CallAgent provides AI phone agents that make and answer calls for your business. They follow up on new ad leads in seconds, qualify prospects, book appointments, confirm schedules, and run outbound campaigns at scale. You can connect a dedicated number, integrate calendars and CRMs, and trigger calls via API or webhooks. Monitor recordings, transcripts, and analytics in real time. CallAgent supports multilingual, natural conversations, 24/7 availability, and GDPR-grade security with pay-as-you-go and tiered plans.
Omnia shows how your brand appears across AI engines and tells you exactly what to do to improve it. We track share of voice, citations, competitor benchmarks, and visibility across AI search. Insights turns that data into prioritized, prompt-level tasks, indicating what content to create, what to improve, and where to get featured based on real citation data, brand authority, and category. Monitoring is the diagnosis, and Insights is the prescription. There are no dashboards collecting dust, just a clear plan to win AI visibility.
Looking to take the next step in your search marketing career?
Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.
(Provided to Search Engine Land by SEOjobs.com)
(Provided to Search Engine Land by PPCjobs.com)
Digital Marketing Manager 10x Health System (Scottsdale, AZ)
Paid Ads/Growth Manager, Robert Half (Hybrid, Atlanta Metropolitan Area)
SEO Manager, Clutch (Remote)
Marketing Manager β SEO & GEO, Care.com (Hybrid, Austin Texas)
Digital Marketplace Manager, Venchi (Hybrid, New York, NY)
Advertising Media Manager, Vetoquinol USA (Remote)
Programmatic Advertising Manager, We Are Stellar (Remote)
Marketing Manager, Backstage (Remote)
Demand Generation Manager, Shoplift (Remote)
Search Engine Optimization Manager, Confidential (Hybrid, Miami-Fort Lauderdale Area)
Note: We update this post weekly. So make sure to bookmark this page and check back.
Intel says that it is listening to feedback Club386 has had the opportunity to talk with Intelβs Robert Hallock, the companyβs VP and GM ot its βEnthusiast Channelβ. When asked about the possibility of βa future where Intel sockets support more CPU generations, Hallockβs answer was simple: βI do. Thatβs it β I doβ. Elaborating [β¦]
The post Intel says βwe are listeningβ when it comes to long-lived CPU sockets appeared first on OC3D.
As AI agents reshape how advertising platforms are used, Google is bringing focus toward the developers behind the systems and create content specifically for them.
Whatβs happening. Googleβs Advertising and Measurement Developer Relations team has launched Ads DevCast, a bi-weekly vodcast and podcast hosted by Cory Liseno. The show focuses on technical deep dives across Google Ads, Google Analytics, Display & Video 360 and related tools.
Zoom out. This is a companion to Ads Decoded, hosted by Google Ads Liaison Ginny Marvin, which focuses on campaign strategy. Ads DevCast is explicitly built for developers and technical practitioners.
Driving the news. Episode 1 β βMCPs, Agents, and Ads. Oh My!β β centers on what Google calls the βagentic shift,β where AI agents are becoming primary users of advertising APIs.
Why we care. Ads DevCast gives developers a direct line to the engineers building Googleβs ad tools, which should help stay ahead of technical changes, discover new capabilities faster, and build more efficient integrations in an increasingly AI-driven ecosystem.
The big picture. AI is expanding who can work with ad tech systems. Google is seeing a shift from a narrow βAds Developer Communityβ to a broader βAds Technical Community,β where marketers can execute technical tasks without full development cycles.
Whatβs next. Ads DevCast is a pilot, and Google is collecting feedback to shape future episodes.
Bottom line. Google is positioning Ads DevCast as a tool to give developers a front-row seat to Googleβs latest ads innovations, with practical insights to build, test, and adapt faster in an AI-first landscape.
A new Google Merchant Center update changes how e-commerce sites must handle out-of-stock products, with direct implications for product approvals and ad performance.
Whatβs happening. Google now requires that out-of-stock products must still display a buy button, but it can no longer be active or hidden. Instead, the button must be visibly disabled and appear grayed out. In other words, users should be able to see the button, but not click it.
This marks a clear shift from common practices where retailers either left the βAdd to Cartβ button clickable or removed it entirely. Both approaches are now non-compliant.
How it works. In practical terms, the requirement is simple. The buy button must remain on the page, but its functionality needs to be turned off. Typically, this is done by applying a disabled state so the button becomes unclickable and visually subdued.
The catch. The button change is only part of the update. Google also expects clear availability messaging on the product page, such as βin stock,β βout of stock,β βpre-order,β or βback order.β This information must match exactly with what is submitted in the product feed.
Any inconsistency between the page and the feed can lead to disapprovals.
The bigger shift. This update removes a long-standing workaround used by many retailers. Previously, it was possible to keep selling out-of-stock products by leaving the purchase button active. That approach is no longer allowed.
If a retailer still wants to accept orders for unavailable items, the product must now be labeled as βback order.β This status needs to be reflected consistently across both the landing page and the feed.
Bottom line. What looks like a small UI requirement is actually a meaningful policy change. Retailers will need to review how they manage out-of-stock products and ensure their pages and feeds are fully aligned to avoid disruptions.
First seen. This update was spotted by Google shopping specialist who shared the his how to video on LinkedIn.
Dig deeper. About landing page requirements
Google is testing AI-generated review replies in Google Business Profile.
Why we care. Responding to reviews can impact conversions and trust. But generic AI replies could be risky and erode trust, especially on negative reviews where authenticity matters most. Response quality matters more than whether a business replies to reviews.
What it looks like. Hereβs a screenshot:
The details. Google appears to be rolling out a limited test of Reply to reviews with AI inside Google Business Profile.
Early behavior. Some users report prompts focused on older, unanswered negative reviews.
First seen. The feature was first shared on LinkedIn by Chandan Mishra, a freelance local SEO specialist, and amplified by Darren Shaw, founder of Whitespark.
Google Chrome 146 fixes 26 security vulnerabilities but with no evidence of active exploitation so far. The update addresses three critical memory-related flaws, along with several high-risk issues impacting components like WebGL and the V8 JavaScript engine.
Wirewiki lets you explore internet infrastructure across domains, IPs, and DNS servers. You can search domain profiles, inspect IP addresses, and run lookups for DNS propagation, SPF, MX, TXT, reverse DNS, and website-to-IP. The platform helps you trace delegation paths, check zone transfers, and validate email sender records to troubleshoot issues or research setups. It's a quick way to answer routing questions and review how domains are configured.
Whether you're scaling a company or building a business, getting real value from AI is harder than it should be. Cuadra AI lets you build your own AI that continuously learns from your business, runs on any model, and works wherever your users are. You can synchronize your documents, Notion, or Google Drive, allowing your AI and knowledge to grow together. Connect with WhatsApp, Slack, SMS, or Telegram to meet your users where they already are. If you have a team, you can access your model in your own private workspace.
Google is testing AI-generated headline rewrites in Search results, describing it as a small, narrow experiment for now.
Whatβs happening. Google confirmed to The Verge (subscription required) that itβs testing AI-generated titles in traditional Search results, not just Discover.
One example showed Google replacing original headlines with shorter or reworded versions, sometimes changing tone or intent (e.g., reducing βI used the βcheat on everythingβ AI tool and it didnβt help me cheat on anythingβ to ββCheat on everythingβ AI tool.β).
Why we care. Google Search is already sending fewer clicks. Now you also have to contend with Google generating entirely new headlines with AI, risking changes to meaning, brand voice, and click-through rates.
Dig deeper. Google changed 76% of title tags in Q1 2025 β Hereβs what that means
What theyβre saying. Sean Hollister, senior editor at The Verge, wrote:
Title links. According to the Google Search Central section on title links, originally published in 2021:
Googleβs generation of title links on the Google Search results page is completely automated and takes into account both the content of a page and references to it that appear on the web. The goal of the title link is to best represent and describe each result.
Google said it uses these sources to βautomatically determine title linksβ
Login<title>Β elements<h1>Β elementsog:titleΒ metaΒ tagsWebSiteΒ structured data What to watch. Google called this one of many routine experiments, but thatβs no guarantee it stays small. The Verge noted a similar βexperimentβ in Discover later became a full feature.
Reaction. After seeing this news, Louisa Frahm, SEO director at ESPN, wrote on LinkedIn:
Intel is expected to launch its first βBig Battlemageβ GPUs next week According to Videocardz, Intel is getting ready to launch its first βBig Battlemageβ GPUs next week, on March 25th. Intelβs new GPUs are the ARC PRO B70 and the ARC PRO B65. Both of these GPUs feature 32GB of ECC GDDR6 memory, making [β¦]
The post Intel ARC Pro B70 and B65 GPU release date and Specs Leak appeared first on OC3D.
RConnectFor, inspired by Larry Mitchell's The Faggots & Their Friends Between Revolutions, which states "Friendship was not an idea or a status you took for granted, but something you did over and over," is a web and mobile platform to support all stages of reconnecting with friends and community
RConnectfor, allows you to set personal goals to define your reasons for seeking deeper connections, offers customized activity list to ensure meaningful interactions, intelligent scheduling features to minimize decision fatigue about when and where to meet, organizational tools for managing social groups, and a community space to share stories and find inspiration for growth and collaboration.
StackForge is an AI-powered platform designed to accelerate the development of SaaS applications and modernize legacy systems. Its core capability lies in transforming high-level specifications and complex code, such as PL/SQL procedures, into production-ready codebases using modern technologies like Java Spring Boot on the backend. As a self-contained modernization engine, StackForge eliminates repetitive manual work by automating the generation of entities, repositories, and services from a structured JSON Schema mapping.
YouTube is where all sports come to life β because no sporting event or game would be complete without the commentary, breakdowns and analysis surrounding it. In 2025 alβ¦AI wonβt make SEO obsolete, but itβll change how the work gets done. Thereβs a growing concern that as AI systems improve, theyβll replace the need for human SEO analysis entirely. Early experiments suggest otherwise.
While AI can assist with technical tasks and even generate usable outputs, it still depends heavily on detailed human input, structured data, and technical oversight to produce meaningful results.
The real shift is toward redistribution. AI is accelerating parts of the workflow, raising the bar for execution, and changing where human expertise matters most.
AI aims to reduce the need for semi-technical expertise. Where data is highly structured (e.g., coding a Python script), it has an advantage.
Even then, human expertise is still required. AI can generate scripts, but without detailed instructions and debugging, the output is often unusable.
Generative AI can produce working functions with strong prompts, but it still βthinksβ like a machine. Thatβs why technical practitioners are best positioned to get the most from it.
