WebAuditFlash delivers a complete website audit that detects invisible barriers holding back your traffic and sales. It analyzes SEO, performance, accessibility, AI compatibility (GEO), and UX, then provides a clear PDF report with a prioritized action plan in minutes.
Choose from three audits: Google Business Profile, SEO Plus, or Site 360, starting from β¬29. The service uses visual AI to identify conversion issues and works with WordPress, Shopify, Prestashop, Wix, Squarespace, and Webflow. No subscription required.
Mockly lets you create realistic fake chat and social media screenshots across WhatsApp, Instagram, Discord, iMessage, LinkedIn, TikTok, Reddit, and more. It lets you build conversations, posts, comments, stories, and even leaked emails with control over names, avatars, timestamps, and appearance, then download share-ready images. It also supports AI chat mockups for ChatGPT, Claude, Gemini, Grok, and Perplexity. Sign up free to unlock more platforms and create polished visuals in minutes.
The technology is designed to reduce strike zone disputes, long the source of baseball's most heated arguments. Under the new system, each team receives two challenges per game and only loses a challenge if it is incorrect. In practice, this incentive has quickly reshaped game-day strategy β and last Saturday's...
Arcway helps students and early professionals master conversations that start their careers. Most people don't struggle in interviews because they're unqualified but because the conversation feels unfamiliar. Arcway closes that gap through structured practice with real-time AI feedback on delivery, clarity, and confidence. By the time the real interview happens, it no longer feels new.
Sora Watermark Remover is an easy-to-use online tool designed to help users remove watermarks from Sora videos quickly and cleanly. Whether you are looking for a Sora watermark remover or want to download Sora videos without watermarks, this platform offers a simple solution for cleaner video results. It is built for creators, marketers, editors, and anyone who wants professional-looking videos without distracting overlays.
Today, because of the expiry of the ePrivacy derogation enabling the use of technology to detect child sexual abuse material (CSAM), Europe risks leaving children acrossβ¦
An ex-Fallout and Skyrim developer says Bethesda's Todd Howard might not be getting the best feedback from his teams, as they're "afraid to say no to Todd."
The two largest Gemma 4 models β 26B Mixture of Experts and 31B Dense β require an 80GB Nvidia H100 GPU to run unquantized in bfloat16 format. Google claims these models deliver "frontier intelligence on personal computers" for students, researchers, and developers, providing advanced reasoning capabilities for IDEs, coding assistants, and agentic workflows.
Intelβs Core Ultra 270K Plus delivers impressive rendering, editing, and AI performance, often outperforming older Intel and similarly priced AMD CPUs.
Tesla's headcount fell from 21,191 workers to 16,506 workers in 2025, according to a report, as it grappled with its second straight year of declining sales.
In addition to Lightcap's new role, OpenAI CMO Kate Rouch will be stepping away from the company to focus on cancer recovery, with a plan to return when her health allows.
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.
About Us Would you like to be part of a fast-growing team that believes no one should have to succumb to viral-mediated cancers? Naveris, a commercial stage, precision oncology diagnostics company with facilities in Boston, MA and Durham, NC, is looking for a Digital Marketing Associate team member to help us advance our mission of [β¦]
Position Summary: The Digital Marketing Specialist leads the evolution of digital marketing, early adoption and integration of AI-enabled marketing at Scot Forge, shaping how the company attracts, engages and converts customers and candidates in a rapidly evolving digital landscape. This person plays a key role in driving demand generation and is responsible for managing our [β¦]
About AllTrailsΒ AllTrails is the worldβs most popular and trusted platform for outdoor exploration. We connect people to the outdoors, help them discover new places, and elevate their experiences on the trail. With the most comprehensive collection of trails in the world, AllTrails supports inclusive access to nature for a global community of millions of [β¦]
Please note that only applicants from the Philippines will be considered for this role. Job Title: Senior SEO Specialist / SEO Specialist Employment Type: Full Time Position Type: Additional Headcount Primary Location: Β Remote Main Purpose of the Role This is a full-time and permanent role for a data-driven SEO professional who will support the Head [β¦]
Why Join CIP? Vacation Time β 15 days full time only Paid Holidays β 13 days full time only;Holiday premium pay for part time only Paid Sick Days and Personal Days accrued Medical, Dental and Vision Insurance Voluntary Benefits: Short and Long-term Disability, Additional Life, Child Life and Spousal Life Dependent Care Flexible Spending Account [β¦]
Company Description Warren Resort Hotels is a collection of properties across some of the Westβs most popular destinations, offering a balance of comfort, quality, and value. Based in Santa Maria, CA, we focus on creating welcoming stays with strong service, well-maintained properties, and thoughtful guest experiences. Each of our hotels has its own character, but [β¦]
Job Description Attention: Kapitus is aware that individuals posing as recruiters may be communicating with job seekers about supposed positions with Kapitus. Kapitus has received reports that the content and method of communication can vary, but messages may contain requests for payment (e.g., fees for equipment or training) and/or for sensitive financial information. Kapitus will [β¦]
SEO & AI Search Consultant At Botify, weβre redefining SEO consulting by combining cutting-edge technology, a culture of innovation, and the brightest minds in the industry. As an SEO Consultant, you will play a pivotal role in solving our customersβ most complex SEO challenges while delivering measurable business outcomes. Youβll work hand-in-hand with industry-leading brands [β¦]
As an SEO Specialist at Corkboard Concepts, you will be responsible for developing and implementing effective search engine optimization (SEO) strategies to increase website traffic and improve organic search rankings. You will identify key SEO growth opportunities and develop plans to capitalize on them. Your role will involve conducting thorough keyword research, optimizing website content [β¦]
Take us to the top! If youβre the kind of marketer who loves the thrill of ranking #1 on Google, thrives on building pipelines that overflow with leads, and gets just as excited about data dashboards as you do about killer creative ideas, keep reading. Docutrend is the leading modern office and workforce technology company [β¦]
Job Description Salary: From 40k basic per year + annual bonus About Visca Web Visca Web is a digital marketing company operating in competitive international markets, with a strong focus on performance-driven acquisition across SEO and paid channels. We work in challenging, high-competition verticals (igaming) where creativity, adaptability, and data-driven decision-making are key to success. [β¦]
Job Description Local or 100% Remote About Point Real Impact, Real People: Our mission at Point is to make homeownership more valuable and accessible. Your work directly helps homeowners access their wealth, achieve financial flexibility, and realize life changing goals. Funding: With over $175M raised from top investors like Andreessen Horowitz, WestCap, Greylock, [β¦]
Description: Balance Health, a national leader in podiatry, is seeking a dynamic and analytical Paid Media Manager to orchestrate paid digital marketing. The Paid Media Manager will be responsible for driving new patient volume across paid channels from Google Ads (Search, Pmax, Demand Gen, etc.) to Meta Ads (Facebook, Instagram) to paid marketplaces (ex. ZocDoc) [β¦]
Job Description Sircle Media is a social media agency based in New York City that works with some of the best brands in the CPG & Beverage verticals. We focus on strategy, execution, content development and paid social media buying to help our clients win online and in-store. Founded in 2012, Sircle has spent the [β¦]
The Paid Search Strategist is a mid-level role within 829βs Paid Advertising department. In this role, you will drive the strategy and development of digital advertising campaigns for various medium-to-large clients. This position is ideal for someone who is proactive, a great communicator, analytical, and willing to go the extra mile to get your work [β¦]
Organic Growth: Build and execute the SEO roadmap across technical, content, and off-page. Own the numbers: traffic, rankings, conversions. No handoffs, no excuses.
AI-Optimized Search (AIO): Define and drive CARE.comβs strategy for visibility in AI-generated results β Google AI Overviews, ChatGPT, Perplexity, and whatever comes next. Optimize entity coverage, content structure, and schema to ensure weβre the answer, not just a result.
Toshiba's M12 Series of 3.5-inch drives uses Shingled Magnetic Recording to achieve storage capacities ranging from 30 to 34TB. The Japanese corporation β one of the world's largest HDD manufacturers alongside Seagate and Western Digital β said the new line of drives is specifically designed for hyperscale customers, cloud service...
Tether AI is a personal AI agent that lives inside Telegram. Message it like a friendβit searches the web, manages your calendar, sends reminders, and runs recurring routines on autopilot. Set up a morning briefing with news and your schedule, and it delivers it every day while you sleep.
Unlike other AI agents that require API keys, self-hosting, or complex setup, Tether works in 2 minutes. Sign in, link Telegram, and you're done. It supports voice, images, and Google Calendar/Gmailβand every action is fully logged so you always know exactly what your agent did and why.
ScanSkinAI is an AI skin scanner that lets you upload a photo to screen moles, rashes, acne, and other concerns, returning results in about 30 seconds. It analyzes images against 31+ skin condition categories and provides a detailed report with risk indicators and recommended next steps. You can add fast dermatologist review and track progress over time with monitoring, guides, and community support. Plans are available for individuals and enterprises, with API and SSO options for clinical integration.
A China-aligned threat actor has set its sights on European government and diplomatic organizations since mid-2025,Β following a two-yearΒ period of minimal targeting in theΒ region.
The campaign has been attributedΒ to TA416, a cluster of activity that overlaps with DarkPeony, RedDelta, Red Lich, SmugX, UNC6384, and VertigoΒ Panda.
"This TA416 activity included multiple
Google is fixing a long-running Search Console bug that inflated impression counts. As the fix rolls out, reported impressions will decrease.
What happened. A logging error caused Google Search Console to over-report impressions starting May 13, 2025. Google today updated its Data anomalies in Search Console page:
βA logging error is preventing Search Console from accurately reporting impressions from May 13, 2025 onward. This issue will be resolved over the next few weeks; as a result, you may notice a decrease in impressions in the Search Console Performance report. Clicks and other metrics were not affected by the error, and this issue affected data logging only.β
A Google spokesperson told Search Engine Land:
βWe identified a reporting error in Search Console that temporarily led to an over-reporting of impressions from May 13, 2025 onward. Bug fixes are being implemented to ensure accurate reporting.β
Whatβs changing. Google is deploying fixes that will change how impressions are recorded and reported. As the rollout continues, youβll likely see a drop in impressions in the Performance report. Clicks and other metrics arenβt affected.
The timeline. The issue began May 13, 2025 and persisted until now. Google said the correction will take several weeks to fully roll out across reporting.
Why we care. If your Google Search Console impressions change in the coming weeks, it will likely be due to this bug fix.
The time places the Mk IV just behind two record-holding hybrid and electric machines β the Porsche 919 Hybrid EVO and Volkswagen's ID.R β highlighting the car's singular achievement in an increasingly electrified field. It is also the fastest lap ever recorded by an American manufacturer at the famed German...
The revelation came from Luke Dicken, who was made Take-Two's Head of AI in early 2025. He wrote in a LinkedIn post that "It's truly disappointing that I have to share with you that my time with T2 β and that of my team β has come to an end."
As hyperscalers seek efficiency and control from custom CPUs they build in house, they adopt Arm and 90% of servers running custom silicon will use the Arm ISA in 2029.
Via LA, the patent pool administrator for H.264/AVC, restructured its streaming license fees earlier this year, replacing a flat $100,000 annual cap with a tiered system.
TendTo is a caregiver coordination platform that centralizes bills, medications, appointments, and documents for aging parents. It provides families with a shared dashboard, role-based access, and a calendar so everyone knows whatβs due and whoβs doing it. Tali, the built-in AI companion, delivers daily briefings, answers care questions, flags drug interactions, and scans pill bottles and insurance documents. TendTo sends reminders, tracks payment status, and stores records securely, replacing spreadsheets and group texts with one command center.
Guide IQ fine-tunes any AI or agent to think and act like a CMO who knows your business. It builds your brand DNA, buyer personas, and marketing strategy, pairs them with 26 expert marketing skills, and packages everything to upload into the AI you already use. Answer 20 strategic questions, export the system files, and get up and running in under 30 minutes. For a one-time $499, you gain CMO-level marketing judgment without hiring an agency or full-time exec, with a 14-day money-back guarantee.
Tick Dog offers features like deep analytics for forms with no subscription fee, charging strictly based on usage. That means you get all the bells and whistles without extra costs.
