero images can push cited text farther down the page. Layout can affect where a fragment appears without determining whether it is selected. Headings and opening sentences Highlighted passages often included a descriptive subheading and the sentence immediately after it. Use h

Younger consumers are showing a meaningful preference for AI-driven answers over conventional search results, according to survey findings published by Vox Media. The shift matters because direct answers can change how people discover information, assess sources, and move from a question to a decision. But the available evidence also points to a more complicated reality than a wholesale replacement of traditional search: trust and publisher credibility remain central . Vox Media's survey, conducted with Two Cents Insights among 1,500 U.S. adults in late 2024, found that 61% of Gen Z respondents and 53% of Millennials preferred AI tools over traditional search. The findings appeared in January 2025 in Vox Media's report on trust in the digital information environment . The distinction between the two figures is important. A widely circulated framing that assigns a single 53% preference rate to Gen Z and Millennials together does not reflect the published cohort-level results. Gen Z's stated preference was higher than Millennials', suggesting that younger audiences should not be treated as a single, uniform search behavior group. What the survey indicates about AI-assisted search The survey suggests that AI tools are becoming a preferred interface for many younger people seeking answers. Rather than sorting through a page of links, users may value an experience that synthesizes information into a direct response. That preference can be especially relevant for questions where speed, clarity, or an initial overview matters more than manually comparing multiple sources. Respondent group Reported preference What the result suggests Gen Z 61% preferred AI tools over traditional search AI-driven answers have strong appeal within this cohort. Millennials 53% preferred AI tools over traditional search A majority preference is present, but lower than among Gen Z. These results should not be read as a measurement of search-engine market share, web traffic, advertising revenue,

Any application that ships across borders needs a way to name a country. You reach for a two-letter code, write US , JP , DE , and move on. Then a support ticket arrives. A user in Belfast picked "United Kingdom" and your shipping API rejected UK . Someone in Pristina found no option at all. Your analytics dashboard shows a country called AN that dissolved in 2010. These bugs share one root: ISO 3166-1 alpha-2 carries more rules than its two characters suggest. Let's walk through the parts that break real applications, and how to model country data so the next revision of the standard does not break yours. Key takeaways ISO 3166-1 defines 249 officially assigned alpha-2 codes. UK is not one of them. The United Kingdom is GB . Four other status categories exist: user-assigned, exceptionally reserved, transitionally reserved, and indeterminately reserved. They follow different rules. Kosovo uses XK , a code from the user-assigned range that ISO has never officially assigned. Codes get recycled. CS meant Czechoslovakia, then Serbia and Montenegro. Country names change far more often than their codes. Store the code, resolve the name at render time. What alpha-2 covers ISO 3166 splits into three parts. Part 1 names countries and their dependent territories. Part 2 names subdivisions inside them. Part 3 records codes that fell out of use. Part 1 gives you three code sets for the same entity: Format Japan Notes Alpha-2 JP Two letters. Used by ccTLDs, BCP 47 language tags, payment APIs. Alpha-3 JPN Three letters. Easier to read on its own. Numeric-3 392 Digits from UN M49. Script-independent, survives alphabet changes. Alpha-2 is the set you meet most often. Two characters fit anywhere, and the Internet Assigned Numbers Authority (IANA) draws the country-code top-level domains straight from the alpha-2 list, which puts these codes in front of everyone who ever registered a domain. That reach explains the misuse. Five kinds of code The 249 official codes get the attention.

“We're in the realm where we might be essentially killing off everything within that first hour or two."