Technical knowledge is also required for AI-assisted SEO tasks like generating product descriptions or alt text at scale. Even with tools like OpenAIβs API, you still need to transform and structure data into rich, usable prompts β for example, turning Product Information Management data into prompt-ready inputs.
AI depends on human instructions, and output quality reflects input quality. Thinking in structured terms β IDs, classes, and distinct entities β is key to getting reliable results. Itβs what makes the output usable.
That makes prompt creation a critical skill. Employers should factor in technical expertise when using AI to drive efficiency.
However, donβt celebrate too soon.
As AI evolves and absorbs more information, this advantage may be temporary. For now, AI still depends on human expertise to function β which is why SEO isnβt obsolete.
Data is both AIβs strength and weakness.
Early generative AI models relied on curated data within their LLMs. OpenAIβs models couldnβt perform web searches up to and including GPT-4. After GPT-4, AI systems began relying less on internal data and more on web searches for fresh information.
Because the web isnβt curated and contains a lot of misinformation, this initially represented a step backward for most AI tools, including ChatGPT and Gemini. This shift also mirrors how traditional algorithms rely on raw information.
This raises a key question: Is more information always better for AI?
The open web contains both empirical data and subjective opinion, and AI often canβt distinguish between the two. Giving it access to uncurated data has arguably caused more errors and issues in its outputs.
Finding the right balance of data remains a challenge. How much data helps or harms performance, and how much curation is needed? While developers continue refining LLMs and connected systems, users still need to load up prompts with as much detail as possible to offset how AI sources and evaluates information.
These limitations highlight a core issue: without structured input and human judgment, AI struggles to produce reliable SEO insights.
Dig deeper: 6 guiding principles to leverage AI for SEO content production
Basic AI tools can assist with SEO tasks, but full automation is far more complex than it sounds.
That said, AI platforms and technologies are evolving rapidly. The first wave of this evolution came as organizations began producing AI agent platforms like Make, N8N, and MindStudio.
These platforms provide a canvas for automating workflows, combining inputs, outputs, and AI-driven decision-making. Used well, they can turn from-scratch content creation into structured editorial processes, with efficiency gains that can be significant.
However, applying this to real-world SEO work is where complexity sets in. A full technical SEO audit pulls from multiple data sources and environments β crawl data, browser-level diagnostics, and desktop tools.Β
While parts can be automated, stitching everything together into a reliable, end-to-end workflow is difficult and often requires custom infrastructure, API work, and ongoing maintenance.
Even with platforms like N8N, full end-to-end automation of complex SEO tasks remains challenging. Simpler, checklist-style audits can be automated, but deeper, more technical work often needs to be simplified to fit automation β which isnβt advisable.
In practice, fully automating SEO at depth requires tradeoffs β which is why human expertise is still critical.
Dig deeper: AI agents in SEO: A practical workflow walkthrough
More recently, thereβs been a wave of local AI applications that let you create your own βbrainβ on a laptop or desktop. These tools are often code editors with support for popular AI models, along with local structures for saving reusable skills, similar to Claude Projects or ChatGPT Custom GPTs.
Tools like Cursor and Claude Code allow you to connect models, generate code, and automate parts of workflows through prompts.
Itβs possible to use these technologies to vibecode a system that automates a technical SEO audit. I attempted this. While the capability exists, building a system that matches the depth and quality of a manual audit could take months, especially when handling large volumes of data.
Initial issues included memory limitations, where AI struggled to retain both the data and its detailed instructions. In some cases, outputs were also misweighted β for example, flagging missing H1s as critical despite finding no instances.
These issues could be resolved over time, but they highlight that these tools arenβt automatic shortcuts. Making effective use of them still requires technical expertise, time, testing, and troubleshooting.
They lower the barrier to building AI-driven systems, but they donβt eliminate the need for technical expertise. They simply shift the work.
For SEO to become obsolete, AI would need to operate independently, reliably, and at scale β without human correction. Generative AI can only act with human input, and it struggles to differentiate between fact and fiction.
Some algorithms have reached their limits in terms of commercial viability. This is arguably why Google is trying to convince us that links are redundant before they truly are.
Consider AI as an evolution of algorithmic output. These systems can attempt to make analytical determinations based on input data. However, the idea that feeding AI more and more data is an unrestricted path to success is already running into significant limitations.
This doesnβt mean technical analysts are entirely safe. Humanityβs ambition for faster, more efficient insights will continue. Initially, AI will be seen as the solution to everything. If one AI falls short, another can critique its results.
However, AI requires significant processing power. The real challenge will be finding the balance between AI and simpler algorithms. Algorithms should handle basic tasks, while AI should be used for analysis and insights.
This balance between AI and algorithmic efficiency is still years β perhaps decades β away. Only then will AI truly test SEO professionals and create the potential for redundancies.
AIβs learning is hindered by the webβs misinformation, providing SEO professionals with temporary insulation. This advantage wonβt last forever, but it offers a valuable head start.
Dig deeper: How AI will affect the future of search
There are also limitations tied to how society adopts AI. Many technological innovations β like the internet and the calculator β were initially considered βcheating.β
Calculators were banned from exam rooms, and the internet was seen as a shortcut compared to traditional research. Yet those perceptions didnβt last.
Most technologies, despite rapid advancement, arenβt adopted quickly due to cost and social factors. We value human perspective and often resist tools that threaten how we think or work.
The main barrier to AI replacing us is how we perceive it. As long as itβs seen as a threat to our ability to provide, it wonβt fully replace human roles. That perception, however, will change over time.
As these technologies become normalized, adoption will follow. Governments will adapt, and expectations around human creativity will continue to evolve.
Algorithms and Google didnβt end human interaction on the web, and AI wonβt eliminate contributions from people. In the medium to long term, adaptation is inevitable.
Dig deeper: How to start an SEO program from scratch in the AI age
AI bots could outnumber humans on the web by 2027, according to Cloudflare CEO Matthew Prince, as agent-driven browsing explodes alongside generative AI adoption.
Why we care. Search is shifting from human clicks to AI-generated answers. If bots become the webβs primary βusers,β youβll need to reshape your strategy to ensure AI systems can access, trust, and use your content.
The details. Prince said AI agents generate far more web activity than humans because they gather information differently. A person shopping might visit five sites. An AI agent could hit thousands.
He also noted the webβs baseline is shifting fast.
Prince said this growth isnβt spiking like COVID-era traffic. Itβs rising steadily with no end in sight.
Between the lines. Prince compared AI to past shifts like mobile and social. The difference: users may no longer visit websites directly. Instead, they rely on AI interfaces that aggregate and answer.
AI sandboxes. AI agents also change how computing works behind the scenes. Prince described a future where βsandboxesβ β temporary environments for AI agents β spin up and shut down instantly, potentially millions of times per second.
The result: sustained pressure on internet infrastructure.
The business impact. Companies are already split on how to respond to AI agents. Prince pointed to diverging strategies across major retailers.
At the core is a bigger risk: losing the customer relationship.
For publishers. Prince argued AI could both hurt and help media. While AI reduces direct traffic and breaks ad-based models, AI companies need unique, original data β especially local and hard-to-replicate information β and may pay for it.
He pointed to local media as an example.
For small businesses. Prince was more blunt. AI agents optimize for price, quality and efficiency β not brand loyalty or proximity.
That could erode traditional advantages.
What to watch. The next phase of the web will hinge on control and compensation. Prince said:
Prince said the core question is still unresolved:
The SXSW interview. The Internet After Search
You could be ranking in Position 1 and still be completely invisible.
I know that sounds counterintuitive. But hereβs whatβs actually happening:
A potential customer opens ChatGPT or Perplexity and asks, βWhatβs the best [tool/agency/platform] for [your category]?β Your competitor gets mentioned. You donβt. Your No. 1 ranking did absolutely nothing to help you.
This is the new SEO reality, and itβs catching many smart marketers off guard.
LLMs synthesize consensus across multiple sources, rather than relying on a single source. This means you need corroborating mentions distributed across the web. The game has shifted from ranking to consensus, and if you donβt understand that difference, youβre already losing ground.
Let me break down whatβs actually happening and, more importantly, what you can do about it.
Traditional SEO had a clear logic: rank high, get clicks, drive traffic. In this retrieval-based system, Google found pages and users chose which ones to visit.
AI-driven search doesnβt work that way. Systems like Googleβs AI Overviews, ChatGPT, and Perplexity are now constructing answers. They pull from dozens of sources, identify which claims appear consistently across credible publishers, and synthesize a single response.Β
The data backs up just how significant this shift is: organic CTRs for queries featuring AI Overviews have dropped 61% since mid-2024. Even on queries without AI Overviews, organic CTRs fell 41%. Users are simply clicking less, everywhere.
The technical engine behind this is retrieval-augmented generation (RAG). The AI retrieves content from across the web, gathers potentially dozens of sources, identifies the claims that repeat most consistently across credible publishers, and generates a response based on that consensus.
Your goal isnβt just to publish a great page. Itβs to be one of those sources. Repeatedly.
Think of the consensus layer as the degree to which multiple AI systems produce consistent, repeatable outputs about your brand. Itβs about pattern recognition at scale.
When AI systems encounter your brand described the same way across multiple credible sources, in the same category, with the same expertise, and with the same problems you solve, they build confidence. When they donβt see that pattern? You become a statistical outlier, and outliers get filtered out.
This happens because AI systems are engineered to prevent hallucinations. Their primary defense is corroboration: if multiple independent sources say the same thing, the AI assigns higher confidence to that claim. If only one source says it, the AI can become cautious or ignore it entirely.
This creates a rule most marketers havenβt fully internalized yet: isolated authority isnβt enough. You need distributed credibility.
Iβve seen this firsthand. A client ranking first for a competitive keyword, with solid traffic and strong domain authority, was invisible across ChatGPT. Why? Because that page existed in isolation. No corroboration, no distributed mentions, no external validation.Β
As Will Scott wrote: βBrands arenβt losing visibility because they dropped from position three to seven. Theyβre losing it because they were never cited in the AI answer at all.β
Dig deeper: The infinite tail: When search demand moves beyond keywords
So what signals do AI systems actually use? Hereβs where to focus your energy.
Backlinks, domain authority, and topical depth remain foundational. But theyβre no longer sufficient on their own. They get you in the game; consensus is what wins it.
AI systems scan the web for brand references, even when those mentions arenβt linked. Unlinked mentions are growing in importance as signals for both traditional search and AI visibility. A mention in an industry publication with no link is still a consensus signal.