The newest feature set, Tests, enters the user testing and research area. It uses AI and a complex persona system to navigate your website like a real human, even on mobile.
Ugh Design is a browser-based generative design tool for creating bold visuals. Adjust algorithm and style, structure, scale, spacing, rotation, and regularity to shape compositions, then fine-tune tone, contrast, density response, sharpening, and noise. You can toggle invert and transparent backgrounds to fit any layout.
Use it to explore patterns, textures, and abstract graphics quickly while staying in control of every parameter.
ThreatΒ actors are increasingly using HTTP cookies as a control channel for PHP-based web shells on Linux servers and to achieve remote code execution, according to findings from the Microsoft Defender Security ResearchΒ Team.
"Instead of exposing command execution through URL parameters or request bodies, these web shells rely on threat actor-supplied cookie values to gate execution,
With Apple turning 50 this week, I decided there was only one way to celebrate β melt my brain by playing some of the finest games made for the Apple-1. Here's how I got on.
The Lowe's 2026 SpringFest sale is live, and I'm rounding up all the best deals on backyard essentials, including patio furniture, grills, tools, lawnmowers, and more.
CorelDRAW Graphics Suite 2026 is here, and what does this powerful collection of software bring to the budding artist? Well a lot of AI actually. If you want to see if this new version is right for you, read on
Intel's long-awaited gaming CPU king, the Core 200 "Bartlett Lake-S," is an intriguing CPU generation that isn't actually meant for gaming. The top version, the Core 9 273PQE, is a 12-core, 24-thread processor launched exclusively for commercial and industrial PC OEM markets as an edge/embedded design, and it's not available for the DIY PC crowd. Intel didn't plan for these CPUs to run on commercial motherboard chipsets, and major motherboard manufacturers have confirmed this. However, a modder has achieved something remarkable by booting Intel's "Bartlett Lake" Core 9 273PQE processor into the Windows operating system on a standard Z790 motherboard. Previously, these chips could only reach the POST stage on consumer LGA 1700 boards, but they couldn't run a fully functioning operating system.
Built on the Intel 7 node and designed for Socket LGA1700, "Bartlett Lake" is a non-hybrid chip featuring up to 12 "Raptor Cove" P-cores without any E-core clusters. It was not intended to be compatible with consumer Intel Z790 chipset motherboards. However, main issue was never the socket itself. "Bartlett Lake" shares the same physical LGA 1700 layout as Intel's "Raptor Lake" chips, but that doesn't automatically make it compatible with consumer boards. Typically, the absence of official BIOS and microcode support prevents proper CPU initialization. Earlier attempts usually resulted in startup failures, blank screens, or error codes before progressing further. This recent success changes that narrative. By modifying the motherboard firmware to accept the processor during early boot, the modder overcame the hurdles that had previously caused failures. Once the system accepted the chip at initialization, Windows loaded successfully.
Lightbulb Crew is thrilled to announce that Ex Sanguis, their blood- and strategy-rich game, will release on Steam in Early Access on May 21st! So what's Ex Sanguis all about? You're here to lead elite warriors, called Stillae, to save a dying world from the forces of stasis. And how do you do that? Through many blood-filled turn-based battles, of course!
While engaging in turn-based combat, you'll be able to strategically trigger a chain of events across the battlefield to turn the tide. Use Timeline Effects that can hasten, delay, or swap time positions to impact the turn order in a way that favors you and disadvantages foes.
The new system, known as the Orion Artemis II Optical Communications system, or O2O, will allow astronauts aboard Orion to send 4K footage from their mission to Earth at speeds up to 260 megabits per second, according to NASA. The capability marks a shift from radio-based transmissions that defined the...
If youβre looking for a deal on a gaming PC, it tends to be cheaper these days to buy a prebuilt, and this deal from Newegg delivers a powerful RTX 5070 / 7800X3D for significantly less than it would cost to build the system yourself.
As cloud giants plan to spend $650 billion on AI infrastructure this year, the availability of power infrastructure components has become a significant obstacle to deploying AI data centers.
Two Nvidia GTX 1080 GPUs, a 21-inch screen, and a $10K price tag - the Acer Predator 21X was a ridiculous device 10 years ago, and I fired it up to see how it fares today.
CERT-EU blamed the cybercrime group TeamPCP for the recent hack on the European Commission, and said the notorious ShinyHunters gang was responsible for leaking the stolen data online.
Customer journeys are collapsing into a single moment of evaluation. David Edelman recently described this shift as the convergence of behaviors that used to happen separately.
As decisions compress, brands need to be clearer about what they are trying to solve for the customer. Many organizations are increasing activity instead, without sharpening the underlying strategy.
His central insight is that generative AI has snapped these four behaviors together so tightly that the old model β awareness, then consideration, then purchase, each in its own tidy lane β no longer describes reality. Consumers bounce between platforms, multitask, and shift fluidly between entertainment and intent.
The data point that stopped me cold: people are now asking AI-enabled search engines much longer, richer, more emotionally descriptive queries. Not keywords. Paragraphs. They share context, constraints, preferences, and urgency.Β
The AI then breaks those queries into multiple search streams and synthesizes results in real time. What once required dozens of browser tabs β hours of work β now takes seconds.
Edelman draws two implications from this.Β
The fundamental unit of competition has changed. Brands are now evaluated as solutions to specific situations, not as products within a category.
The familiar demand framework β create demand, capture demand, and convert demand β must be treated as simultaneous, not sequential. You canβt do them in order anymore because the journey doesnβt proceed in order.
Walt Kelly gave us Pogo, the philosophical possum of Okefenokee Swamp, whose most celebrated utterance was the 1970 Earth Day poster declaration: βWe have met the enemy, and he is us.β
Kellyβs most persistent target was not any external villain, but the human tendency to mistake activity for progress. His characters were always busy β scheming, planning, campaigning, reorganizing β and almost never clear on why.
Another line often attributed to him captures it just as well: βHaving lost sight of our objectives, we redoubled our efforts.β
Read Edelmanβs argument through that lens, and the pattern becomes harder to ignore. He describes brands racing to keep up with compressed customer journeys β more content, more specificity, more βanswer audits,β more presence across platforms and formats. The advice is sound.Β
But without clarity about what a brand is actually trying to solve for the customer, more content and more channels are just Pogoβs swamp creatures running faster through the same mud.
The compression trap: When speed substitutes for clarity
Edelman is right that the journey is compressing. But compression can serve two different masters.Β
For brands with crystal-clear positioning β brands that genuinely know what problem they solve and for whom β compression is a gift. It helps a consumer build confidence faster.Β
Warby Parker, which Edelman cites approvingly, is a clean example: its home try-on program, transparent pricing, and frictionless returns all express a single, coherent answer to a specific question: βCan I trust buying glasses without trying them in a store?β Every element of that brand experience is aimed at one objective.
For brands that lack that clarity β brands that have accumulated messaging layers over years of campaign-by-campaign marketing β compression is a disaster. The consumerβs AI-enabled query now synthesizes everything a brand has ever said across every channel, every format, every platform.Β
If those signals are inconsistent, contradictory, or simply incoherent, the synthesized answer will be a muddle. The consumer will move on. In Pogoβs swamp, the creature that runs fastest without knowing where itβs going simply reaches the wrong destination sooner.
Edelman gestures at this when he writes that brand should be understood as βthe sum of signals that make a company recognizable as a solution.βΒ
Heβs right. But Iβd push harder: the compression of the customer journey isnβt primarily a technological problem. Itβs an objectives problem.Β
Most brands canβt clearly articulate, in a single sentence, what specific situation they are the best answer to. If you canβt say it plainly, AI certainly canβt infer it.
Pogo would recognize the funnel debate immediately
One of Edelmanβs shrewder observations is that some of his clients have constructed a βfalse trade-off between brand and performance.β
Marketing departments argue over budget allocations between brand-building and demand generation as though they are fundamentally separate activities. This is, as Kellyβs characters would say, a very impressive argument that completely misses the point.
Kelly spent years satirizing exactly this kind of internal organizational warfare β committees forming to study committees, campaigns launched to counteract the confusion caused by previous campaigns.Β
Organizations are often earnest and busy, and just as often distracted by their own processes. The brand-versus-performance debate is the marketing equivalent of explaining why two teams canβt collaborate because their mandates are structured differently.
In a compressed journey, brand is performance.
The clarity of a brandβs positioning determines whether it surfaces as the right answer to a specific query.
The quality of its content determines whether it captures demand at the moment of confidence.
These are the same thing viewed from two angles.Β
The brands winning in Edelmanβs compressed journey world β Nike, Glossier, IKEA, Warby Parker β donβt appear to be having this argument internally. They have simply decided what problem they solve and built everything around that answer.
The βanswer auditβ is only half of the solution
Edelman recommends something he calls a βrecurring answer auditβ: examine what a consumer would actually encounter across social discovery, video search, retail listings, and AI assistants for their most common customer scenarios. Gaps and inconsistencies, he says, quickly become visible.
This is excellent advice. Itβs also, if Iβm being blunt in the spirit of Kelly, only half the medicine. An audit shows you where your signals are inconsistent. It doesnβt tell you what they should be consistent about.Β
You can audit your way to a perfectly coherent set of messages that still fail to answer any real consumer question, because the messages were never designed around actual consumer situations in the first place.
You need to audit your objectives. What, precisely, is your brand the solution to? Not the product category. Not the feature set. The actual situation.
The specific tension in a personβs life that this brand, and not a competitor, is best positioned to resolve. Until that question is answered with unambiguous clarity, the answer audit is tidying the swamp without draining it.
None of this is meant to diminish what Edelman has written. On the contrary, his framework for thinking about the compressed journey is the most coherent Iβve seen in years.Β
Three of his observations deserve to be tattooed somewhere visible on the forearms, wrists, hands, necks, and behind the ears of every marketing professional.
Thatβs not just a description of a media landscape. Itβs a theory of consumer psychology. Confidence is the triggering condition for a purchase. If youβre optimizing for impressions without asking whether those impressions build confidence, then youβre very busy going nowhere.
Brands must shift from βproduct languageβ to βsolution language.βΒ
This sounds simple and is, in practice, revolutionary. The default mode of most brand organizations is to lead with what they make.Β
Edelman says lead with the situation you resolve. That is a fundamental reorientation of how marketing is conceived and executed.
βAre you the customerβs solution? Will they know it?βΒ
Two questions. The first is a strategy question. The second is an execution question. Most marketing fails by answering the second question without having honestly answered the first.
Kellyβs Pogo ran for 25 years, and the swamp never did drain. The characters were charming, the satire was sharp, and the folly continued because the creatures were incapable of distinguishing between effort and progress. Kelly found that funny.
Marketing history, filled with elaborate, energetic, and expensive campaigns from brands that no longer exist, is less amusing.
Edelman has given us a useful map of the compressed customer journey. Itβs fast, complex, AI-mediated, and it rewards clarity above all else. What he understates β though it runs beneath the surface of his argument β is that compression is also a reckoning.
Brands built on accumulated momentum, legacy awareness, and category inertia will find that a faster journey exposes their vagueness more brutally than a slower one ever did.
The compressed customer journey demands better thinking. And better thinking, as Pogo understood, begins with recognizing that the problem isnβt out there in the swamp. Itβs in here β in the planning meeting, the brand brief, the objectives slide that everyone in the room suspects isnβt quite right, but no one challenges.
With apologies to Pogo, βWe have met the enemy of the compressed customer journey. And itβs our inability to clearly say what we are actually for.β
Over the course of my three-decade career, the keyword drove paid search. Today, itβs one of many signals. Strategy is what determines performance.
Keywords were what you researched for weeks, then built your strategy around based on what you uncovered or hypothesized. You managed everything from bids to matched search terms to negatives and the audiences you targeted. Your career was built and measured by how well you structured around a keyword.
Paid media has always been deeply tactical, with Google driving the majority of search. You were methodical about placements, audiences, bids, headlines, extensions, and keyword-stuffed URLs.
This model worked. It gave practitioners the control they needed to get results.
You could see which search queries triggered ads and what they cost. If there was value, you expanded or doubled down. You might over-segment ad groups by theme or build campaigns around keyword audiences, then layer in modifiers and match types to drive 1200% ROAS.