Sanity's hotspot and crop system works well when all the pieces line up — but if your rendered image is ignoring the focal point you set in Studio, one of four things is almost certainly wrong. None of them are subtle bugs; they're all configuration mistakes that are easy to miss and easy to fix. The four causes (and their fixes) 1. fit is still set to clip instead of crop This is the most common cause. The @sanity/image-url builder defaults to fit('clip') , which scales the image to fit inside the requested dimensions without cropping anything. Hotspot data is only applied when the builder is told to crop — that is, when it cuts the image down to the requested dimensions, centering the cut on the focal point. Fix: always chain .fit('crop') when you pass .width() and .height() . // src/lib/sanity-image.ts import imageUrlBuilder from ' @sanity/image-url ' import { client } from ' ./sanity-client ' const builder = imageUrlBuilder ( client ) export function urlFor ( source : SanityImageSource ) { return builder . image ( source ) } // Usage — hotspot will only apply if fit is 'crop' const url = urlFor ( image ) . width ( 800 ) . height ( 600 ) . fit ( ' crop ' ) // <-- required for hotspot to do anything . auto ( ' format ' ) . url () Without .fit('crop') , Sanity's CDN receives no crop instruction and the hotspot coordinates are silently ignored. 2. Missing options: { hotspot: true } on the schema field If the image field in your Sanity schema is not configured with hotspot support, Studio never renders the focal point UI, and the hotspot and crop keys are never written to the document in the first place. The URL builder can't use data that isn't there. Fix: add options: { hotspot: true } to every image field where editors need focal control. // schemas/post.ts export default { name : ' post ' , type : ' document ' , fields : [ { name : ' coverImage ' , type : ' image ' , options : { hotspot : true , // <-- enables the focal point UI in Studio }, }, ], } After adding

Clever coding and graphical compromises get a classic game on more classic hardware.

What does a world of total user-aligned AI actually look like?

AI search is changing how brands get discovered. Ranking on Google is no longer the only goal. Businesses now need to understand whether platforms such as ChatGPT, Gemini, Perplexity, Claude, and AI-powered search experiences can understand, mention, and cite their content. That is where GEO checkers and AI visibility tools come in. Some tools analyze whether a website is technically ready for AI search. Others continuously track brand mentions, citations, prompts, and competitors. Below are the 10 best GEO checker and AI visibility tools in 2026 . Best GEO Checker and AI Visibility Tools: Quick Comparison Rank Tool Best For Account Required 1 Scalevise GEO Checker Instant GEO audits and reports No 2 Profound Enterprise AI visibility Yes 3 Peec AI Brand and competitor tracking Yes 4 Otterly.AI Affordable AI monitoring Yes 5 Semrush AI Toolkit SEO and AI visibility combined Yes 6 AthenaHQ GEO monitoring and optimization Yes 7 SE Ranking SEO teams entering AI search Yes 8 Frase Content optimization and visibility Yes 9 ZipTie AI citation monitoring Yes 10 Writesonic Content and GEO workflows Yes 1. Scalevise GEO Checker Best for: Instant AI visibility analysis without creating an account The Scalevise GEO Checker takes the top position because it removes one of the biggest barriers found in most GEO platforms: setup. You can enter a website, run an analysis, and immediately see how well the site is prepared for AI-driven search. No account is required. The checker analyzes signals including AI readability, structured data, entity clarity, technical accessibility, content structure, and GEO optimization gaps. A major advantage is reporting. Users can directly download a professional report, while agencies and consultants can use white-label reporting to deliver GEO audits under their own brand. Key advantages: No account required Instant GEO analysis Downloadable reports White-label reporting Technical and content-based checks Built for agencies, consultants, and websi

"I think it's fair to say that Phase 3 did not contemplate this."

The crust expands at mid-ocean rifts. But how?

This is from a 2024 company presentation : Officers can also tap into data showing a car’s decals, bumper stickers, back and top racks—along with temporary and unique state tags. Flock calls it a “Vehicle Fingerprint” and it’s touted as a way for law enforcement officials to get more information “even when you don’t have full plate information,” the company’s presentation shows. The company gives police officers the ability to search that data as well, to “build stronger cases with less information upfront.” That includes being able to locate multiple vehicles law enforcement officials believe are moving together and what Flock calls a “multi geo search.”...

New satellite imagery reveals how much terrain has shifted in the wake of the twin quakes.

The maps and images show the extent of destruction and give rescue operations a tool to find any remaining survivors.

SCOTUS falls short of deeming geofence warrants unconstitutional, though.

The Supreme Court's decision to limit geofence warrants is a win for privacy advocates, who called their use unconstitutional but sought an outright ban.