Nearly 9 out of 10 webpages cited by ChatGPT appear outside the top 20 organic results for the same queries, per a Semrush study. This tells you everything you need to know about how different this game is.
Being mentioned repeatedly on the same domain doesnβt build consensus. Being mentioned across a range of credible, independent publishers does.
Diversity tells AI systems your authority isnβt contained to one corner of the web. Itβs recognized broadly across your industry.
Reddit, Quora, and niche forums are becoming major consensus signals. AI systems increasingly pull from community discussions because they represent real user opinions and experiences.Β
With Reddit dominating the SERPs, positive brand mentions in relevant subreddits contribute meaningfully to how AI systems perceive you. You canβt fake your way into genuine community trust, you have to earn it.
Search engines use knowledge graphs to understand entities and how they relate to each other. If your brand is inconsistently described across platforms or your category is ambiguous, AI systems struggle to incorporate you into their answers.Β
Structured data, schema markup, and JSON-LD are critical here. Google has explicitly stated that βstructured data is critical for modern search engines.β The clearer your entity profile, the easier it is for AI to retrieve and cite you.
Alright, letβs get tactical. Before you start building, you need to know where you stand.
Open ChatGPT, Perplexity, Gemini, and Google AI Overviews, and start asking questions the way your customers would.Β
Pay attention to three things:Β
You may find outdated information, missing context, or, worse, a competitor owning the narrative in your category entirely.
This audit becomes your baseline. It tells you what gaps to close, what misinformation to correct, and where your consensus footprint is weakest. Only once you know that, should you start building.
Your site needs to be technically sound and semantically clear. Use structured data. Establish explicit entity definitions, who you are, what you do, and what problems you solve. Reinforce those same entities and relationships across multiple pages within your site.Β
Topic clusters, pillar pages supported by related subtopic content, create semantic reinforcement that signals depth and expertise. Without a strong foundation, nothing else sticks.
Press coverage, guest posts, podcast appearances, and expert citations distribute your authority across the web. More than links, digital PR is now about narrative control.Β
One placement wonβt move the needle. A sustained, coordinated presence across trusted publications will. Monitor your brand-to-links ratio, unlinked mentions alongside traditional link building is now the balanced strategy to pursue.
This is the highest-leverage consensus tactic most brands are underinvesting in.Β When you create genuinely novel data, an industry benchmark, a proprietary survey, original research, other publishers reference it naturally, journalists cite it, and AI systems incorporate it into answers. Establish yourself as the source for benchmark data in your niche, and youβll earn citations for years.
AI systems are trained on vast amounts of text, including articles, research, and interviews. When your team members are consistently positioned as recognized experts, quoted in articles, cited in reports, and contributing bylined pieces, they become recognized entities that AI systems trust. Optimize author profiles with structured data, consistent bylines, and entity markup to reinforce this.
This doesnβt mean dropping links in Reddit threads. It means answering questions, contributing knowledge, and building a reputation where your audience already hangs out.Β
When users recommend your brand organically because they find it genuinely valuable, thatβs your strongest consensus signal.
Dig deeper: Why surface-level SEO tactics wonβt build lasting AI search visibility
Traditional rankings tell you where you stand in search results. They donβt tell you whether AI systems are citing you. You need new metrics, and as more SEOs are recognizing, success metrics are shifting from clicks and traffic to visibility and share of voice.
Start by systematically testing high-value queries across Google AI Overviews, ChatGPT, Perplexity, and Gemini. Note when your brand appears, how itβs described, and which sources get cited alongside you.Β
Track share of voice in AI responses, how often your brand gets mentioned relative to competitors in AI-generated answers. If competitors are consistently appearing and youβre not, youβre losing the consensus battle regardless of how your rankings look.
Also monitor cross-domain mention density (how many unique domains reference your brand) and entity co-occurrence (how often your brand appears alongside relevant topics, competitors, and concepts). These give you a real picture of your consensus footprint and where the gaps are.
The brands winning in AI-driven search arenβt necessarily the ones with the best content or the highest domain authority. Theyβre the ones building distributed credibility, authority that appears consistently across owned media, earned media, and community platforms.
As Googleβs Danny Sullivan said, βGood SEO is good GEO.β The fundamentals havenβt disappeared, but theyβre now table stakes, not differentiators. The new formula is: authority + consensus + distribution.
Integrate SEO, digital PR, and community engagement into one cohesive strategy. Building a distributed network of authority, mentions, citations, and community validation that takes time to construct, and is nearly impossible for competitors to dismantle overnight.
Thatβs the visibility moat worth building, and the clock is ticking.
Dig deeper: Content alone isnβt enough: Why SEO now requires distribution
Competitor Analyzer helps marketers track and analyze competitors' social media across Facebook, Instagram, and X. You can use a unified dashboard to compare engagement rates, content performance, posting frequency, and audience growth over time. Monitor trends with historical data and receive AI-powered insights and alerts that surface actionable opportunities while maintaining secure data controls.
Adobe will shut down the SEO feature in Marketo Engage at the end of March 2026, according to its February 2026 release notes.
The tool will be deprecated on March 31, and you must export any existing SEO data before then. (This page includes links to the export instructions.) The SEO tile will be removed from the platform on April 1.
What happened?
Adobeβs Keith Gluck said deprecating low-use features lets the Marketo Engage team focus on other areas of the platform. For your SEO needs, Adobe announced in 2025 that it was acquiring Semrush, a full-featured SEO and visibility tool. (Reminder: Semrush owns Third Door Media, the publisher of Search Engine Land.)
The deprecation came as no surprise if you follow Marketo news closely. Reports suggest few people fully configured the SEO tool, and its features didnβt seem to be a priority for the Marketo Engage product team in recent years.
With LLMs rapidly changing the search landscape, it was time to say goodbye. The arrival of Semrush into the Adobe family provided the perfect opportunity.
If your law firmβs referrals arenβt converting, validation may be the problem.
Referred prospects donβt go straight from recommendation to contact. They research, compare, and verify what they were told β on your website, in search results, and through AI tools.
These are your highest-value leads β pre-sold through trusted recommendations and expected to be your easiest conversions. But when that validation falls short, even they lose momentum.Β
This is the referral validation gap: the moments during online research when trust is broken rather than built. Hereβs where referral validation fails and how to fix it.
While this article focuses on law firms, the same dynamics apply to any referral-based business.
Referral loss follows predictable patterns β and once you can spot them, you can fix them.
In under three seconds, a website visitor forms a first impression. If your site doesnβt immediately validate what the referrer said about you β if it looks outdated, generic, or fails to showcase the specific expertise they praised β that trust becomes conditional.
A referred prospect arrives expecting professionalism, confidence, and authority, only to encounter uncertainty. Thin attorney bios, generic claims (βexperienced,β βtrusted,β βresults-drivenβ) without proof, or outdated design can all create hesitation.
The referral earned you consideration. Your digital presence determines what happens next.
The prospectβs reaction is simple: This doesnβt look like what I was expecting. That moment of doubt is often enough to end the process.
What you can do about it
Implement practice area-specific landing pages with targeted H1s, schema markup for your specialties, and prominent visual trust signals (credentials, case results, awards) above the fold. Ensure mobile page speed stays under two seconds with Core Web Vitals optimization.
Referrals are almost always problem-specific. The website theyβre referred to rarely is.
Imagine a prospect referred for a complex custody dispute lands on a homepage about βfamily law.β A business owner referred for a ground lease negotiation sees βcommercial real estate services.β
Nothing is technically wrong. But nothing confirms the recommendation. When a site fails to mirror the exact issue that prompted the referral, the prospect starts to question it: Does this firm actually specialize in my problem, or was the referral overstated?
At the same time, prospects are actively looking for proof β case results, credentials, relevant experience. If that evidence is buried, disconnected, or requires more than two clicks to find, momentum drops quickly.
What you can do about it
Create practice area-specific case study pages with structured data markup. Implement FAQ schema tied to common referral scenarios. Ensure content directly reflects the search intent behind the referral, and use internal linking to guide visitors from homepage β specific expertise β proof points within two clicks.
Referral prospects are asking questions like: βIs this firm actually good at complex custody cases?β or βDo they have experience with ground lease negotiations in New York?β β increasingly through AI search tools.
If AI tools canβt find credible, structured information on your site to validate the referral, they wonβt confirm it. And if competitors provide clearer answers, those are the sources AI will surface. This creates an immediate form of negative validation. The prospect starts to question the recommendation: If theyβre so good, why arenβt they showing up here?
If a competitor has invested in content thatβs structured for citation, the AI will quote them, reference their work, and position them as the authority, even though the prospect came to you through a trusted referral. You canβt claim authority. AI systems will either confirm or contradict it.
What to do about it
Forward-thinking firms are now monitoring a new metric: AI search share of voice β the percentage of relevant AI-generated answers that mention or cite your firm compared to competitors. Start by:
If your firmβs content, credentials, and case results arenβt structured for AI parsing and citation, youβre invisible in these crucial validation moments regardless of how strong the initial referral was. Once youβve identified where your competitors are outperforming you, create in-depth topic clusters around your specialties, and build authoritative content that answers the questions prospects ask AI tools.Β
Friction gaps occur after trust has already been established, but conversion still hasnβt happened. Common examples include:
At this stage, prospects are ready to act. But any delay introduces doubt and gives them time to reconsider or move on. Youβve earned the referral. Your site validated your expertise. The prospect is ready to hire you β but canβt quickly figure out how to take the next step.
This is the final failure point in the referral validation gap: when a motivated, pre-sold prospect abandons because the conversion path is unclear, inconvenient, or unnecessarily complicated. You need to remove every obstacle between βI want to hire this firmβ and βIβve made contact.β
What to do about it
A referred prospect should be able to answer these questions within three seconds of landing on any page:
Test it yourself: open your site on your phone and start a timer. Can you initiate contact within a few seconds without scrolling? Try it from a homepage, attorney bio, and practice area page. If the answer is no, youβre losing prospects at the finish line.
Closing the referral validation gap doesnβt require a complete digital overhaul on day one. Strategic, phased implementation will allow you to see quick wins while building toward comprehensive optimization. Letβs look at the steps you can take.
These are some changes that require minimal investment but can immediately reduce referral abandonment:
These initiatives can require more investment but, over time, can generate a sustainable competitive advantage:
These strategic initiatives can position your firm for sustained advantage in an AI-driven search environment:
But, most importantly, donβt let this roadmap overwhelm you. The firms that successfully close the referral validation gap donβt do it by accomplishing everything all at once. Instead, they start with a single, crucial decision: acknowledging that the gap exists. And then they take the first step to fix it.