What changed across platforms
Advertising has converged on a single structural shift: AI, or more precisely, automation built into the platforms. These systems now handle targeting, bids, and creative assembly that practitioners used to manage manually.
The keyword hasnβt disappeared. Itβs moved from the primary optimization lever to one signal among many that platforms use to deliver ads based on user behavior and the auction.
On Google, AI Max for Search is the clearest example. Itβs not a new campaign type. Itβs an optimization layer, similar to Smart Bidding, that changes how keywords function inside a search campaign. Googleβs AI uses your existing keywords, copy, and landing pages, including H1s and H2s, as signals rather than instructions to find and serve ads.
Google reports that advertisers using AI Max see 14% more conversions at a similar CPA or ROAS, with campaigns using exact and phrase match seeing lifts of up to 27%. Pair it with Performance Max across Search, Shopping, YouTube, Display, Discover, Gmail, and Maps, or Demand Gen for upper-funnel awareness, and the system expands further.
When I say strategy is the new keyword, Iβm not speaking in abstractions. Iβm saying there are specific inputs that now determine where your ads show up, who sees them, and whether they convert. These inputs have largely replaced the keyword list in paid media as the highest-leverage control.
The distinction matters. Strategy dictates the activity needed to achieve your goal and vision. Tactics are the execution. Whatβs shifted is that platforms now handle the tactics, and our job is to define the strategy that guides them.
Conversion data quality, including server-side tracking, has become the most important input in any account. Googleβs Smart Bidding and other platform optimization systems depend on conversion or event signals to learn and improve.
You can prioritize from all to one, which conversions matter more, whether itβs a lead from a high-value market versus a newsletter sign-up, or a new customer versus a returning one. These distinctions used to be handled through keyword segmentation and bid modifiers. Now, in a small way, theyβre handled through strategic conversation, where value is assigned or determined at that point.
First-party data, customer lists, CRM data, website behavior, and offline imports have become the equivalent of keyword research. The richer and cleaner the data you feed these systems, the better they perform. Itβs less about search volume and more about understanding your own customer data, making sure itβs structured properly, and connected to the platforms you advertise in.
Creative is a beast. Itβs moving from a production deliverable to a strategic signal.
For Demand Gen, Display, and Meta, your creative, functionally speaking, is your targeting. Platforms read your images, video, and copy to determine who sees your ads. Google AI Max generates headline and description variations based on your landing page content, your H1s, H2s, and so on.
The strategic questions, what themes resonate with which segments, what visual approaches drive action at different funnel stages, and what messaging frameworks allow AI to generate variations, now carry the weight the keyword used to.
Landing page and website quality have become paid media inputs, not just a thing for UX or CRO. AI Max reads your page to determine what queries to match and which headlines to generate. Final URL expansion in AI Max and Performance Max sends users to the page AI deems most relevant. Poor post-click experiences, thin content, and slow load times can tie back to lower conversion rates.
All of this limits AIβs ability to serve your ads.
The most valuable work is no longer managing keyword lists or adjusting manual bids. I have strong opinions on that, but Iβll ask you, what else could you be doing with your time, instead of manually adjusting bids for thousands of keywords?
Itβs the strategic framework that AI systems operate within: ensuring data quality, defining creative strategy, building measurement into your teams, and knowing when the LLM is wrong and you, as an SME, need to adjust course.
The job of subject-matter experts is to guide the machines. That guidance takes the form of conversion architecture, audience signal quality, creative frameworks, and brand guardrails, rather than keyword lists and bid sheets.
This means investing time in understanding how:
These systems work.
Platforms learn.
LLMs prioritize.
Itβs the pros and cons we choose to emphasize β the signals we prioritize. It means building robust first-party data, developing frameworks across audiences, creative, and UX, and feeding that into AI to enhance. It means accepting that the keyword era is giving way to something fundamentally different.
The practitioners who treat strategy as their primary lever, who invest their energy in architecture and design rather than lever-pulling, will be best positioned as this shift continues.
The keyword list isnβt gone. Itβs no longer the center of the work. Strategy is.
Intel is reportedly preparing yet another CPU price hike, adding to the previous increases implemented in February and March. According to the latest research and channel checks from Chinese market research firm Minutes Logic Society, Intel plans to add another price increase to its existing ones. In February, Intel implemented a first round of CPU price increases ranging from 10% to 15%, depending on the segment and SKU. Just a month later, the company introduced another increase in March, around 15%, with earlier reports suggesting a 10% hike for the consumer CPU sector, like the Core Ultra family of CPUs. This time, we are expecting another increase in May across the overall CPU portfolio, meaning that Intel will again raise prices by a few more percent, depending on the CPU sector, whether it is a Core Ultra CPU or a Xeon server processor.
The total cumulative goal for the price hike is about 30% higher compared to 2025 pricing. Interestingly, Intel is facing a significant problem with CPU supply that it can't address immediately. While a large portion of CPU production is internal, with Intel Foundry handling a bulk of orders, some CPUs require TSMC's silicon for Intel to ship these CPUs. Especially with multi-die packaging, where some parts are manufactured on Intel's node and others on TSMC's node, shipping is impossible until every part arrives and Intel assembles it with its advanced packaging.
GMKtec has launched the NucBox K17, a compact mini PC featuring an Intel Core Ultra 5 226V processor, a Lunar Lake chip built on TSMC 3 nm process with 8 cores, a 2.1 GHz base clock, and up to 4.5 GHz boost on the performance cores. Total AI compute comes in at 97 TOPS across the CPU, Arc 130V iGPU, and dedicated NPU, with the NPU alone rated at 40 TOPS. The Intel Arc 130V handles graphics, clocking up to 1.85 GHz and supporting ray tracing and XeSS upscaling. Triple display output is available via two HDMI ports and a USB4 port, with support for resolutions up to 8K. That same USB4 port runs at 40 Gbps and supports eGPU connectivity, Power Delivery, and display output simultaneously.
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Rowhammer attacks exploit vulnerabilities in DRAM hardware by causing targeted bit-flips, allowing attackers to bypass memory isolation and gain control over a device. These attacks were initially focused on CPUs and their associated DDR memory, such as DDR4. However, recent research indicates that NVIDIA GPUs are also vulnerable due to the fragile nature of the GDDR6 memory they use, which directly compromises the CPU host. Two independent research teams have discovered ways to exploit this decades-old memory vulnerability against modern graphics hardware, with trouble extending beyond the GPU itself. The "GDDRHammer" and "GeForge" groups have each developed functional exploits that use Rowhammer bit-flips in NVIDIA GPUs with GDDR6 memory to gain complete control over the host CPU's memory. This attack can perform bit-flips on some NVIDIA GPU models, ranging from the "Ampere" to "Ada Lovelace" families of cards.
An attacker who succeeds in this can read and write anything stored in the machine's main memory. Both teams have also introduced new Rowhammer techniques specifically designed for GPU architecture, achieving a significantly higher rate of bit-flips on GDDR6 memory than previous methods. The critical step in both exploit chains involves targeting the GPU's memory allocator, using controlled bit-flips to corrupt the GPU's page tables. Once these page tables are compromised, the attacker gains arbitrary read and write access to CPU memory, breaking down the security boundary between the graphics subsystem and the rest of the machine. The end result is a full system compromise, as the attacker can manipulate memory at will and gain root access, achieving total control without interacting with privileged software paths. The affected GPUs include the GeForce RTX 3060, which experienced 1,171 bit-flips, and the RTX 6000 "Ada" GPU, which saw 202 bit-flips from the attack.
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Paid search is often the highest-leverage ecommerce growth channel, delivering strong conversion rates and efficient spend when structured effectively.
Google Shopping and Amazon Ads capture high-intent demand while generating the data needed to scale it. These platforms connect search queries directly to revenue, enabling you to identify which terms drive sales and allocate budget accordingly.
The real challenge is organizing campaigns to act on that signal.
Why paid search works so well for ecommerce
Paid search performs differently from other channels because it combines two advantages: intent and data.
Intent: Google and Amazon are search-driven environments. When someone searches for a product, theyβre signaling exactly what they want. Thereβs no inference required, no audience modeling, and no interrupting someone mid-scroll. Youβre providing the answer to a question the customer is already asking.
Data: Both Google Shopping and Amazon Ads provide keyword-level revenue data that most other advertising platforms canβt. You can see which search terms generated sales, at what conversion rate, and at what cost. Amazon goes further, offering clearer and more direct revenue visibility at the product and category level.
Together, these create a powerful feedback loop. Search terms tied to revenue let you shift spend toward higher-converting queries, improving ROAS over time. On Amazon, this loop extends furtherβstronger conversion rates can improve organic rankings, lowering future acquisition costs.
Success in search campaigns depends on building multi-funnel structures. The concept is consistent across platforms, but implementation varies by campaign types, settings, and bidding strategies.
The architectures outlined below use wide-net, low-cost discovery campaigns to map the full search landscape, then funnel high-intent, proven converters into dedicated performance campaigns with appropriate bids. The result: stronger ROAS, improved rankings, and more scalable growth.
The priority sculpting method is based on Martin Roettgerdingβs approach, with adaptations over the years. It uses a three-layer campaign structure to route keywords into different campaigns based on performance.
This lets you control spend on discovery keywords and maximize investment in high-performing, high-intent terms. The key is Google Shopping priority settings β βhigh-priorityβ campaigns serve first at lower bids.
Layer 1: Brand
The goal is to capture branded search traffic.
This layer uses a Performance Max campaign and can also use standard Shopping.
It remains assetless to keep it focused on Shopping inventory and prevent bleed into Display and YouTube.
Itβs set with a high ROAS target, as PMax tends to go after brand traffic naturally, especially when set with a high target ROAS.
Alpha terms are negatived in this campaign, as they may also have high ROAS.
Layer 2: Catch-all
The goal is to cast a wide net, test search terms cheaply, and generate conversion data.
This layer uses standard Shopping with a high-priority setting to catch non-branded traffic.
Bids are kept low to control costs.
Brand terms and alpha terms are negatived using a negative list.
Over time, low-performing terms are also negatived once theyβve been tested and failed.
Layer 3: Alpha
The goal is to dedicate budget to best-performing terms and generate strong ROAS.
This layer uses standard Shopping with a low-priority setting and high-ROAS bidding settings.
By negating converted terms, or alpha terms, in the catch-all campaign, those queries fall through to this campaign, where you bid aggressively on whatβs already working.
The key considerations in this structure include the following:
Routing logic using negatives
The system relies on routing logic: Googleβs priority settings determine which campaign serves a query first. Negative keywords in the catch-all push proven converters into the alpha, where bids are higher and budget is protected. At the same time, non-alpha terms run through high-priority campaigns at the lowest possible bids.
The method lives or dies on weekly search term negation. Two actions are done regularly:
Negate non-converting terms in the catch-all. A good rule of thumb is over 20 clicks and zero conversions, these terms are negated. Weβve tested them, and removing them frees up the budget for other search terms. Note that this requires consideration before negating. If a keyword is highly relevant, you might want to let it run longer.
Negate converted terms (alphas) from the catch-all so they fall through to the alpha campaign. Over time, the alpha accumulates a curated list of proven terms bid on aggressively, while the catch-all keeps finding new ones cheaply. Itβs a compounding system.
Shared budgets
Shared budgets are critical. Layers 2 and 3 should work on a shared budget.
The system works only if they run together, because each query needs to be sculpted through the system. It wonβt work with separate budgets because if the budget on the catch-all high priority runs out, then the alpha would be the first contact, and the query would likely show on the alpha (at a higher bid), even though itβs not an alpha.
SKU separation
The system is designed to run across a unique set of SKUs. All three layers should target the same set of SKUs. Itβs recommended to start with all SKUs to begin with and then build out from there.
Products that get buried in the main campaigns or operate at a different margin tier can be peeled off into their own mirrored catch-all/alpha pair, ring-fencing their budget. Only do this when thereβs a clear reason. More campaigns mean more overhead and more fragmented data.
Feed quality
Itβs important to optimize the feed, as Google heavily relies on titles mainly for understanding the context of the product and which keywords to serve it.
Amazonβs campaign structure is more advanced than Google Ads and offers several advantages.