Once you accept that your best leads are researching you β on your website and through AI tools β and making judgments based on what they find (or donβt find), your path forward for fixing that gap will become clear.
Prospects are getting their answers without ever visiting your website. The gap between digital presence and digital authority is widening β and for firms that wait, it becomes unbridgeable.
Closing the referral validation gap isnβt just about improving conversion rates. It means:
Firms that master this will pull ahead. Those that donβt will watch their best leads slip away β one validation failure at a time.
A referral gets you consideration. Your digital presence determines what happens next. Closing the referral validation gap turns trust into conversion.
Intel reportedly plans 10% CPU price hike at the end of this month According to a report from ETNews, Intel has informed its customers about a price hike that will apply to βmost major productsβ in its CPU lineup at the end of this month. CPU prices will reportedly rise by 10%, placing greater cost [β¦]
The post Intel reportedly informs PC makers of incoming CPU price hikes appeared first on OC3D.
Intelβs Precompiled Shader Distribution is exclusive to Xe2 and newer ARC GPUs Intel has confirmed that its Precompiled Shader Distribution technology will be exclusive to its ARC Xe2 and newer GPU architectures. Specifically, itβs coming to Core Ultra 3/200V series CPUs and Intelβs ARC B-series discrete GPUs. As for Intelβs ARC Alchemist A-series GPUs, they [β¦]
The post Intel confirms that ARC A-series GPUs wonβt get Precompiled Shader Distribution appeared first on OC3D.
SEO has moved past shortcuts and quick wins. What drives results now isnβt just content β itβs content that earns attention, builds trust, and converts.Β
Storytelling plays a direct role in that. Used well, it can improve engagement signals, strengthen relevance, and turn traffic into action.
Here are seven storytelling techniques to apply in your business blog.
Use these to shape how your content flows, from the opening hook to the final call to action.
T.S. Eliot put it simply: βIf you start with a bang, you wonβt end with a whimper.β
Many modern authors recommend starting a story in the middle of the action and letting readers catch up. But how does that apply when youβre writing a B2B or B2C blog?
You can still hook your reader, just with different techniques:
Donβt be afraid to combine these techniques in your blog posts. If you struggle with what to come up with, a success story is always a great way to begin a B2B blog. Empathizing with a readerβs issues, then promising a solution, works for both B2B and B2C blogs.
Stories are full of foreshadowing: hints that somethingβs going to happen, language that immerses the reader in the genre, and elements that build suspense.
To get a reader excited about your blog, build suspense with the same techniques. Use phrases like βYou will learnβ¦β or βYou will discoverβ¦,β tell them what youβre going to tell them, and use compelling language throughout.
This is particularly important the first time you mention a keyword. Why? Because regardless of what you write for a meta description, Google often ignores it and uses text from the page instead β most commonly where a keyword is first mentioned. If this is part of a promise stating what your article, product, or business solution will deliver, this will improve your CTR.
Dig deeper: 5 behavioral strategies to make your content more engaging
Fiction writers spend a lot of time debating whether to write in first person (I/me) or third person (they/he/she). You have the option of the second person (you), but donβt always take full advantage of it.
Using βyouβ rather than βourβ can make your content feel more direct and personal. Consider which of these resonates with you most strongly:
While βyouβ is important, another largely overlooked word is βmy,β at least when it comes to calls to action (CTAs). In a story, you imagine yourself as the hero. In a business blog, using βmyβ evokes the same feeling β this action is meant for you. It wonβt work for every CTA, so experiment with it, monitor the results, and you may be surprised by the outcome.
Authors are sometimes told to βkill your darlings,β meaning to remove extraneous characters or even whole chapters. Your blog must do the same. For each paragraph, ask yourself if it achieves one of the following:
If a paragraph doesnβt advance, engage, or persuade, ask yourself if you can delete it.
Dig deeper: How to align your SEO strategy with the stages of buyer intent
If a potential customer relates to the problem you describe, youβre off to a good start. If they can imagine using your product or service, youβre halfway there.
Not every blog needs to present a solution. But if your blog convinces readers they need your solution, it will increase conversions.
Author Jessica Brody puts it this way:
To fully embrace storytelling in your blog, create a three-act story. Hereβs one way you could achieve this:
Dig deeper: How to apply βThey Ask, You Answerβ to SEO and AI visibility
Even professional authors say some version of βYour first draft will suck.β Donβt expect perfection when you start writing. You have the luxury of revising your work.
Once you finish your first draft of your business blog, you know what you want to say, along with the structure and main points. Editing is where you decide how to say it.
When youβve finished editing, youβll have a polished blog that tells a story, engages your reader, and generates conversions.
These techniques make your content more effective, and their impact shows up in performance. Evaluate content using measurable outcomes to reduce subjectivity and ensure it supports your business goals.
As you experiment with storytelling in your business blog, measure:
You can measure the first three in Google Search Console. You can measure the last two in Google Analytics. These metrics give you concrete data to compare content and assign financial value.
With experimentation, you wonβt just tell a better story β youβll drive measurable traffic and conversions.
Crimson Desert doesnβt run on Intel ARC GPUs β Gamers told to get a refund Crimson Desert is now available on PC, and it doesnβt run on Intel ARC graphics cards. When gamers try to run the game with Intel ARC graphics, they are greeted with a βThe graphics device is currently not supportedβ error [β¦]
The post Crimson Desert says no to Intel ARC GPUs β Get a refund appeared first on OC3D.
OrchestrAI helps engineers move from prototype to production by automating code quality, security, compliance, documentation, testing, and orchestration. It analyzes your codebase to find issues, maps them to standards like OWASP and CWE, and generates fixes with pull requests ready for review.
OrchestrAI also keeps documentation up to date, creates comprehensive test suites across popular languages, and adds instrumentation for analytics and observability, allowing teams to ship reliable, compliant software faster.
Cleanlist is the GTM playbook engine that turns messy lead data into action. It enriches emails and phone numbers via a 15+ provider waterfall, verifies deliverability, adds firmographic context, and syncs results to your CRM and sales engagement tools in real time. ICP scoring, routing, and intent signals help you focus on accounts that convert.
Use the Chrome extension, Sales Navigator import, or CSV upload to extract contacts. Then launch pre-built playbooks for outbound, ABM, events, and CRM cleanup with simple credit-based pricing.
PodShrink uses AI to condense full-length podcast episodes into concise, narrated audio summaries. You can choose your duration, voice, and language, then hit play to get key insights without the filler.
Search millions of shows, generate 1 to 10 minute briefs in under two minutes, and listen in nine languages with 12 premium voices. Stream in your browser or save summaries to your library to reclaim hours each week while staying fully informed.
Valura.ai is a client-facing wealth platform built to simplify investing from planning to execution to monitoring. We solve three core frictions: high minimum ticket sizes, confusing product choices, and fragmented accounts across multiple providers. Valura enables fractional participation through regulated micro-units, guides users with a quantified goal-based roadmap that includes monthly targets, risk level, and product mix, and consolidates visibility and control through a single command center that connects to multiple brokers and custodians. The result is a simpler, more disciplined investing experience designed for modern investors.
Push events and chat with Claude Code via Telegram & Discord
Create ads with AI actors that look truly human
Your repository becomes your agent
Instant Privacy & Screen Blur
Turn your Mac's top edge into a hidden command center
Full-stack vibe coding powered by Antigravity + Firebase
The Mac cleaner built for developers
A Gmail with clearer inbox, focused writing, less noise
Mindful screen time for macOS without blocking apps
Stop bridging the design-to-code gap, close it
AI-Powered Mac System Data Cleaner
Agent that collects feedback across multiple platforms
Knowledge Sharing for AI Agents
The Ultimate Sheet Music Library Solution
One place for all your AI skills
See how developers really experience your product
Build Figma plugins with just a prompt
Build modern client portals for service businesses
A better Quick Look: code, Markdown, Mermaid, SQLite & more
Fast, token-efficient frontier-level coding model
turn your backend into a chat app instantly
Speak like you always know what to say
Everything you need to build your own membership
Utterly brings fast, private speech-to-text to iPhone, iPad, and Mac. It runs fully on device with no accounts or cloud, supporting 26 languages for meetings, lectures, interviews, and notes. Use live transcription and captions, dictate polished text, or transcribe audio files and system audio. Start free or unlock unlimited file transcription and more with Pro or a lifetime license.
CouncilDesk aggregates opinions from several leading AI models, conducts a blind peer review, and delivers a consensus verdict on your decisions, tasks, and presentations. You pose a question or upload materials to get independent recommendations; a designated "chairperson" then formulates the final strategy and action plan.
The platform integrates with OpenRouter, Together AI, Groq, OpenAI, XAI, and local APIs, and supports cloud sync and use of your own API keys. A free tier offers 10 "councils" per month, while Premium allows unlimited access.
Claudify is an operating system for Claude Code that adds 1,727 skills, 9 agents, and persistent memory to your development workflow. It works across editors and terminals including Cursor, Windsurf, VS Code, Warp, and the standard terminal, so you install it once and use it everywhere. Claudify lets Claude Code execute real tasks, remember context, and coordinate agents to build, refactor, and automate from your own stack while you keep control of your setup.
Edunation is a digital platform that connects teachers, students, and parents while simplifying school operations. Schools manage classes, schedules, resources, assignments, grading, and attendance in one place, with messaging, announcements, and push notifications to keep everyone aligned. The platform supports analytics for data-driven decisions, fee plans and invoices, and secure consent management. Students get guided learning paths and progress tracking, while AI helps generate quizzes and assist grading.
BillionVerify provides an AI-native email verification API that delivers 99.9% SMTP-level accuracy in under 300 ms. It integrates with MCP for Claude and Cursor, LangChain, CrewAI, and leading marketing platforms. Use it to validate signups, clean prospect lists, and protect sender reputation with spam-trap, disposable, catch-all, and role-based detection. BillionVerify scales from real-time checks to bulk processing with 99.99% uptime and global coverage.
NUVC uses multiple AI agents to analyze your pitch deck in 60 seconds. It extracts key signals, scores you across five investment lenses with a NuScore visible to founders, prioritizes red flags, and stress-tests your financials. You get clear next steps and a path to fundability, plus matches to thesis-aligned investors when you hit the bar.
Calibrated on 180+ real VC memos and used by thousands of investors, NUVC keeps your deck private and encrypted and delivers a concise report with actionable fixes.