Amazon typically delivers higher conversion rates and more conversion data. Ad spend also drives both conversion rates and rankings, with a clear, measurable link between ad spend and organic ranking.
Ads drive traffic, traffic drives conversions, and conversion rate drives organic rank. That makes Amazon Ads an investment in organic search.
Google Ads campaigns run across the whole catalog. On Amazon, you build campaigns at the SKU level, typically one SKU per campaign.
The structure uses three campaign tiers: research, ranking, and performance. Each has a distinct goal and is managed by adjusting advertising cost of sale (ACOS) targets to reflect different profitability goals.
Tier 1: ResearchΒ
Campaigns use broad and phrase match keywords, along with automatic targeting.
The goal is to cast a wide net and generate keyword ideas and variations.
ACOS tolerance is relatively high, since the goal is data, not profit.
Tier 2: Performance
Campaigns use exact match keyword targeting.
The goal is profit, with a competitive ACOS target below break-even.
Move proven converters from the research tier into exact match campaigns. Run your best keywords at efficient bids to maximize returns on whatβs already working. This mirrors the alpha campaign in Google Ads.
Tier 3: Ranking or exposure
Use single-keyword campaigns (SKCs) with exact matchβone keyword per campaign.
The goal is usually ranking, though it can shift over time.
For ranking, set aggressive bids with high ACOS tolerance (often 50%+). Push volume through high-value keywords to drive top organic positions. Once you reach positions 1β3 organically, pause those keywords.
Ranking campaigns are debated. If youβre already ranking, thereβs no need to pay for visibility you get for free.
This layer doesnβt exist in Google Ads, where ad spend doesnβt influence rankings.
With Amazon Ads, we bid toward an ACOS target. ACOS is the advertising spend as a percentage of revenue. Because Amazon data is so clean and conversion rates are high, we can calculate our bids to drive a certain ACOS.
The ACOS-based bidding formula:Β
Target bid = (Revenue per click) x Target ACOS
Implementing ACOS bidding can be automated using software like Scale Insights. Different campaign tiers can be assigned different ACOS targets, and CPCs can be adjusted daily by the software.
Keyword routing
Similar to Google Ads, keywords are funneled through from research campaigns into performance or alpha campaigns. This can be done manually or automatically with Scale Insights using an import rule.Β
The concept is very similar in that keywords that shine get imported down the funnel, while non-performing keywords are phased out through testing.
The conversion rate signal
If a productβs conversion rate is below the market average on a given keyword, more spend will not likely improve its rank. Amazon usually surfaces the better-converting product.Β
The correct response is to fix the underlying issue: price, listing quality, imagery, or the product itself. Most advertisers skip this step and keep spending into a hole.
The ranking cannibalization rule
There are two strong views on ranking and cannibalization. Some argue that once your product ranks highly for a keyword on Amazon, you should reduce or stop ad spend. If youβre ranking organically, you can save on ads.
On the other hand, if a keyword performs well with strong ROAS, having two listings can outperform one. It increases your chances of a click. Ads also typically appear above organic listings, giving you higher placement.
Whichever view you take, the three-tier method lets you drive rankings through SKCs, then reduce or stop ad spend once you rank, if you choose.
How Google Shopping and Amazon Ads compare for ecommerce
The underlying logic for advanced campaign setup is the same across Google Shopping and Amazon Ads, with key differences beyond the core structure.
Google Shopping (Priority sculpting)
Amazon Ads (Multi-tier architecture)
Similarities
β Route queries to campaigns via priority and negatives. β Discover converting terms in a catch-all at a low cost. β Graduate proven terms to alpha with high tROAS. β Regular search term reviews, negatives, and alphas.
β Route keywords across research β ranking β performance. β Discover new keywords in broad, phrase, and auto campaigns. β Graduate proven terms to exact match for profitability. β Regular search term reviews, negatives, and imports to lower funnel.
Differences
β Run across the whole feed, separate high-margin products for ring-fenced budgets. β ROAS-based bidding. β Product feed determines search term targeting, and the advertiser is unable to select.
β Campaigns built at the SKU level rather than across the whole catalog. β ACOS-based bidding. β Search terms selected by advertiser. β Ads drive rankings, and you can save budget by monitoring organic rankings.
Which platform is right for your ecommerce strategy
Like all good answers, it depends heavily on your business and your goals. Both have advantages and disadvantages. We can say that:
Amazon Ads often perform better, delivering higher conversion rates and faster ranking and sales when intent is strong.
Google Ads is better for long-term brand building. It offers broader reach, potentially lower costs, and drives traffic to your own website, where you retain customer data.
The ideal is to run these together. Many brands may launch on Amazon and grow over to their own platforms and utilize Google Ads.
Paid search for ecommerce is probably the most effective advertising avenue you can explore. Both platforms offer significant opportunities when implemented properly. Each platform has pros and cons, and I would recommend further exploring the details in these campaign structures and deciding on the right implementation for your business.
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It used to be that Google searches opened up a world of questions. You searched, sifted through links, and came to your own conclusion.
Today, AI Overviews, ChatGPT, Perplexity, and other AI platforms compress multiple sources into a single, synthesized response. In the process, nuance is flattened, and certain viewpoints can be overrepresented.
This marks a fundamental shift in online reputation management. Search engines now shape the information they surface. The result is a rise in zero-click behavior, where users accept AI-generated answers without visiting underlying sources.
For brands, that changes the stakes. Visibility no longer guarantees influence. Even a No. 1 ranking can be bypassed if the narrative tells a different story.
AI narrative formation: How AI systems deliver users their answers
AI search engines now follow a new pattern for delivering answers. For the sake of this article, weβll call it AI narrative formation. Hereβs how it works.
Source pooling
AI systems pull from a wide range of sources. While you might expect trusted, peer-reviewed content, they often draw from Reddit, YouTube, review platforms, complaint forums, and social media sites like Instagram and TikTok.
Signal weightingΒ
Not all sources carry equal weight. A single trusted source can be outweighed by a large volume of lower-quality content. For example, a highly active Reddit thread filled with negative reviews may outperform a fact-checked source like Wikipedia.
Narrative compression
AI condenses dozens of inputs into a short, digestible summary. In the process, nuance is lost, and fringe cases can become dominant themes. A complex reputation may be reduced to: βUsers say this company is not trustworthy.β
Continued reinforcement
These summaries donβt stay contained. Theyβre screenshotted, shared, and repeated across platforms. Those repetitions become new inputs, reinforcing the same narrative in future AI outputs.
How a finance companyβs solid reputation unraveled in AI search
To see how AI narrative formation works in action, letβs look at a use case.
My company recently worked with a finance organization to repair its online reputation. For this example, weβll call it Company X.
Problems emerged for Company X with the rise of Google AI Overview. Previously, under traditional SERPs, Company X had a solid reputation. Users searching Google for reviews would find a 4.2 rating on Trustpilot, a strong company website with employee bios, and numerous positive blog reviews from trusted sources.
Google AI Overview changed that. How? By resurfacing an old Reddit forum centered on negative complaints about Company X.
When users asked Google, βWhat are opinions like about Company X?β AI Overview delivered a clear answer: βCompany X has mixed reviews, with specific complaints regarding customer service.β But those customer service issues were resolved nearly a decade ago.
AI Overview pulled multiple reviews from that Reddit thread, combined them with strong negative phrasing, and factored in the lack of structured positive content to form a semi-negative impression. A new perception of Company X was created.
We can dig deeper into how AI impacts reputational risk. Consider the following:
How negative AI narratives spread: In traditional search, users had to dig for negative results. With LLMs, those results can surface instantly, even when theyβre defamatory or incorrect.
Hallucinations and misinformation: Most users are now aware of AI hallucinations, but they arenβt always easy to spot. Making matters worse, LLMs can present incorrect claims or factual inconsistencies with confidence.
The snowball effect: As discussed in narrative reinforcement, AI-generated answers get screenshotted, shared, and repeated across platforms. That repetition builds momentum, creating challenges ORM firms now have to manage.
A hard truth has emerged in ORM: The most accurate claim doesnβt rise to the top. The most repeated claim does.
A step-by-step guide to auditing AI-generated narrative formation
Letβs walk through another case to see how an AI-generated narrative can be audited.
CEO X is the founder of a SaaS company. He has an ongoing thought leadership presence and a strong reputation in his industry.
On a recent podcast appearance, one quote was taken out of context and aggregated across several platforms. The quote was framed as an opinion rather than a fact. Blog posts were written, and Instagram Live reactions spread online.
In no time, ChatGPT and Google AI Overview turned CEO X into a controversial figure.
Hereβs a step-by-step guide to approaching that reputation management crisis.
Step 1: Mapping queries
We begin by identifying what search engines are saying about CEO X. We ask ChatGPT and Google AI Overview questions such as βWhat did CEO X say?β and βWhat is CEO Xβs current reputation?β This helps us analyze the issues.
Step 2: Capturing outputs
We identify the claims associated with CEO X. Google AI Overview and ChatGPT describe CEO X as a controversial figure who recently made comments in poor taste. The narrative formed across both platforms is trending negative.
Step 3: Delving through sources
Next, we analyze the sources AI Overviews and ChatGPT rely on. We look for whether theyβre outdated, repetitive, or low quality. (In the case of Company X, the latter two apply.)
Step 4: Analyzing the narrative gap
We identify the gap between AIβs narrative and reality.Β
What are CEO Xβs actual views?Β
What was the context of the quote?Β
And what has their reputation been up to this point?
Step 5: Correcting and replacing sources
The final step is to replace or respond to those negative sources. Claims can be addressed directly on Reddit, Instagram, or other platforms spreading the narrative. Structured explanations should also be published through FAQs and policies, while strengthening third-party validation.
Focusing solely on SEO rankings is no longer enough. We need to think in terms of narrative shifts and framing. That also means thinking in terms of inputs and outputs.Β
Users arenβt evaluating individual pages. Theyβre engaging with AI-generated answers. Rather than managing what users find, we need to manage the answers AI systems deliver. That means strengthening what those systems rely on:
Publishing high-quality first-party content.
Earning credible third-party mentions.
Reinforcing positive customer reviews.
Addressing misinformation directly.
Improving structured data.
Maintaining accurate Wikipedia or Wikidata entries where applicable.
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"The role of legal and people leadership has never been more critical as Intel drives cultural transformation with discipline, speed, and integrity," said Intel CEO Lip-Bu Tan. "Aparna brings a rare combination of operational rigor, business judgment, and people-first leadership. Her experience helping scale global technology companies through periods of significant change will be invaluable as we build a stronger, more agile Intel."
Hardware Hunter monitors Reddit, eBay, Slickdeals, and more to surface used computer hardware deals worth buying. It scores each listing for scam signals, vague specs, condition red flags, and price versus market, then delivers concise email or Telegram alerts with a verdict and link. Define what you're huntingβcategory, specs, sources, and price rangeβand it checks every two hours so you only see real opportunities.
Skedly is the reliability layer between your event sources and your application. It receives, filters, retries, and replays webhooks so you never miss a critical event, with real-time observability into payloads, headers, status codes, latency, and delivery history. HMAC signature verification, workspace isolation, and configurable alerts help you secure and monitor traffic.
Use the open-source CLI to test with real production events on localhost, and embed a customer-facing webhook dashboard in your app to let users manage endpoints, filters, and retries without building infrastructure.
AΒ large-scale credential harvesting operationΒ has beenΒ observed exploiting the React2Shell vulnerability as an initial infection vector to steal database credentials, SSH private keys, Amazon Web Services (AWS) secrets, shell command history, Stripe API keys, and GitHub tokens atΒ scale.
CiscoΒ Talos has attributed the operation to a threat cluster it tracksΒ as
SPOILERS: World of Warcraft's latest expansion is already proving controversial for re-treading some of the game's most hated lore. But is Blizzard trying to fix it, or will they end up making it worse?
Tests conducted by Hardware Canucks show that the XPS 14 delivers exceptional battery life, lasting more than 43 hours on a single charge while browsing the web with variable refresh rate enabled. During the test, the screen refresh rate was reduced to 1Hz. The laptop tested features an Intel Core...
Leenheer is best known for creating HTML5test.com, the WhichBrowser user-agent parser. He began exploring a CSS-based Doom renderer after completing an earlier experiment that ran the game on an oscilloscope, reusing code to extract map data from the original WAD file.
Mitsui O.S.K. Lines (MOL) and Hitachi have signed a memorandum of understanding to build and operate floating data centers hosted on repurposed ships. The two Japanese corporations aim to develop and commercialize a novel approach to the data center business, believing that a seafaring solution could significantly improve convenience and...
Preplo turns cooking videos from YouTube, TikTok, and Instagram into clear, actionable recipes. Paste a link and its AI parses transcripts or descriptions to extract ingredients, quantities, cost estimates, and timed steps you can follow in full-screen cook mode. Generate smart shopping lists, track a weekly cooking streak, and adapt recipes to your needs. Start free with limited extractions or upgrade for unlimited extractions, advanced features, and priority support. Available on iOS, with Android coming soon.
REAIGENT7 gives real estate agents their own listing platform with AI that answers buyer questions 24/7, captures leads, and routes every inquiry directly to your REAIGENT7 dashboard or your CRM. Build professional property pages in minutes, share anywhere, and keep your brand front and center.
Manage leads in a simple dashboard, move them through stages, and get follow-up reminders. Generate listing descriptions and social posts with one click, track showings and open house sign-ins, and grow your pipeline without portals or referral fees.
Dellβs supercapacitor-powered keyboard and mouse deliver ultra-fast charging and long usage, redefining convenience and eliminating reliance on traditional batteries.
Flipboard's social websites consolidate profiles and posts from Bluesky, Mastodon, Threads, YouTube, podcasts, blogs, and RSS feeds into a single, shared destination.
After 36 years of leading Call of Duty developer Raven Software, studio head Brian Raffel is leaving the company to "step into a well-earned retirement."
Canonical increased the recommended system memory for the upcoming Ubuntu 26.04 LTS "Resolute Raccoon" to 6 GB of RAM, a first major change since 2018. According to the release note, the 26.04 LTS now lists 6 GB of RAM as the baseline for a comfortable desktop experience, alongside a 2 GHz dual-core CPU and 25 GB of storage, unchanged from previous generations. This represents a 50% increase over Ubuntu 18.04 LTS, which in 2018 raised the bar to 4 GB, and a notable shift from earlier releases that ran on as little as 1 GB. The change is not caused by a heavier core OS. Instead, it reflects the reality of modern workloads. The GNOME desktop, now updated to newer revisions, along with current web browsers (i.e., Firefox) and everyday apps like LibreOffice, demand more memory in multitasking scenarios.
Importantly, 6 GB is not a hard requirement. Ubuntu 26.04 LTS will still install on systems with less than 6 GB of RAM. However, performance may suffer. Early testing shows that the OS remains functional even on 2 GB systems although with significant slowdowns. As before, the 25 GB storage requirement remains mandatory for the desktop edition. As Ubuntu 26.04 LTS is expected to be the next long-term support release from Canonical, the Ubuntu ecosystem provides many options for those using lower-end hardware. Lighter flavors such as the official Lubuntu or the Linux Lite distro, and manual installations with a minimal base remain available and viable options. Also, Ubuntu Server can be deployed on systems with around 1-1.5 GB of RAM, depending on the use case. Ubuntu 26.04 LTS is currently in development and scheduled to be released on April 23.
Valve's next-generation Steam Deck 2 handheld console is reportedly planned for release in 2028, with significant manufacturing changes expected for this sequel to the highly successful handheld gaming device. According to a well-known industry leaker, KeplerL2, posting in the NeoGAF community, Valve is targeting a 2028 refresh for the second-generation Steam Deck. However, the ongoing supply chain shortages of DRAM and NAND Flash could cause disruptions to these plans, potentially leading to delays. Interestingly, this period is when the shortages are expected to start easing, so the Steam Deck 2 could still be released on time, depending on Valve's sourcing capabilities.
One of the most significant procurement shifts for the Steam Deck 2 is Valve's choice of the computing base that will power the handheld. Instead of using a semi-custom AMD APU, Valve is expected to use an off-the-shelf AMD APU that won't require any custom tuning from AMD to meet Valve's needs. This is welcome news, as the latest Steam Machine showed that Valve's reliance on a semi-custom APU solution made the hardware "obsolete" quickly while the rest of the industry advanced. With any semi-custom solution, stockpiling silicon and waiting for DRAM/NAND modules to arrive puts pressure on Valve to ship a product that is significantly underpowered or too expensive. However, with an off-the-shelf solution, Valve could use the best available option at the time of shipping and optimize SteamOS around it.
Imagine you're a web hosting vendor leasing out specific number of CPU cores of a large core-count processor. You'd want to specify QoS limits on the shared L3 cache performance for those cores, so they don't hamper performance of other tenants. AMD this week released a technical document detailing the Platform Quality of Service (PQOS) ISA extensions for its next-generation Zen 6 microarchitecture. These ISA enhancements provide sysadmins and cloud providers with greater control over processor and memory subsystem performance. The latest document outlines three primary additions to the Zen 6 PQOS feature set, Global Bandwidth Enforcement (GLBE), Global Slow Bandwidth Enforcement (GLSBE), and Privilege-Level Zero Association (PLZA). These features are designed to scale performance management across complex multicore environments by allowing software to regulate bandwidth and execution privileges more effectively across expansive groups of logical processors. The development shows that AMD is steering toward a more closely collaborative hardware QoS solution for its multicore processors.
A highlight of the Zen 6 PQOS updates is the implementation of Global Bandwidth Enforcement (GLBE), which allows system software to specify L3 external bandwidth limits for groups of cores that span across multiple traditional QoS Domains. By grouping these into a unified "GLBE Control Domain," AMD enables a competitively shared bandwidth ceiling for specific Classes of Service (CoS). This upgrades older architectures that only provided L3 external bandwidth enforcement on a strictly per-domain granularity. Next up, AMD introduced Global Slow Bandwidth Enforcement (GLSBE), a parallel feature that applies the exact same multi-domain bandwidth limiting principles to system memory explicitly designated as "Slow Memory." Both GLBE and GLSBE provide granular controls via specific model-specific registers.
On your Windows PC, the Unified Extensible Firmware Interface (UEFI) uses Secure Boot certificates to ensure that only trusted software initiates the startup sequence. The certificates currently in use were originally issued in 2011 and are set to expire in late June 2026. To address this, Microsoft has been quietly rolling out updated certificates through Windows Update. Starting in April 2026, users can check their device's status via a new indicator in the Windows Security app. By navigating to Device security and then Secure Boot, a color-coded badge will show whether your device is fully updated, awaiting an update, or requires immediate attention.
The badge system is simple yet significant. A green checkmark indicates that the new certificates are installed and no further action is needed. A yellow caution badge, which will start appearing in May 2026, means the update is either pending or has been blocked by a hardware or firmware limitation. A red stop icon is the most serious state and could appear as early as June 2026, once older certificates start expiring. When this occurs, the device will no longer be able to receive critical boot-level security updates. The same status is reflected in the Windows Security system tray icon, so warnings are visible even when the app is closed.
Researchers at USC's Viterbi School of Engineering have demonstrated an electronic memory device that functions far beyond previously known thermal limits. In experiments, the device remained stable at 700 degrees Celsius β hotter than molten lava β without showing signs of degradation.
We interview experts, including Chris McGuire, senior fellow for China and emerging technologies at the Council on Foreign Relations, and former senior director in the U.S. National Security Council on the Trump Administration's shifting stance toward AI accelerator export control rules.
Dellβs 14-inch Pro Premium combines a lightweight magnesium chassis, optimized motherboard, AI-capable processors, and high-quality display for executive mobility.
The new ChatGPT ad format is standardizing, according to a new Adthena analysis of 40,000+ daily placements. What once felt experimental is becoming a disciplined, high-intent system for users already deep in decision mode.
The big picture:ChatGPT ads are converging on a short, structured, highly contextual style that favors precision over persuasion and utility over storytelling, marking a shift from creative-led advertising to real-time, intent-driven assistance.
By the numbers. Every word must carry weight and contribute directly to clarity or conversion:
The average headline clocks in at just 30 characters and around 5 words.
Body copy averages 116 characters and roughly 19 words.
Whatβs working. The dominant pattern is a βBrand: Benefitβ headline, separating the name from a specific value. It works because users in conversational environments expect immediate clarity, not intrigue or ambiguity.
Almost every ad leads with the brand name. You need easy recall in a setting where users are already evaluating options, not discovering them.
Headlines are compressed. Headlines often read like functional labels rather than slogans. This brevity carries into the body copy. It typically uses two tight sentences: a proof point followed by an offer or nudge, showing youβre not trying to win an argument but give one compelling reason to act.
Context mirroring is a defining feature. The strongest ads directly reflect the userβs query or situation, signaling real-time tailoring. This marks a new level of AI-native targeting that goes beyond keyword matching into conversational relevance.
Concrete value signals carry outsized weight. Dollar signs and specific numbers β prices, savings, performance β consistently outperform vague claims. Numbers dominate body copy because they feel credible and native in a setting where youβre actively researching and comparing options.
Offers. Low-friction offers β especially βfreeβ trials or demos β are the most common conversion lever, reducing commitment barriers while users are exploring.
Calls to action. These are explicit and action-oriented, favoring direct phrases like βShop now,β βCompare,β or βBookβ while abandoning generic prompts like βLearn more.β
The overall tone. Calm, confident, and measured, with minimal exclamation points or question marks. It aligns more with helpful guidance than ad hype, helping ads blend into the conversational flow rather than disrupt it.
Why we care. ChatGPT ads reach users at high intent, where clarity and relevance matter more than creativity or storytelling. In a conversational environment, ads compete with useful answers, so vague or overly branded messages get ignored while precise, value-driven copy performs better. This shift rewards short, structured messaging and gives early adopters an advantage as the format standardizes.
Between the lines. While ChatGPT ads share DNA with paid search β especially in their focus on intent and relevance β they differ by integrating into dialogue, responding to high-intent users, and delivering messaging that feels assistive rather than interruptive.
The takeaway. Success in ChatGPT advertising depends on precision, relevance, and credibility over creativity, emotional appeal, or brand-led storytelling. The winning strategy: fit in perfectly when a user needs a clear, trustworthy answer.
The analysis. Adthena CMO Ashley Fletcher shared the data on LinkedIn.
Even a multi-billion-dollar rocket can't escape Microsoft's buggy Outlook apps. Artemis II Commander Reid Wiseman reported two broken versions of Outlook on his Surface Pro.
As we enter a new month, Steam's Hardware and Software Survey data has been processed, providing us with a clearer view of the overall gaming market that uses Steam platform. Today, the most notable change in the Steam Survey is the increase in Linux gamers, who have moved from their historically low single-digit market share to mid-single digits. As of March, Linux-based operating systems were running Steam on 5.33% of all polled systems. This represents an impressive 3.10% increase over February's data, which showed a dip in Linux market share from January's 3.5%. Fortunately, the numbers have now been revised upwards, marking a significant improvement for the community that has been steadily implementing improvements and making Linux-based gaming more accessible to everyone.
What might not be surprising is that a large portion of those 5.33% Linux installations run on Valve's customized SteamOS operating system. With a 24.48% share, the use of SteamOS grew by 0.65% last month alone, while other Linux distributions also contributed significantly. Other Windows alternatives like macOS are gaining momentum as well, with Apple seeing a 1.19% month-over-month increase to 2.35%. Although Linux now holds more than twice the market share of macOS, its growth within the Steam install base is a significant change, nearly doubling in just a month. Perhaps these alternative operating systems are now attracting enough attention from big game studios to encourage them to release native ports instead of relying on translation tools like Wine/Proton.
Urban Ascend is a city-building game centered on continuous expansion, system-driven design, and long-term optimization. Players grow a small town into a highly efficient metropolis by placing buildings, managing resources, and refining interconnected systems that evolve over time. The full version launches on Steam on April 3, 2026, following a public demo that introduced its core progression loop. The full release expands on those systems with additional buildings, upgrades, and mechanics designed to deepen strategic decision-making and long-term planning.