Users will be able to press a button to get an AI-generated summary of the key points of any long-form article posted in the app.Β
The downvotes, which will only be available on post replies, will help X to train its ranking algorithms.
The Edits team has also added some new cinematic effects and editing options.Β
TikTok's also launching a new #BookTok label that can be added to books in stores.
Pinterest says that advertisers are seeing significantly improved results when the use its AI targeting tools.
Meta says that its AI systems are getting much better at performing moderation tasks.Β
UNTILL is a social wellness and productivity app that rewards time spent offline. It gamifies and quantifies intentional unscreened activities, lets you stay connected with others, earn points, and uses positive reinforcement with social accountability to build healthier habits. The platform has no ads and is opt-in by design, giving you control over your time and data.
Flowlines is an observability and memory layer for production AI agents. It helps teams understand why their agents fail and prevents the same mistakes from happening again. Flowlines captures every LLM call as structured traces and highlights issues like context loss, inconsistent behavior, and user frustration. It extracts structured memory from these interactions and feeds it back into your agent to improve performance over time. Install a lightweight SDK (Node.js or Python), monitor sessions in real time, and turn every interaction into persistent state your agent can use.
AMDβs FSR 4.1 upscaler is now available with AMD Software 25.6.1 With the release of AMD Software 26.3.1, the company has officially launched its FSR 4.1 ML upscaler. This new FSR release uses the same neural network foundation as Sonyβs new/improved PSSR upscaler, which recently became available to PlayStation 5 Pro owners. This new driver [β¦]
The post AMD Software 26.3.1 arrives with FSR 4.1 and new game support appeared first on OC3D.
As technology companies, we are deeply concerned about the breakdown in EU negotiations to secure the continued protection of minors against child sexual abuse. Allowingβ¦
River puts an AI sales employee on your website who video calls visitors the moment theyβre curious. It personalizes conversations by industry and role, answers product and pricing questions, handles objections, and speaks any language to convert interest into action.
River qualifies buyers, books meetings or closes deals on the spot, follows up with documents and next steps, logs every conversation, and only routes high-intent buyers to your team, helping you capture more pipeline without slow forms or follow-ups.
Walmart said conversion rates for purchases made directly inside ChatGPT were three times lower than when users clicked through to its website.
Why we care. This suggests agentic commerce isnβt ready to replace traditional shopping. Sending users to owned environments still drives higher conversion rates.
The details. Starting in November, Walmart offered about 200,000 products through OpenAIβs Instant Checkout. Users could complete purchases inside ChatGPT without visiting Walmartβs site.
Goodbye, Instant Checkout. Instant Checkout was designed to let users complete purchases directly inside ChatGPT without visiting a retailerβs website. However, earlier this month, OpenAI confirmed it was phasing out Instant Checkout in favor of app-based checkout handled by merchants.
Whatβs changing. Walmart will embed its own chatbot, Sparky, inside ChatGPT. Users will log into Walmart, sync carts across platforms, and complete purchases within Walmartβs system.
The WIRED report. Why Walmart and OpenAI Are Shaking Up Their Agentic Shopping Deal (subscription required)
HejBit is a backup solution for Nextcloud that stores files on decentralized Swarm storage instead of a single server. Instead of relying on traditional cloud providers, it distributes encrypted data across the network, giving users another way to protect their files while keeping control over where they are stored.
We're currently running an early adopter program and looking for Nextcloud users who want to test decentralized backups in real environments. The goal is to gather feedback, improve the product, and better understand how decentralized storage fits into everyday Nextcloud setups.
ClawStreet is a platform where autonomous AI agents reason, plan, and trade stocks with zero human intervention. Agents register themselves, analyze real market data with 15+ technical indicators (RSI, MACD, Bollinger Bands, etc.), and execute trades autonomously. It is built on the OpenClaw framework or lets you roll your own agentic workflow. Paper trading only, so there is no financial risk.
Compatible agents include OpenClaw, NemoClaw, NanoClaw, ZeroClaw, Nanobot, PicoClaw, Clearl, Cursor Automation, or you can build your own with any language or LLM.
Nvidia has upgraded GeForce Now with a 90 FPS VR mode and has added support for several new games Nvidia has upgraded its GeForce Now service for Ultimate Members, adding a new 90 FPS gameplay mode for users of VR headsets. This includes Appleβs Vision Pro, Meta Quest devices, and Pico devices. Users can create [β¦]
The post Nvidia GeForce Now gains a 90 FPS VR mode and several new games appeared first on OC3D.
Perplexityβs new Comet browser for iOS defaults to Google Search. Thatβs because mobile queries often focus on navigation, local results, and transactions, where βGoogle does a much better job β¦ than anyone else β¦ including Perplexity,β according to Perplexity CEO Aravind Srinivas.
Comet for iOS. It includes Perplexityβs AI assistant directly in the browser. Comet for iOS also blends AI answers with standard search results. For many queries, youβll still see a traditional results page.
What Comet does. According to Perplexity, the assistant can act on your behalf. Examples include:
What Perplexity is saying.
Why we care. The near future of search increasingly looks hybrid, which means youβll need to optimize for traditional Google results and AI-driven answers. This also reinforces Googleβs dominance in commercial and local search while shifting competition to the AI layer.
The announcement. Comet is Now available on iOS
Microsoft is changing how advertisers configure automated bidding, aiming to reduce complexity while keeping performance outcomes the same.
Whatβs happening. The platform is streamlining its bidding options by folding familiar targets like Target CPA and Target ROAS into broader automated strategies rather than standalone campaign settings.
Going forward, advertisers will choose between two core approaches: Maximize Conversions or Maximize Conversion Value, with optional targets layered on top.
How it works. For conversion-focused campaigns, advertisers select Maximize Conversions and can optionally set a target CPA. For value-focused campaigns, they select Maximize Conversion Value and can optionally set a target ROAS.
Microsoft says the underlying bidding behavior has not changed β only the way advertisers configure it has been simplified.
Why we care. This update makes automated bidding simpler and more standardized, which lowers the barrier to using Microsoft Advertisingβs performance tools at scale. By consolidating Target CPA and Target ROAS into broader strategies, it reduces setup complexity while still keeping key performance controls available as optional targets.
In practice, this means faster campaign setup, more consistent optimization behavior across accounts, and fewer structural differences between how advertisers manage conversion and value-based bidding.
Whatβs staying the same. Existing campaigns using Target CPA or Target ROAS will continue to run normally without any required updates. Portfolio bid strategies also remain unchanged.
The bigger picture. The change is part of a broader push to make automated bidding more accessible, reducing setup decisions while maintaining control over performance goals.
Bottom line. Microsoft is consolidating bidding options into simpler frameworks, keeping familiar optimization controls available but moving them into a more streamlined setup experience.
Google is doubling down on the infrastructure behind βagentic commerce,β introducing new capabilities to its Universal Commerce Protocol (UCP) while making it easier for retailers to plug in.
Google says UCP β its open standard for connecting retailers to AI-powered shopping experiences β is getting new features designed to make online buying feel more like a traditional storefront, even when handled by automated agents.
Whatβs new. The latest updates focus on making shopping via AI agents more functional and flexible.
Why we care. This update accelerates the shift toward AI-driven, agent-led shopping, where platforms like Search and the Google Gemini app may choose, compare and even purchase products on usersβ behalf. That makes product data quality β pricing, inventory and feeds β very important for visibility, while simplified onboarding and support from platforms like Salesforce and Stripe suggest rapid adoption, giving early movers a competitive edge.
Zoom out. UCP is designed as a modular system. Retailers and platforms can choose which capabilities to adopt, rather than implementing everything at once.
That flexibility is key as the industry experiments with how much control to hand over to AI-driven shopping experiences.
What Google is doing. Google plans to bring these capabilities into its own ecosystem, including AI-powered experiences in Search and the Google Gemini app.
The company is also working to expand adoption by lowering the barrier to entry. A simplified onboarding process inside Merchant Center is expected to roll out over the coming months.
Bottom line. UCP is evolving from a concept into a broader ecosystem play. By adding more capabilities and simplifying onboarding, Google is pushing to make agent-driven commerce easier to adopt β and harder to ignore.
Our new study shows what happens when contrail avoidance is built directly into the tools airlines already use.Google is sharing five ways itβs helping to protect you from scammers this tax season.A look at what Google learned when it collaborated with Stanford researchers to find out why some people adopt AI, and some donβt.
Demi turns Slack into a command center. It auto-drafts customer replies from your teamβs Slack history, surfaces answers before you need them, and delivers morning briefings and channel digests so sales and support stay on top of what matters. Connect it to your Slack workspace to search past threads, docs, and decisions, then review and send customer-ready responses without pinging engineering. Demi helps your team cut through noise and focus on closing deals while protecting your data.
HeyDriver is a privacy-first QR code communication tool. Generate a unique QR sticker for your car, luggage, keychain, or wallet. When someone scans it, they can instantly send you a message β delivered to your email, no personal info exchanged, no app needed.
Lost luggage at the airport? Blocked driveway? Found someone's keys? Just scan and type. Currently in beta β join the waitlist at heydriver.app and get a free Premium account.
Google's Universal Commerce Protocol adds cart management and catalog access, highlights identity linking support, and begins simplifying Merchant Center onboarding.
The post Google Expands UCP With Cart, Catalog, Onboarding appeared first on Search Engine Journal.
o Intel launches its Precompiled Shader Beta for ARC graphics cards With its Intel Graphics Driver 32.0.101.8626 for ARC graphics cards, Intel has launched its Precompiled Shader Distribution Beta. With this beta, users of ARC B-series (Battlemage) GPUs and Intel Core Ultra 3-series and 2-series CPUs with built-in ARC GPUs can benefit from precompiled shaders [β¦]
The post Intel launches Precompiled Shader Delivery with its ARC GPUs appeared first on OC3D.
Kaggle Community Hackathons enable people to to create their own hackathons with prizes up to $10,000.Start building real apps for the modern web with the Antigravity coding agent and Firebase integration now in Google AI Studio.
Every time a new large language model (LLM) drops or Google tweaks an AI Overview, the SEO industry loses its mind. We develop this weird collective amnesia, scrambling to optimize for features that were actually mapped out in patent offices 10 years ago. Weβre so obsessed with the now and the next that weβve stopped looking at the blueprints.
If you want to survive 2026, stop trying to be a futurist. Instead, be an archaeologist.
To actually deliver for our clients, we need a research framework that isnβt just reactive. It has to be a balance: Look back at the foundational patents to understand the rules, and look ahead to see how AI is finally being given the muscle to enforce them.