Urban Ascend features nearly 100 buildings and hundreds of upgrades that reshape how the city functions. Players manage citizen needs such as happiness, safety, and governance to unlock powerful bonuses, while responding to dynamic incidents that introduce new challenges as the city grows.
Solidigm, a pioneer in enterprise data storage, today announced it has exceeded initial investment goals for its Greater Sacramento initiatives, including the company's Rancho Cordova headquarters and surrounding research and development (R&D) campus. Announced in September 2022, Solidigm committed to investing $100 million into regional R&D facilities. Approximately three-and-a-half years into the build out, the company has surpassed this figure and will continue to invest in local talent and technology to help fuel global AI advancements.
In addition to $75 million in local lab investments, Solidigm has introduced close to 100 new NAND tools through the development of more than a $5 million world-class NAND lab and R&D center. "We have the most robust data storage product line for AI data centers," said Greg Matson, SVP, Head of Products and Marketing at Solidigm. "Our industry leading SSDs help our customers achieve the highest levels of efficiency, density, and performance in storage for their AI demands. And all of the innovation for us starts right here in Rancho Cordova."
No jokeβGFN Thursday is skipping the tricks and heading straight into the games. April kicks off with ten new titles, bringing fresh adventures to GeForce NOW, including the launch of Capcom's highly anticipated PRAGMATA.
A dozen new games are available to stream this week, including Arknights: Endfield, which expands the acclaimed series into a full 3D realβtime strategy adventure. On GeForce NOW, every battle flows with precision and every mission looks sharper than ever. So gear up, grab a controller or gaming device of choice, and get ready to streamβanother month of great gaming is now underway.
8BitDo has released a limited edition version of its Retro 68 mechanical keyboard to mark Apple's 50th anniversary. Called the AP50, it takes direct visual inspiration from the Apple II color scheme with the familiar beige and brown colors of that era of computing. The keyboard uses a 68-key compact layout built around a gasket-mount system for better typing acoustics and a softer key-press feel. Construction is all-aluminium, chassis, plate, and keycaps, and the 323.3 x 138.5 x 46.5 mm body reflects that, with the keyboard weight reaching 2.2 kg. Switches are Kailh BOX Ice Cream Pro Max, and the PCB is hot-swappable if you want to try something else without soldering. RGB backlighting is included, and the keyboard is programmable through 8BitDo Ultimate Software V2 or via fast-mapping directly on the keyboard without any software.
Connectivity covers all three modes: wired USB, 2.4G wireless, and Bluetooth LE. It's compatible with macOS, Windows 10 and above, and Android 9.0 and above. The 6500 mAh battery is rated for up to 300 hours of use with a 9-hour charge time. The package also includes a set of Wireless Dual Super Buttons (160.2 x 75.3 x 32.6 mm, 270 g), a 2.4G adapter, and a USB cable. At $499.99, the 8BitDo AP50 keyboard is clearly aimed at collectors and enthusiasts rather than anyone shopping on a budget.
CiscoΒ has released updates to address a critical security flaw in the Integrated Management Controller (IMC) that, if successfully exploited, could allow an unauthenticated, remote attacker to bypass authentication and gain access to the system with elevated privileges.
TheΒ vulnerability, tracked as CVE-2026-20093, carries a CVSS score of 9.8Β out of a maximum ofΒ 10.0.
"This
French consumer group UFC-Que Choisir recently announced a new lawsuit against Ubisoft after the French publisher pulled The Crew's servers offline. This "unilateral decision" stripped consumers who purchased the game of their rights, the group said, as Ubisoft never mentioned the fact that the racing game had an expiration date.
Four astronauts β Commander Reid Wiseman, pilot Victor Glover, mission specialist Christina Koch, and Canadian astronaut Jeremy Hansen β rode a 322-foot Space Launch System off Launch Complex 39B at 6:35 pm Eastern on Wednesday, beginning a roughly nine-day loop around the Moon.
Nvidia's market share in the Chinese data center market has shrunk, with a wealth of options coming from Huawei, Cambricon and more, bringing their total share up to 41%.
Cloud-native AI and data intensive workloads coming to IBM Z and LinuxONE systems as IBM wants to make its machines more relevant for today's workloads.
N+One gives cyclists an AI coach that designs and adapts training to your goals and daily readiness. Connect Strava, Garmin, Wahoo, or WHOOP to sync activities, sleep, HRV, and heart rate, then view recovery, mood, and FTP estimates on a single dashboard. Chat with your coach anytime for guidance, nutrition tips, and real-time plan adjustments that fit your schedule.
Video Clipper helps creators repurpose long-form YouTube content into Shorts. Paste a YouTube URL and let AI transcribe, detect the most engaging moments, and cut ready-to-upload clips. You pay per minute processed with credits that never expire, starting with 20 free credits. Upload results straight to YouTube and scale your clipping workflow without subscriptions.
The definitive AI documentary is now in theatres and is expected to stream in 2026. Hereβs how you can currently watch The AI Doc: Or How I Became an Apocaloptimist ahead of its streaming launch.
After the rocky Handmaid's Tale season 6 finale, The Testaments on Hulu and Disney+ is just the beginning of something even bigger, better, and bolder.
The acting director of U.S. Immigration and Customs Enforcement told lawmakers that the use of Paragon spyware is necessary to counter terroristsβ βthriving exploitation of encrypted communications platforms.β
Thereβs a flood coming. A downpour of noise β more content, more channels, more AI-generated everything, moving faster than most teams can keep up with. Somewhere in that volume, your customers are quietly drowning β overwhelmed, underserved, and one bad experience away from choosing someone else.
Youβve probably felt it on your team, too. Another tool. Another sprint. Another quarter of doing more with less. The productivity metrics look fine from the outside. But inside, people are running on empty.
Thereβs an old story about a man named Noah who, facing catastrophic disruption, didnβt freeze or panic. He didnβt look for shortcuts or try to outswim the storm. He built β with intention, with a clear design, and with people he trusted. When the waters rose, the ark held.
The brands that lead donβt adopt the most technology the fastest. They build with intention β designing systems and experiences that protect people.
What follows is the case for building your ark β and a practical framework to do it.
AI power users report that it makes their overwhelming workload more manageable (92%), boosts creativity (92%), and helps them focus on their most important work (93%), per Microsoft and LinkedInβs Work Trend Index,.
Yet, 60% of leaders say their company lacks a concrete AI vision or plan β meaning the very tool that could relieve team burnout is sitting underutilized.Β
That gap shows up in real ways.
For customers, it creates friction β too many choices, unclear navigation, and messaging that misses where they are. They arrive with a question and leave with more confusion. They donβt feel seen or helped.
For marketing teams, the impact is quieter but just as serious:
Decision fatigue disguised as strategy.
Tool overload framed as innovation.
Burnout that looks like productivity β until it doesnβt.
Fragmented workflows that drain energy faster than they produce results.
Brands that recognize these human issues move faster, retain stronger talent, build deeper customer loyalty, and drive better business outcomes. Enter what I call the wellness sweet spot.
The wellness sweet spot is the moment where AI, empathy, and human-first design converge β creating conditions where both your customers and your team can think clearly, act confidently, and trust the experience theyβre in.
Itβs an architectural decision about how your entire marketing ecosystem is designed to make people feel. When its three pillars are genuinely working together, four things become true simultaneously:
AI reduces waste and cognitive load in the experience β making things simpler.
Emotional friction is intentionally minimized at every touchpoint.
Marketing teams operate from a foundation of wellness (and well-being).
Systems and workflows support human thriving, not just throughput.
When these conditions are in place, something shifts. AI stops feeling like a disruption and starts working as a stabilizing layer β supporting, protecting, and quietly holding the system together. It manages the overwhelm. The ark keeps floating.
Most marketing leaders still think about AI in terms of what it does β automate, generate, optimize, analyze. Those outcomes matter, but they donβt tell the full story. The more consequential question is how AI makes people feel while itβs doing those things.
For customers, AI used well is a guide that:
Summarizes complexity without dumbing it down.
Narrows choices in ways that feel helpful rather than manipulative.Β
Anticipates what someone needs next and removes ambiguity from decision paths.Β
Saves time β which is, in a very real sense, saving emotional energy.
For teams, thoughtfully deployed AI absorbs the work that depletes people most: the repetitive, the reactive, and the administrative. It creates space for what human brains do best: strategy, creativity, relationship-building, and nuanced judgment.
When you build your marketing systems around it, the output quality goes up because the people producing it arenβt running on fumes.
This is empathy at scale. Not the kind that lives in a tagline, but the kind thatβs baked into how your systems are structured and how your content is designed to reach people.
The new emotional metrics: What to measure when you start caring about feelings
This is where things get practical and start to move ahead of the curve. Most marketing dashboards show what happened β click-through rates, conversion rates, and time on page. Those metrics matter, but they donβt explain why someone left or how they felt along the way.
Emotional metrics help fill that gap by focusing on the conditions under which decisions are made. Research in psychology and neuroscience shows that people make better decisions, build stronger brand relationships, and become more loyal when they feel clear, confident, and calm.
Hereβs how traditional metrics map to emotional KPIs:
Traditional metric
Emotional KPI
What it measures, reimagined
Time on page
Clarity index
How quickly someone finds what they need β without confusion
Conversion rate
Decision effort score
Cognitive load required to complete an action
Engagement rate
Customer calm markers
Behavioral signals of confidence, not stress (Qualified attention)
Team output volume
Wellness throughput
Strategic output produced with reduced burnout
These are upstream indicators that help explain downstream performance. A low clarity index often shows up as stalled conversion rates. A high decision effort score can lead to rising cart abandonment. Declining wellness throughput tends to result in average output from top strategists.
Brands that start tracking these now gain an advantage over those that wait to react.
5 steps to design toward your wellness sweet spot
A caution before the roadmap: more speed and scale applied to a broken system will not fix it. It will amplify everything thatβs wrong with it. These five steps are meant to be done before you push harder on AI adoption.
Step 1: Run an empathy audit
Where are customers confused? Hesitating? Leaving? Map these moments using behavioral data combined with qualitative insight β customer interviews, session recordings, support tickets, search data. Focus less on what people clicked and more on where they felt lost.
Step 2: Simplify for cognitive ease
Fewer choices. Plain language. Cleaner navigation. Every step you remove from a decision path is a small act of respect for your customerβs mental energy. This is generous. Itβs designing with intelligence.
Step 3: Use AI as a shepherd
Deploy AI to enhance orientation, clarity, and confidence. Donβt push aggressive automation or manufacture a sense of urgency. AI should make customers feel helped, not herded. Thereβs a difference, and your audience feels it.
Step 4: Rebuild team workflows around energy
Audit where your teamβs cognitive energy actually goes each week. Identify the work that is routine, reactive, or repetitive β and build AI into those gaps first. Protect the hours that require human judgment, creativity, and relationship-building. Those are the hours that drive real growth.
Step 5: Measure the feels
Begin tracking emotional outcomes alongside performance metrics. Start simple: add a one-question post-interaction survey.Β
Review search data for confusion signals. For example, growing volume for βhow do Iβ or βwhy canβt Iβ phrases on your own site may indicate your content isnβt answering questions before theyβre asked.Β
Monitor support ticket themes for friction patterns. A perfect measurement system isnβt required to start. The intention to look is.
The future belongs to emotionally intelligent brands
In a market where nearly every brand claims to be customer-centric and frictionless, the real differentiator comes down to how people feel and whether systems consistently deliver on that promise.
Leading organizations donβt rely on bigger AI budgets. They align technology with clear intent, prioritize well-timed, empathy-led content over volume, treat customer well-being as part of the brand promise, and protect their teamsβ energy as rigorously as performance.
Creating value starts with protecting the people who create it. Noah didnβt survive the flood by ignoring it or fearing it. He paid attention, took action, and built with intention β something designed to carry what mattered most: his people, his purpose, his peace, and his future. Thatβs the kind of leadership this moment calls for.
You donβt have to figure this out alone. The tools are here. The framework is yours. The decision is whether to build before the pressure hits or react once itβs already underway.
Youβve done everything right. You have a fast website with comprehensive content, pages ranking in the top 10, and a strong backlink profile. Yet when you search the query you rank for, your site doesnβt appear in Googleβs corresponding AI Overview.