Thereβs a massive misconception that to understand AI search, you need to be a prompt engineer or read every new research paper from OpenAI. You donβt.Β The logic governing todayβs magic is often math that was written a decade ago.
We canβt talk about patent research without honoring the late, great Bill Slawski. For 20 years, he was the SEO industryβs archaeologist. While everyone else was arguing about keyword density, he was reading dry, technical filings to predict exactly where weβre standing right now.
History proves his method worked.
The algorithm isnβt magic. Itβs math. When a new feature drops today, the engineering blueprints were likely filed between 2007 and 2016. If you want to win, go read the old stuff.
Dig deeper: The origins of SEO and what they mean for GEO and AIO
Donβt get buried in buzzwords. Categorize your learning into two buckets: βstrategyβ or βmechanic.β
For years, the industry talked about moving from strings to things (entities). But in 2026, thatβs just the baseline. Weβve moved from strings to verifiable things. An entity is worthless if the AI canβt prove itβs real.
Think of it like building a house:
The industry often uses AEO and GEO synonymously, but they require different content structures and serve different objectives.
AEO is for the βdirect answer.β Think Siri, Alexa, or that single snippet at the top of the page. Itβs binary. Itβs rooted in those 2006 fact repository patents.
You need βconfidence anchors.β These are unnuanced, structured facts. The engine isnβt βthinking,β itβs fetching. If your fact isnβt provable and anchored to a verified source, the engine wonβt risk a hallucination by citing you.
GEO is for the βsynthesis.β This is Gemini or ChatGPT search explaining how something works. It was formally defined by researchers at Princeton and Georgia Tech in 2023.
You need information gain. These engines donβt just want a fact; they want to see how Concept A affects Concept B. Theyβre looking for relationships and unique perspectives.
In short, AEO is about being the fact. GEO is about being the authority that the AI trusts to explain those facts.
Dig deeper: SEO, GEO, or ASO? What to call the new era of brand visibility in AI [Research]
Thereβs a danger in becoming an SEO time traveler. If you spend all your time in the patent archives or stress-testing GEO relationships, you might forget that the AI still has to reach your content.
You can have the most verified, E-E-A-T-heavy content in the world, but if your siteβs technical health is a mess, the confidence anchors will never weigh in.
Basic SEO requirements havenβt changed. The tolerance for ignoring them has simply disappeared.
Many of the frustrating technical SEO issues weβve fought for years β like bloated JavaScript and poor Largest Contentful Paint (LCP) β are finally being solved by headless/composable architectures. By decoupling the front end from the back end, we can deliver the raw, lightning-fast data that answer engines crave while maintaining a high-end experience for humans.
But headless isnβt a βget out of SEO jail freeβ card.Β It solves the speed problem, but it introduces new risks around dynamic rendering and metadata delivery.
Whether youβre on a 20-year-old CMS or a cutting-edge headless build, the today requirements are non-negotiable:
You donβt get to play in the frontier of AEO and GEO until youβve mastered the floor of technical SEO. Donβt let the shiny new objects make you forget the shovel work.
Dig deeper: Thriving in AI search starts with SEO fundamentals
The SEO time traveler isnβt looking back because theyβre nostalgic. Theyβre looking back because they want the blueprint. When you realize AEO is just the modern enforcement of a 20-year-old patent and GEO is just the evolution of semantic relationships, the chaos of AI updates disappears.
Stop optimizing for strings. Start optimizing for verified facts. Give the engine a fact it canβt doubt, connected to a person it trusts, and a relationship it canβt ignore.
The future of search wasnβt written this morning β it was written years ago. You just have to be the one to actually build it.
Dig deeper: The future of SEO: Why optimization still matters, whatever you call it
AnySlate is a modern Markdown editor for macOS, Windows, Linux, and the browser. It delivers a fast writing experience with real-time collaboration, cloud sync, and version history. Use AI to summarize, rephrase, and improve drafts, or extend capabilities via MCP. You can preview and export with professional control, publish to the web, and customize themes and styling so your workspace fits the way you write.
Opera GX acknowledges PC gamingβs Linux shift with official browser support Opera GX has officially arrived on Linux, giving Linux users a gaming-focused browser option. As a web browser, Opera GX prides itself on its performance, privacy, and customisability. These are all traits that Linux users love. At launch, the browser is available in Debian [β¦]
The post Opera GX Gaming Browser launches on Linux appeared first on OC3D.
Universal Commerce Protocol (UCP) releases new capabilities, and Google shares a new onboarding experience to simplify UCP integration.Weβve signed 1 GW of data center demand response with utility partners, supporting smart, affordable electricity growth.
Multi-location brands are investing heavily in content. But more content doesnβt automatically mean more growth.
I keep seeing the same issue. Each individual location has a blog, and they all cover the same topics. Same keywords. Same structure. Same search intent. The goal is local visibility, but the result is often internal competition and diluted authority.
Building an effective content strategy for multi-location brands requires clarity around roles. What should live at the corporate level to build authority, and what should stay local to drive relevance and conversions? Without that alignment, brands risk competing with themselves instead of winning in search.
Most multi-location content issues arenβt intentional. Theyβre often the result of growth without a clear content framework, or simply too many cooks in the kitchen without overall governance.
Corporate teams are focused on building brand authority and scaling marketing efforts. At the same time, local teams or franchisees want content that answers their customersβ questions and lives on their own site, rather than sending users elsewhere. The assumption is simple: more content equals more visibility.
However, without clear ownership or strategic keyword targeting, overlap becomes inevitable. Similar topics are published across multiple URLs, and over time, this creates internal competition rather than building authority for the entire site.
In general, corporate should own the content that applies to the brand as a whole and build authority at scale. This includes blog content that targets broader informational queries and answers user questions, no matter where users are located.Β
Educational resources, industry insights, and evergreen topics perform best when consolidated in one place rather than duplicated across multiple URLs.
Core service, product, and line-of-business pages should also be centralized. These pages define what the brand offers and typically remain consistent across markets. While location pages can reference and support this foundational content, they often donβt need to be recreated at the local level unless they differ between locations.
Brand-level content, such as company history, leadership, mission, and differentiators, should also sit at the corporate level. These elements reinforce credibility and should be standardized across the organization.
Dig deeper: Local content playbook: From service pages to jobs-to-be-done pages
When it comes to local content, focus on whatβs relevant to that specific market. This includes geo-specific content such as:
On location pages specifically, there are additional opportunities to highlight uniqueness:
These elements can live on a single, well-built location page or expand into a microsite structure (pages living under a subfolder) when it makes sense for the business. Remember, the goal of these pages is to strengthen relevance, target geo-modified and local intent queries, and ultimately drive conversions.Β
One common concern with location pages is duplicate content. The question often becomes, how much duplicate content is acceptable? Instead of focusing on a percentage of unique versus shared content, teams should focus on whatβs most useful for the user.
Typically, content that doesnβt need to be unique across every location includes:
Dig deeper: Local SEO sprints: A 90-day plan for service businesses in 2026
When content production lacks clear governance, it can lead to a range of issues that affect organic visibility and crawl efficiency. Over time, this can cause inconsistent rankings, diluted authority, and missed opportunities to convert traffic into leads.
Keyword cannibalization occurs when multiple pages across a site target the same keywords and search intent. Instead of strengthening rankings, those pages end up competing against each other in search results, and, in some cases, may not get indexed at all.
For multi-location brands, this often happens when individual locations publish similar blog content. For example, a plumbing brand might have multiple location pages with blogs, each posting a blog post titled βTips to fix a leaky faucet,β creating several URLs targeting the same informational query.
A more strategic approach is to consolidate that topic into a single, strong corporate-level post. This would allow the brand to serve as the authoritative source, build backlinks, answer usersβ questions effectively, and strengthen the siteβs overall credibility.
When multiple pages on a website are targeting the same or overlapping keywords, search engines have to determine which one to rank, and sometimes itβs not the page you intended.
On a multi-location site, that may mean a local blog ranks nationally for a topic that would be better suited to live on the corporate site and build broader brand authority. While the page may be relevant to the query, it may not guide users clearly to the next step, leading to customer confusion or bounces.
It may also cause users who arenβt in-market to leave the site after absorbing the information because thereβs no clear next step for them, or because they only see information about services in Austin, Texas, while theyβre located in Cleveland, Ohio.
Instead, consolidating authority on a single, well-ranking page that clearly directs users to take action, whether that means finding their nearest location or submitting a form, would be more beneficial for the brand and users.
Publishing multiple blog posts on the same topic, especially when the answer doesnβt vary by location, can result in duplicate or low-value content. While these pages may be regularly crawled due to internal linking, they often never make it into the index.
At scale, this can become a bigger issue, especially for sites with many locations that publish similar informational topics. For a site with dozens or hundreds of locations, having similar blog posts across those locations can create crawl bloat, where search engines may spend time and resources crawling repetitive or low-impact URLs rather than more high-impact pages.
When similar content exists across multiple URLs, backlinks and internal links are split among pages instead of consolidating authority on a single strong page. Rather than building momentum around a single piece of content, link equity is distributed across competing versions.Β
For multi-location brands, this can weaken overall ranking potential. Consolidating authoritative content at the corporate level allows links, authority, and trust signals to compound, strengthening the entire domain and supporting location pages more effectively.
Dig deeper: The local SEO gatekeeper: How Google defines your entity
After defining roles, move to governance. Multi-location brands need a shared plan for ownership, keyword targeting, and team collaboration.
Before new content gets created, the right questions need to be asked, such as:
Clear keyword mapping and a centralized content calendar can prevent overlap before it starts. When teams understand their roles, content supports overall growth instead of competing internally.
Content collaboration also creates opportunities to strengthen E-E-A-T signals for the site as a whole. Corporate can cover broader educational topics while drawing on real expertise and experience from local teams.
For example, a roofing company might want to write a post about how often homeowners should replace their roofs. The topic is universal. However, the answer could vary by region due to factors such as the material used in that area or the weather.Β
The blog could include quotes from franchise owners or team members across different regions to provide insights into regional factors, such as heat and humidity in the South versus harsh winter weather in the North.
This would allow corporate to own the topic and give locations the opportunity to provide their unique expertise and experiences. Plus, linking to relevant location pages can reinforce context and create stronger internal linking throughout the site.
Another option would be to create a local hub within the blog.
Search may be changing, but many of the fundamentals remain the same. High-quality, well-structured content that genuinely helps users is what earns visibility.
With Googleβs AI Overviews and large language models pulling from authoritative sources, content that clearly answers questions and reflects real expertise is even more valuable. Pages created solely to scale across multiple locations β without adding unique value β are unlikely to perform consistently, and can even hurt a site in the long run.
Content shouldnβt be treated as a volume game. More pages alone wonβt drive growth. What matters is planning, ownership, and alignment.
When corporate and local teams build a shared content strategy, it helps turn content into a growth driver rather than just more pages on a site.
The Visibility Governance Maturity Model (VGMM) is about something most SEO programs lack: clear ownership, documented processes, and decision rights that keep your work from being undone by teams who donβt understand it.
So how do you actually score that?
Each domain uses a bank of governance questions tailored to the business. Theyβre not about how SEO is executed. Theyβre not about tools. And theyβre not an audit.
VGMM questions go to managers and the C-suite β the people who should know about governance but often donβt. Meanwhile, you (the SEO practitioner) actually know whether standards are documented, whether QA is in place, and whether processes exist.
VGMM diagnoses organizations where SEO knowledge lives in practitionersβ heads, rather than in documented, governed processes. If VGMM surveyed only practitioners, it would measure whether you know what to do (you do). But governance maturity measures whether the organization can sustain capability when youβre on vacation, when you get promoted, or when you leave.
Questions go to managers because governance gaps show up as:
When managers canβt answer governance questions, thatβs the signal. It means processes arenβt institutionalized.Β
Dig deeper:Β Why most SEO failures are organizational, not technical
Single point of failure (SPOF) questions can cap your organization at Level 2 maturity until theyβre resolved.
Here are some examples of SPOF question:
Right now, youβre probably the SPOF. Youβre the person who knows where all the bodies are buried, how the redirects work, why that weird canonical setup exists, and what breaks if someone changes X. That feels like job security. Itβs actually a job prison.
When VGMM identifies you as an SPOF:
The organization canβt move past Level 2 until SPOF conditions are cleared. This forces leadership to address hero-dependency.
Each domain model (SEOGMM, CGMM, WPMM, etc.) produces a maturity score based on its own question bank. Hereβs how they roll up:
Each domain asks 30-60 governance questions tailored to that area. Questions are behavior-based, not opinion-based:
Answers are weighted based on impact. Not all governance failures are equal:
If SPOF conditions exist, the domain score maxes out at Level 2 (emerging) even if other governance is strong. You canβt be structured (Level 3) when capability depends on one person.
Domain scores average into the overall VGMM score with adjusted weighting based on:
The overall VGMM score maps to maturity levels:
Domain questions adapt to the maturity model being used.
SEOGMM questions focus on:
LVMM questions focus on:
IVMM questions focus on:
Same governance principles, different operational contexts. An ecommerce company doesnβt need LVMM. A restaurant chain with 500 locations absolutely does.
Dig deeper:Β SEOβs future isnβt content. Itβs governance
VGMM scores are internal quality metrics, not competitive benchmarks. A 62% score doesnβt mean youβre ahead of another organization at 58%. Hereβs why.
Not comparing apples to apples.
The only meaningful comparison is your organization against itself over time:
Use VGMM to answer:
Donβt use VGMM to answer:
As an SEO practitioner, this scoring approach protects you.
When governance assessment reveals gaps, managers are answering questions about organizational capability. Theyβre not evaluating your individual performance. The assessment asks, βDoes the organization have documented standards?β not βIs the SEO person doing a good job?β
When SPOF questions flag that the organization depends entirely on you, leadership sees it as an organizational risk β not as proof youβre valuable. They canβt move to Level 3 until they fix it, which means resources for documentation, training, and knowledge transfer.
When content governance scores low, but SEO governance scores high, it shows other domains arenβt holding up their end. This redirects leadership attention to where governance actually needs strengthening.
When your organization moves from Level 2 to Level 3 over two quarters, you have concrete evidence that governance investments are working. This isnβt βtraffic went up 15%,β itβs βorganizational capability improved measurably.β
Dig deeper:Β SEO execution: Understanding goals, strategy, and planning
VGMMβs scoring approach is designed to:
The assessment focuses on whether the organization can sustain your work without you. Thatβs the difference between being an indispensable hero (exhausting) and being a strategic professional whose expertise is institutionalized (sustainable).
RendrKit is a design API built for AI agents. Your agent sends a JSON request with text and brand colors and receives a professional PNG in under two seconds. Thereβs no need for DALL-E or prompt engineeringβjust 69 deterministic templates that render pixel-perfect images every time.
RendrKit works with LangChain, CrewAI, OpenAI GPT Actions, MCP (Claude/Cursor), and n8n. You can use it via Python SDK, Node.js SDK, or plain REST, and a free tier is included.
Tracium is a developer-first observability layer for AI systems. With a single line of code, it monitors agents and models in real time, tracing every request end-to-end across tools and steps while tracking token spend, latency, and total cost. It captures and classifies errors, supports per-tenant analytics, and lets you compare prompts, models, and routing with live A/B versioning. Use drift detection to spot shifts in inputs and outputs before performance degrades, and manage everything across customers, workspaces, and environments.
As conversational search gains traction, the bigger question isnβt who has more users, but who can monetize them.
Google enters this phase with a massive advantage: mature ad systems, deep advertiser adoption, and decades of optimization. Early AI Mode signals point to a measured rollout.
After a period of panic within the company, Googleβs built-in advantages, coupled with massive capital expenditures, have helped it regain ground on category leader ChatGPT in LLM search.
In December 2025, Googleβs own code red became OpenAIβs code red.
The dust will continue to settle, and analysts have different takes. But one signal stands out: in a major validation, Apple has chosen Google to power its own AI.
It was perhaps premature to assume Google Search would simply lose to ChatGPT on product. That was the consensus at the start of 2025. Google shares fell about 30% from peak to trough before rallying 130%. Today, the company is valued at roughly $3.6 trillion, just behind Apple.
Why did Googleβs recent progress in LLM conversational queries β in the form of AI Overviews and AI Mode β have such a large impact on the companyβs valuation in such a short time?
Ultimately, it comes down to visibility of financial projections. In a company with so much to defend, Googleβs CFO and leadership team needed to determine whether shifts in user behavior β in how search works and how it makes money β would weaken the business model or reinforce it.
Net-net: Google before the shift: huge. Google after the shift: ditto.
Visibility β in the sense of financial planning, not in the SERP β means a great deal to Googleβs advertisers, too.
A large proportion of your annual digital advertising budget is likely allocated to Google. You also still care about how you appear in organic results and increasingly, how your company appears in AI Mode, ChatGPT, Claude, and similar environments.
βIβm fine with 30% less of my business coming in from Google, and figuring out lots of complicated ways to replace it,β β¦ said no advertiser ever.
The competition between monetization models in LLM conversations β especially between the two leaders, ChatGPT and Googleβs AI Mode β will play out differently from the broader race for overall user share. There are several moving parts to keep an eye on:
Right now, OpenAI is at a critical moment because itβs still so early in its monetization. Itβs still testing an inefficient auction model confined to a small group of large advertisers. (Some ads, from their pilot, spotted here.) It may be some time before more mature tools and reporting emerge.
Most recently, OpenAI brought ad platform Criteo (often used for retargeting) on as a partner. The Trade Desk, the worldβs largest non-Google DSP for programmatic, is also in the mix. Some observers have speculated about deeper partnerships or even an acquisition of The Trade Desk, though that seems unlikely.
In any case, outsourcing inventory to programmatic partners is a pragmatic step in OpenAIβs monetization strategy. It also underscores how early the company is in building a scalable ads business.
Despite a broad rollout with partners, OpenAI is stepping back from βcheckout in chatβ integrations after limited adoption from both merchants and consumers. When your primary competitor has a 25-year head start, the learning curve is steep.
So does it make sense now for advertisers to lean into evolving Google user behavior and figure out how to ride the wave?
Expect the transition to more AI Mode sessions β and eventual monetization β to be smoother than initially anticipated. If youβre an advertiser, AI Mode need not equal panic mode.
How do these LLM sessions look to users? Obvious to you and me, but likely less so for many searchers.
Depending on how you search, AI Overviews may appear above other results on the SERP. Thatβs becoming a natural extension of Google Search sessions.
But thatβs not the real conversational layer. The LLM workflow happens in AI Mode. How often users go there remains to be seen.
Itβs improving quickly. Unlike ChatGPT, Google AI Mode downplays how it finds information, whether it is βreasoning,β and which model is being used. The experience feels relatively seamless.
Itβs still early, but ads are already appearing in some cases. The key question is how this evolves, and what advertisers should be paying attention to.
The key areas to watch are:
AI Mode is in a popularity contest and a price war with ChatGPT. Google will likely try to grind down competitors in LLM conversations by monetizing lightly and gradually. Perplexity and Anthropic, for their part, are completely shunning ads.
The result will be less ad volume in this space than you might expect. It may also increase the commercial value of organic visibility in LLM-driven results, leading to renewed focus on content and reputation fundamentals.
Forget ad campaign FOMO, then. It will be interesting to place ads alongside AI-driven sessions, but donβt break the bank. Implement, watch, and learn at your own pace.
Experienced advertisers know there are a few ad formats to consider in any situation like this. The main ones would be: text ads triggered by keywords or similar signals, in a reasonably native format, and feed-based Shopping type ads.
Another way to make money is to allow direct checkout β to take a cut of transactions. As noted above, OpenAI is backtracking on this approach, though not eliminating it entirely. How important it will be for Google merchants (and Google itself) remains to be seen.
Googleβs experience likely allows it, again, to play the long game, study the data, and bring partners and advertisers along for the ride, on an impressive scale.
Recently, Loblaw inked an integration deal with OpenAI. A week later, it made a similar deal with Google.
In terms of execution, weβll want to be on the lookout for which kinds of campaign types in Google Ads make your ads eligible to show in AI Mode.
You can learn everything you want about how ads will show in AI Overviews in Googleβs help files. Unsurprisingly, text and shopping campaigns from Performance Max, standard shopping, and keyword campaigns make your ad eligible to show in AI Overviews.
Google says less about AI Mode in its documentation, for now.
Our agency recently received a Google deck outlining a βShopping Expansionβ beta. Thereβs little mention of AI Mode, though one table, in a subtle way, refers to both AI Overviews and AI Mode.
My expectation is that Google will gradually ease users into AI Mode and test ads sparingly. Even if ads appear in a small share of sessions β say 0.5% β that will still generate significant data and feedback.
Advertiser control will likely be even more limited than it is today. In the world of feed-based ads, you have some levers, but the massive machine learning that controls matching is held by Google and the real-world behavioral ecosystem.