This is a retrieval problem, not a ranking issue. And the difference between the two is the most important shift SEOs need to understand right now.
AI Overviews donβt work like traditional organic rankings. Instead of considering which page has the most signals, AI Overviews look for the page that gives the cleanest, most usable answer.
If your content doesnβt meet that standard, your traditional search ranking is irrelevant. Hereβs whatβs going wrong, and how to fix it so your content appears in more AI Overviews.
The ranking-citation gap is real β and growing
The overlap between AI Overview citations and organic rankings grew from 32.3% to 54.5% between May 2024 and September 2025, according to a BrightEdge study.
This trend sounds encouraging. But it also means that even at peak convergence, nearly half of all AI Overview citations come from pages that donβt rank at the top of organic results. Google actively bypasses higher-ranking pages when it finds content that better serves the AI Overview format.
The pattern varies sharply by sector, though. BrightEdge data shows that in ecommerce, the overlap barely changed, remaining essentially flat over the entire 16-month period. And in your money or your life (YMYL) categories like healthcare, insurance, and education, the overlap between AI Overview citations and organic rankings ranges from 68% to 75%.
Ranking and visibility are no longer the same thing. You can rank second and be invisible. Or, you can rank on the second page and be the first thing a searcher reads.
1. Your content answers the wrong version of the question
Informational queries β specifically long-tail and conversational searches β typically trigger AI Overviews. Informational queries drive 57% of AI Overviews, while commercial queries trigger this AI feature far less frequently, according to Semrush research.
Googleβs AI engineΒ looks for content that matches what the user asks, not just the keyword youβve targeted. So, an AI Overview answering the query βwhatβs the best way to manage a remote teamβs workload?β probably wonβt cite a page that ranks for the keyword βproject management softwareβ and leads with features and pricing.
2. Youβve buried the answer
If your introduction spends three paragraphs establishing context, warming up the reader, or restating the question before answering it, the retrieval system moves on. It seeks information it can extract cleanly. If that answer isnβt near the top of the page, the system skips that page.
3. Your structure is opaque to AI systems
Traditional SEO content is built around comprehensive long-form content: 3,000-word guides covering every angle of a topic, written for readers who scroll and skim.
AI retrieval systems donβt work the same way. They need to identify discrete, self-contained answers within your content.
That requires clear heading hierarchies, short paragraphs, and content that AI systems can extract. A section under a specific heading should completely answer the question posed in that heading, without requiring the surrounding context to make sense.
Content written as one long, unbroken narrative is harder for AI systems to parse. Even if every word is accurate and authoritative, it may not earn a citation if the structure doesnβt help the retrieval system identify individual answer units.
4. Your E-E-A-T signals arenβt visible at the content level
Google has been clear that experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) signals are important for content quality in traditional search. It likely matters for AI Overviews, too. But these signals need to appear in the content itself, not just in your domain profile or link graph.
Strong domain authority counts for less than youβd think if the content itself carries no credibility signals.
Who wrote it?
Where did the data come from?
Is there anything here that couldnβt have been written by someone whoβd never worked in this field?
A retrieval system evaluating an individual page doesnβt know your domainβs track record. The page must make the case for itself.Β
Content-level E-E-A-T signals are particularly important in YMYL categories, where AI Overviews are selective about sources because the risk of misinformation is higher.
5. Youβre targeting queries that donβt trigger AI Overviews
Before optimizing your content for AI engines, itβs worth checking whether your target queries trigger AI Overviews at all. As of late 2025, AI Overviews appear in 16% of search results, though that figure isnβt evenly distributed across query types.
Transactional queries, navigational searches, branded queries, and highly local searches are far less likely to trigger an AI Overview. If most of your traffic comes from commercial or transactional keywords, the lack of AI Overview citation may not be a content problem. It may simply be that those query types are less likely to generate overviews in the first place.
What the data tells us about the impact of this shift
The stakes are significant. Research by Seer Interactive shows that organic click-through rates (CTRs) for informational queries that displayed AI Overviews dropped 61%, from 1.76% to 0.61%, between June 2024 and September 2025. Paid CTR fell even further, from 19.7% to 6.34%.
But the same research reveals a critical asymmetry: Brands cited in AI Overviews saw 35% higher organic CTR and 91% higher paid CTR than when they werenβt cited. A citation in an AI Overview doesnβt just protect you from a CTR decline. It actively amplifies your visibility.
The Pew Research Centerβs study of searches by U.S. adults in March 2025 found that only 8% of users who encountered an AI Overview clicked a traditional search result, compared to 15% who clicked when no overview appeared. And 26% of searches with AI Overviews resulted in no clicks at all.
If AI Overviews appear for your most valuable queries and you arenβt cited, you arenβt just missing out on the overview. Youβre losing clicks you previously received from the organic listing underneath it.
How to optimize for retrieval, not just rankings
These trends require you to adjust how you think about content structure and intent. Hereβs where to focus:
Rewrite your introductions: Your first paragraph should directly and completely answer the primary question of the page. Save context and elaboration for later sections. Write as if the first 100 words of your page represent a standalone answer.
Restructure your headings: Each heading should be a question or a complete, specific claim. The following section should fully answer or support that heading without requiring the reader to review previous sections. Think of each section as a self-contained answer unit.
Add explicit expertise signals: Include author attribution with credentials, first-person experience language, original data, and links to primary sources and original research. These signals matter at the content level, not just at the domain level.
Audit your query triggers: Manually test your target queries in Google to see which ones actually generate AI Overviews. For those that do, study how the cited sources are structured, the length of the cited sections, and the format of the answer. Use that as your editorial brief.
Expand your topical coverage: AI Overviews favor sources that demonstrate breadth of knowledge across a topic, not just single-page depth. Focus on answering several related questions well instead of building one exceptional page surrounded by thin content.
What AI Overviews represent is something thatβs been discussed for years, but few have truly prepared for: the separation of content quality from ranking signals.
For two decades, we used rankings as a proxy for quality. High-ranking content was, by definition, good enough.
But that assumption no longer holds. Ranking in traditional search indicates that your brand has authority and that your page is relevant to the search query. It says nothing about whether your content is structured in a way that AI retrieval systems can use.
Visibility now goes to whoever understands how AI systems identify, extract, and surface answers. A strong backlink profile wonβt help you if the answer is buried on page three of a 4,000-word guide.
Ranking in the top 10 is still worth pursuing. But itβs no longer the whole game.
Linux use amongst PC gamers is growing, and Valveβs Steam Machine isnβt even out yet Valve has released its March 2026 Steam hardware survey, and it is clear that Linux adoption continues to grow among gamers. Now, Linux use has reached an all-time high of 5.33% of Steam users. This month, Linux has over twice [β¦]
HP's first laptop featuring the new Qualcomm Snapdragon Elite X2 SoC is now available to buy, and it should be the right choice for anyone who wants an ultra-thin, ultra-light PC with outstanding performance and efficiency.
The HP Omen MAX 16" configuration with an RTX 5070 graphics card, Intel Core Ultra 7 processor, 16GB RAM, 1TB SSD, and more is now on sale at a whopping 32% discount.
Microsoft has unveiled a host of in-house AI models, including MAI-Transcribe-1, MAI-Voice-1, andβ―MAI-Image-2, which will be available via Microsoft Foundry.
TheΒ latest ThreatsDay Bulletin is basically a cheat sheet for everything breaking on the internet right now. NoΒ corporate fluff or boring lectures here, just a quick and honest look at the messy reality of keeping systems safe thisΒ week.
ThingsΒ are moving fast. TheΒ list includes researchers chaining small bugs together to create massive backdoors, old software flaws
IBM (NYSE: IBM) today announced a strategic collaboration with Arm to develop new dualβarchitecture hardware that helps enterprises run future AI and data intensive workloads with greater flexibility, reliability, and security.
IBM's leadership in system design, from silicon to software and security, has helped enterprises adopt emerging technologies with the scale and reliability required for missionβcritical workloads. As AI moves deeper into core business operations, IBM continues to invest in hardware platforms such as the Telum II processor and Spyre Accelerator, which are designed to bring AI from experimentation into everyday enterprise use.
Nintendo can respond to the decision within two months, amend the claims, and appeal. But the fact that the examiner rejected all 26 claims is hardly a great look for a patent that IP lawyers were hammering long before this latest ruling arrived.
The people, who asked not to be identified because the details are private, said SpaceX could seek a valuation above $1.75 trillion, eclipsing Saudi Aramco's 2019 IPO as the biggest to date.
TrendForce reports that DRAM and NAND costs will continue increasing in the second quarter of 2026 as demand from AI data centers remains strong. Meanwhile, sources from the tech industry supply chain have informed Nikkei Asia that shortages of basic materials will raise the prices of printed circuit boards and...
The release notes for Ubuntu 26.04 LTS show that Canonical has quietly raised the minimum RAM requirement for its popular Linux-based operating system by 50%.
ClawCloud provides fully managed hosting for OpenClaw, giving you a private, always-on AI assistant with no setup. Each plan includes a dedicated, isolated instance that stays online 24/7 while the team handles updates, security, scaling, and daily backups. Just log in and start using your assistant to automate tasks across email, code, the browser, files, and more, with priority support available on higher tiers.
Costlix is an AI-powered comparison engine that analyzes pricing, features, and reviews to deliver clear tables and detailed cost breakdowns. It reveals hidden fees, subscription tiers, and long-term value so you can see which option fits your budget. Use it to compare software, electronics, household goods, and travel, with recommendations tailored to your usage.
After censoring the internet for years, the Kremlin is now pushing citizens to the state-controlled MAX by further targeting VPN usage. But the disconnect over the Telegram shutdown could be the needed turning point.
The TSA wait times feature is one of many new additions to the United app, including automatic rebooking assistance and real-time time weather radar maps.
It is the path of least resistance for a growth-stage company to hire from the familiar Silicon Valley pipelines but if a founder wants a diverse team, that value has to be put into practice from the very first hire.Β
Your paid social operation is on fire. You know how your audience thinks, the creative process is dialed in, and the results get better every year. Leadership greenlights an expansion to Google Ads β a new channel and, critically, a new source of revenue.
As it turns out, applying that same strategy really just buys you an express ticket to a very difficult conversation.
Google rewards a different kind of thinking. Intent signals and campaign logic are different, and the mistakes that eat at your budget donβt always make themselves clear. Brands that apply their existing Meta playbook often find themselves looking at shiny dashboards and dull balance sheets.
These six common mistakes tend to do the most damage before anyone realizes whatβs happening. Theyβre what we see most often when ecommerce brands come to us after making the move to Google β and they can all be reversed.
Mistake 1: Treating Google like a retention channel
You can definitely use Google Ads to support retention and brand defense. The problem is when that becomes your whole strategy.
We see this regularly with brands new to the platform who launch directly into Performance Max. Early ROAS looks strong, and everyoneβs happy. But a few months in, someone asks the right question: Are we actually growing, or paying to capture purchases that were going to happen anyway?
One client we worked with came to us with branded search and retargeting doing the heavy lifting inside PMax β essentially a tax on demand that had already been created elsewhere. Revenue flatlined because, while the ad spend was real, growth was not.
Net-new customer acquisition requires a different setup.
Shopping campaigns structured to surface products to people who have never heard of the brand.
Search campaigns built around non-branded, high-intent keywords.
Layered PMax configurations that limit the system from defaulting to the easiest conversions.
When Google has enormous reach into new audiences, treating it purely as a closing channel leaves most of that opportunity untouched.
Mistake 2: Not knowing how to get the most out of Googleβs core levers
Paid social experience transfers to Google in some ways, but there are four areas where we see the biggest knowledge gaps.
Search intent
Ads on social media are an interrupting moment. Ads in search engines meet people as theyβre looking for something you offer. This changes so much about campaign structure, ad copy, and keyword targeting.Β
Upper-funnel terms and lower-funnel terms require different approaches, bids, and landing pages. Collapsing them into a single campaign structure is one of the fastest ways to dilute intent and waste budget on traffic that was never going to convert.