To a lesser extent, thatβs also how keyword matching works. Micromanagers wonβt be too comfortable, but the impact of the ads could still be powerful, especially with data-driven attribution.
Hereβs hoping new signals, new reporting breakouts, and new levers become available to advertisers. Namely: audiences including cool personas; demographics; novel larger buckets around life stages; novel characteristics we havenβt even dreamt of yet, such as their language ability level or aspects of how they interact with the LLM.
The real question is: will reporting be transparent and insightful? We need to at least be able to look at all available metrics for ads that showed in AI Mode specifically. Time will tell.Β
Microsoft seems to be the first out of the gate with AI-conversation-specific reporting breakouts. We expect no less from Google and are impatiently awaiting further guidance on this front β primarily on what kind of reporting will be directly available in the Google Ads interface.
It would be easy for the casual observer to blindly believe that somehow, youβll never be eligible to show up in AI Mode or AI Overviews unless you adopt certain Google Ads campaign types. Thereβs a lot of rhetoric around AI Max.Β
Iβd advise advertisers to do their own research and run their campaigns to suit themselves. Hint: AI Max isnβt the only magical gateway to AI-using users and might not even be a good or appropriate one for many advertisers.
Once reporting is beefed up, youβll want to know how well the AI-specific inventory is doing, however your campaigns wind up serving there.
But that leads us to a wrinkle. Although ads appearing astride AI Mode conversations could certainly be low-funnel (think Shopping ads in high-intent situations), much of the opportunity here is thematic. Your company may now enjoy new opportunities to associate itself with higher-order thinking, new audience definitions, and new intent characteristics.
This opportunity probably comes to your door dressed up as βlower ROAS.β It may be tempting, therefore, to shy away.
Thatβs a mistake.
Why?
Like what happened when everyone started using mobile phones, thatβs where the consumer will be. Ugly early numbers shouldnβt blind us to the imperatives associated with scale.
Midsized to larger advertisers should step back and reimagine how they approach growth and market impact. There are meaningful opportunities for companies to align more closely with their audiences.
This has little to do with AI Max, and everything to do with how LLM-driven research works. Compare how publishers have traditionally assembled consumer personas β often from fragmented behavioral signals β with the much richer context that can emerge from ongoing interactions with an LLM.
A net shift up-funnel could follow. Imagine a world where a significant share of Google search sessions takes place within conversational experiences. Your ads will need to show up there, where appropriate. If that happens, your funnel β and your competitorsβ β will move with it.
Will you be ready?
SoloDeskPad prepares UK sole traders for Making Tax Digital and cuts admin so you can focus on work. It checks your MTD readiness, schedules quarterly HMRC reminders, and keeps records tidy with a mileage and expense logger. It also helps you get paid with automated late-payment chasers and creates UK-law-aware contracts from a short form. Join the waitlist to be ready before the 6 April 2026 deadline.
Google's John Mueller implies that Googlebot crawling 404 pages is a positive sign about that site's content.
The post Google: 404 Crawling Means Google Is Open To More Of Your Content appeared first on Search Engine Journal.
The Optiscaler community has done what AMD couldnβt: bring FSR 4 to RDNA 2 The Optiscaler Community has come together to create an improved version of AMDβs leaked FSR 4 INT8 version. With this new version, FSR 4.0.2b, users of RDNA 2 graphics cards can now use AMDβs improved upscaler with significantly less ghosting. Furthermore, [β¦]
The post Optiscaler delivers improved FSR 4 version for AMD RDNA 2 GPU users appeared first on OC3D.
ValveWhen you reload in CS2, the leftover ammo in your magazine is dumped back into an essentially endless reserve supply. And so the decision to reload has never offered significant trade-offsβin a safe position with enough time, you might reload after firing a single bullet, or half a mag, or after firing down to empty, and the rest of the round would be unaffected. We think the decision to reload should have higher stakes, so in today's update reloading has been redesigned. Now, when you reload, you'll drop the used magazine and discard all of its remaining ammo. Instead of 'topping off' your weapon with a few bullets, a new full magazine will be taken from the reserves whenever you reload.
BabyMealBot lets you send any recipe link or screenshot to WhatsApp and returns a baby-safe version tailored to your child's age and allergies. It extracts ingredients, steps, and tips from TikTok, Instagram, YouTube, and recipe websites, then saves meals to an organized, searchable cookbook. Create grocery lists with one tap and share access with family so everyone stays in sync. Start with three free recipes, then choose simple plans for personal or family useβno app download required.
Hire AI specialists for marketing, sales, support, and more
ExactOnce is an API for creating and consuming single-use actions with atomic guarantees. It resolves concurrent requests deterministically, prevents duplicate side effects, and returns clear failure states like already_used or expired. You can time-bound actions, add optional PIN protection, and get an immutable audit trail. Batch-create actions via CSV for use in magic links, invitations, password resets, secure downloads, approvals, and one-time codes.
Renamer.ai is an AI-powered file renaming tool that looks inside your files to understand what they are, renaming thousands of them in seconds. Drop a folder full of IMG_4382.jpg, Screenshot 2026-02-13 at 10.43.12 AM.png, and Untitled-3-final-v2.pdf files, and get back clean, descriptive filenames without manually touching a single one.
Most batch renamers require you to write rules and patterns. Renamer.ai skips all that; the AI reads the content of each fileβimages, documents, assetsβand generates meaningful filenames on its own. It's available on Windows, Mac, and web, with no naming conventions to memorize, no regex to learn, and no manual sorting ever again.
Nametastic exists because naming a startup shouldn't take longer than building one. Describe your business idea in a few sentences, and the AI generates over 1,000 brandable suggestions - each scored for memorability and pronounceability, each checked against live domain registries across 50+ extensions in real time. The best ideas surface first, and you can see what's available without leaving the page.
Most generators combine random word fragments and hope something sticks. Nametastic actually reads your description, understands the concept, and produces options that fit your industry and tone. Five minutes from idea to a shortlist with available domains. Free, no signup required.
Perplexity's AI browser & assistant
The internet-native payment standard for AI agents
An artist platform using a self-contained Doodles IP LLM
Turn your idea into a real business with AI
Your bank balance in your Mac menu bar
Startup data via API
Realtime meeting notes that donβt leave your Mac
A tiny pixel crab that lives on your Dock
One click accept/reject notifications for Claude Code
Local AI coding assistant for your terminal using Ollama
Agentic AI browser and assistant for mobile by Perplexity
Build AI-powered MCP servers in the cloud
Pixel office for AI agents and multi-agent collaboration
The fast, clean note app Evernote used to be
Offload your test suite to speed up the agent loop
Best Coding Plans with open source models. No lock-in.
Your private, planet-friendly AI assistant from the UK.
Pulls website design tokens into standard W3C DTCG JSON
Your always-on AI agents to monitor the web, now on iOS
Secure MacOS SSH with AI editor, file browser, drag-drop
The Agentic Business Suite that replaces your entire stack
Predict and validate cloud architectures before launch
Your Tesla screen is now a wireless second display
Open source AI calendar scheduler that lives in Gmail
Your Local AI Music Studio for macOS
3D device frame screen recording for macOS
AI that watches your session replays and detects issues
Xiaomi's flagship agentic and omni-modal foundation models
Track AI Agents with a single line of code
The loyalty program for travelers with taste
Start a project with just a prompt on Netlify
AI agents that run your operations (Open source)
Self-evolving AI model powering autonomous agents
Vibe design beautiful production-ready UI in seconds
Google and DocMorris have announced a partnership to create a more intuitive and supportive digital health experience.
NeuralOps is an AI-powered work rhythm intelligence for distributed teams that delivers visibility without micromanagement. It maps focus, collaboration, and recharge patterns, shows real-time activity dashboards and screenshots, and highlights focus time versus meetings and burnout risks. Teams stay in control with transparent tracking, clear indicators, and one-click pauses, with no keystroke logging. Managers coordinate better with team and individual views, while AI nudges help improve workflows across macOS and Windows. Save up to $12K annually compared to similar products.
founder/mode helps founders publish high-performing LinkedIn content in less than 30 minutes a week. Share voice notes, transcripts, and links in a shared knowledge base, then its fine-tuned model drafts posts in your tone while human editors refine every line.
Approve with one click and schedule to post, keep your voice consistent, and turn ideas into conversations with customers, partners, and leads
S2Flow is a Business OS that uses AI agents to run and improve marketing, sales, and e-commerce operations. You set goals and KPIs, and it generates strategies, assigns agents across departments, and executes tasks like ads, content, SEO, email, and reporting while continuously optimizing to hit targets.
You control guardrails via a strategy queue that auto-applies high-confidence actions and flags others for review. S2Flow tracks metrics such as ROAS, pipeline velocity, and average order value, feeds results back into planning, and keeps your growth engine improving without manual orchestration.
Arch Tools provides 61 production-ready AI tools via a single REST API and MCP protocol. These tools include code analysis, web scraping, image generation, NLP, sentiment analysis, crypto data, search, and more. You pay per API call with x402 USDC micropayments on Base, Polygon, Avalanche, and Solana, or you can use traditional API keys. AI agents can discover tools through MCP and pay autonomously without human approval. A free tier offers 100 credits per month, and TypeScript and Python SDKs are included.
Scientio is a knowledge management platform that helps you capture ideas, plan projects, and publish knowledge bases. It uses an AI-first chat interface to create markdown pages and journals compatible with Obsidian and Logseq, while automatically indexing topics and cross-referencing notes. Share read-only online knowledge bases so others can browse your research or documentation without making changes.
The feature enables Shorts viewers to generate their own videos based on a scene from a clip.
LinkedIn is expanding opportunities for advertisers to align promotions with creator content as more B2B marketers work with influencers.
Meta has added Manus, the AI agent platform it recently acquired, into several business elements.
Rather than muting a Reel when you tap, it will now pause the playback.
The program will see creators with large followings on other platforms paid a monthly rate to share the same on Facebook.
Horizon Worlds will become a mobile-only experience, with no VR element.
LinkedIn says that millions of users play its in-app puzzle games every day.
We benchmarked Crimson Desert across 40 GPUs to see how it performs at 1080p, 1440p, and 4K. Here's what to expect from your system - and how well it's optimized for modern hardware.
EthosOne is a governance platform built for independent schools. It centralizes board reporting, state-aligned compliance calendars, and ISO 31000 risk management so leaders can see obligations, owners, and evidence at a glance. Schools document controls, manage duty of care for camps and excursions, and keep every artifact accountable, turning oversight into an active, consistent process across Australia.