Data feed optimization
For ecommerce brands running Shopping and retail Performance Max, the product feed is the foundation everything else is built on. Weak titles, missing attributes, and poor categorization limit how often your products show up and who sees them.Β
Most brands (including Google-native ones) underinvest here because the work is unglamorous. But a well-optimized feed consistently outperforms one thatβs neglected after setup.
Keyword research
Paid search is a keyword-driven channel, which makes keyword strategy its own discipline. Understand match types, search volume, commercial intent, and the relationship between what people type and what they actually want. This takes time to develop, but brands that skip this step usually over-restrict their reach or bleed spend on irrelevant traffic.
Landing pages
Sending high-intent but unfamiliar visitors straight to a product page on Google often underperforms. A more engaging landing page format, like an advertorial, puts that traffic in front of context and trust before asking for the sale.Β
Brands coming from paid social often overlook this because the funnel architecture theyβre used to doesnβt require it.
Googleβs algorithms need consistent data to make the best decisions for your account. But every time a campaign goes dark β for a day or a week β thereβs a risk that the learning resets. What feels like a minor admin issue can mean weeks of degraded performance and wasted ad spend.
Two types of disruption come up more than any other.
Payments: Brands switching to invoice billing or changing card details mid-flight will sometimes see campaigns pause without realizing it until the damage is done. A lapsed payment that takes three days to resolve can cost far more than the bill itself once you factor in recovery time.
Tracking and feed integrity: A broken pixel means no conversion data, and forces Smart Bidding to optimize blind. A feed error in Merchant Center means products disappear from Shopping and Performance Max. Neither of these failures are loud, and they tend to surface slowly as declining performance that gets misattributed.
They are both preventable with automated alerts, weekly feed audits, and a person or AI agent responsible for monitoring account health between reporting cycles. The cost of oversight is low compared to what happens if you only discover issues after the fact.
Mistake 4: Building a campaign structure thatβs too granular
The instinct among detail-oriented advertisers is to segment everything because it feels like control on the surface.
One campaign per product category.
One ad group per keyword.
Separate budgets for every audience.
But Googleβs automation needs data to make good decisions. When you spread your budget across too many campaigns, each one operates on thin resources and even thinner information. Smart Bidding canβt optimize effectively without sufficient conversion volume, so campaigns stuck below that threshold tend to underperform and stay there.
By over-segmenting, youβve created the appearance of precision while actually limiting the systemβs ability to learn.
The same logic applies to budget. Ten campaigns with a modest shared budget will almost always produce worse results than three well-funded ones. Google needs room to test, adjust, and find the traffic worth paying for. Fragmented budgets donβt allow it to do that.
Build a tighter structure with fewer campaigns, clearly defined goals, and enough budget to compete. This gives the algorithm what it needs while keeping the account manageable enough to oversee effectively.
Mistake 5: Leaving campaigns on Max Conversion Value with no ROAS targets
Max Conversion Value is a Smart Bidding strategy that tells Google to spend your budget in whatever way generates the highest total conversion amount β no ceiling, no floor, no efficiency guardrail. Left unsupervised, it will find conversions, but wonβt care what it costs to get them.
For brands new to Google Ads, this setting can trick you into thinking youβre crushing it. Conversion value shoots up in the right direction, making the account appear healthy. The problem surfaces when you look at what you actually spent to generate that value.
Without a target ROAS, Google has no efficiency quotient, and optimizes for volume, not profitability. But the fix is straightforward.
Once you have enough conversion data, set a realistic target.
A ROAS goal gives the algorithm a constraint, and shifts the objective from spending budget to spending it well.
Targets set too aggressively too early can starve campaigns of traffic before theyβve had a chance to learn.
Exercise patience, and a willingness to adjust gradually rather than chasing the ideal number from day one.
Mistake 6: Underfunding campaigns and keeping them stuck in learning
When you launch a Google campaign or make a significant change (like doubling the budget), it enters a new learning period. This is the window for gathering data, testing different auctions, and calibrating toward the conversion patterns youβve defined.
Itβs a normal part of how the platform works, and every campaign goes through it.
But the learning period requires a minimum volume of conversions to complete. Google typically needs around 30-50 conversion events in a short window before bidding stabilizes. A campaign thatβs underfunded for this milestone will stay in learning indefinitely.
Itβs a common trap for brands being cautious when testing Google.
You run your first campaign on a small budget.
CPAs are inflated, and data is inconclusive, so you donβt invest more or cut it entirely.
In reality, the campaign never had what it needed to graduate out of the learning phase.
You walk away from net new revenue before youβve even scratched its surface.
Funding a new campaign adequately from the start β even if it means consolidating into fewer campaigns and chasing fewer goals β gives it the best chance of learning fast and delivering accurate results sooner.
Adding Google to the mix is the right call: Hereβs what to do next
Diversifying away from a single ad platform is one of the smartest moves an ecommerce brand can make once itβs mature enough to fight on two fronts. It removes growth from the anchor of one platformβs algorithm changes, auction dynamics, seasonality, terms of service, etc.
Adding Google to Meta also gives you access to a different kind of demand that is actively expressed rather than passively targeted, which is a meaningful advantage worth building on.
These six mistakes are not reasons you should avoid Google, but a preventative guide to help you approach it with realistic expectations and enough patience to let the system learn. Treating it like a direct analog of what youβre already doing on Meta will make you leave before seeing whatβs truly possible.
Google launched a channel performance timeline view in Performance Max. It gives you a clearer breakdown of how Search, YouTube, Display, and other channels contribute to campaign results over time.
Whatβs new. A timeline graph shows channel-level contributions over a selected period, paired with investment and performance filters. You can quickly see which channels are pulling their weight β and which arenβt.
Yellow box β Channel Performance Evolution Over Time
Pink box (right) β All Ads, Ads Using Product Lists, Ads Using Video
Why we care. Performance Max campaigns run across multiple channels at once, making it difficult to see where your budget is most effective. This gives you a timeline view of channel-level contributions β so if YouTube is underperforming while Search drives most conversions, you can see it without digging through exports or relying on guesswork. You can spot channel-level trends earlier and adjust your asset strategy or budget accordingly.
The big picture. This view gives you a more actionable way to evaluate PMax performance without relying solely on Googleβs automated decisions.
Bottom line. Itβs not full transparency, but itβs a meaningful step in the right direction. You get a cleaner way to spot PMax trend anomalies early and adjust accordingly.
First spotted. This update was first spotted by Axel Falck, Head of Search at Le Mage du SEA, who shared it on LinkedIn.
Steam just released its March hardware and software survey, and it's clear that the PC gaming market is going through a massive flux as inflated prices force buyers into new (and old) areas.
AMD's RDNA 4-based Radeon RX 9070 and RX 9070 XT graphics cards have finally reached reasonable pricing, as German retailers report that these GPUs are now selling below MSRP. In Germany, the European MSRP for Radeon RX 9070 cards is β¬629, including 19% VAT. For its bigger sibling, the Radeon RX 9070 XT, the European MSRP is listed at β¬689, also including the sales tax. However, according to multiple listings from German online retailers, both cards are trading below their European MSRP pricing, marking the first occurrence since the memory shortage fiasco began, which took a toll on the gaming community. The cheapest Radeon RX 9070 non-XT model is listed at β¬539.00 in the form of the ASUS Prime Radeon RX 9070 OC SKU, while the cheapest Radeon RX 9070 XT model is listed at β¬640 for the ASRock Radeon RX 9070 XT Challenger GPU. Do note that this is not the first time that these GPUs are priced below MSRP in Germany, but just the first time since the memory shortages started.
Interestingly, this price drop in Germany is not consistent with the markets in the United States, where GPU pricing for the Radeon RX 9070 and RX 9070 XT still ranges around $810-$820 for the non-XT SKU and about $880-$890 for the Radeon RX 9070 XT model. This represents a large premium in the U.S. market, considering that the Radeon RX 9070 and Radeon RX 9070 XT graphics cards have MSRPs of $549 and $599, respectively. Perhaps a fresh supply of GPUs has hit the German market, causing supply to overwhelm demand and significantly pushing prices down. In the U.S., that is not the case, where prices remain high and on an upward trajectory, according to PCPartPicker. In contrast, the German market is experiencing some of the lowest pricing in recent months, finally giving gamers a break.
Several large media outlets, including the BBC, USA Today, and the Daily Mail, reported yesterday about an X post that purportedly belonged to Joe Hollins, a vet who has previously cared for Jonathan.
According to a study by engineers at Caltech and the UC Department of Physics, quantum computers do not need to be nearly as powerful as previously believed to crack the most advanced cryptographic technologies. The research claims that Shor's algorithm could break RSA public-key encryption using quantum computers with just...
The Dell XPS 14 achieved battery life that's nearly three times as long as the M5 MacBook Air thanks to its 120Hz VRR display that can go as low as 1Hz.
The GameStop NVMe SSD is a decent gamer-oriented drive with good performance and a nice heatsink at a reasonable price. SMI and Samsung hardware come together for a compelling package.
With For All Mankind season 5 and Imperfect Women both on Apple TV, Joel Kinnaman is having the last laugh β but there have still been 'challenges' along the way.
Tracking your brandβs visibility in AI-powered search is the new frontier of SEO. The tools built to do this are expensive, often starting at $300 to $500 per month and quickly rising from there. For many, that price is a nonstarter, especially when custom testing needs go beyond what off-the-shelf software can handle.
I faced this exact problem. I needed a specific tool, and it didnβt exist at a price I could afford, so I decided to build it myself. Iβm not a developer. I spent a weekend talking to an AI agent in plain English, and the result was a working AI search visibility tracker that does exactly what I need.
Below is the guide I wish Iβd had when I started: a step-by-step playbook for building your own custom tool, covering the technology, the process, what broke, and how to get it right faster.
The problem: A custom tool for a complex landscape
My goal was to automate an AI engine optimization (AEO) testing protocol. This wasnβt just about checking one or two models. To get a full picture of AI-driven brand visibility, I knew from the start that we had to track five distinct, critical surfaces:
ChatGPT (via API): The most well-known conversational AI.
Claude (via API): A major competitor with a different response style.
On top of that, I needed to score the results using a custom 5-point rubric: brand name inclusion, accuracy, correctness of pricing, actionability, and quality of citations. No existing SaaS tool offered this exact combination of surfaces and custom scoring. The only path forward was to build.
Here are a few screenshots of the internal tool as it stands. You can see some of my frustration in the agent chat window.
The method: Using vibe coding to build the tool
This project was built using vibe coding, a way of turning natural language instructions into a working application with an AI agent. You focus on the goal, the βvibe,β and the AI handles the complex code.
This isnβt a fringe concept. With 84% of developers now using AI coding tools and a quarter of Y Combinatorβs Winter 2025 startups being built with 95% AI-generated code, this method has become a viable way for non-developers to create powerful internal tools.
You can replicate this entire project with just three things, keeping your monthly cost under $100.
Replit Agent
This is a development environment that lives entirely in your web browser. Its AI agent lets you build and deploy applications just by describing what you want. You donβt need to install anything on your computer. The plan I used costs $20/month.
DataForSEO APIs
This was the backbone of the project. Their APIs let you pull data from all the different AI surfaces through a single, unified system.Β
You can get responses from models like ChatGPT and Claude, and pull the specific results from Googleβs AI Mode and AI Overviews. It has pay-as-you-go pricing, so you only pay for what you use.
Direct LLM APIs (optional but recommended)
I also set up direct connections to the APIs for OpenAI (ChatGPT), Anthropic (Claude), and Google (Gemini). This was useful for double-checking results and debugging when something seemed off.
The playbook: A step-by-step guide to building your tool
Building with an AI agent is a partnership. The AI will only do what you ask, so your job is to be a clear and effective guide.
Hereβs a repeatable framework that will help you avoid the biggest mistakes.
Step 1: Write a requirements document first
Before you even open Replit, create a simple text document that outlines exactly what you need. This is your blueprint. Include:
The core problem youβre solving.
Every feature you want (e.g., CSV upload, custom scoring, data export).
The data youβll put in, and the reports you want out.
Any APIs you know youβll need to connect to.
Start your conversation with the AI agent by uploading this document. It will serve as the foundation for the entire build.
Step 2: Ask the AI, βWhat am I missing?β
This is the most important step. After you provide your requirements, the AI has context. Now, ask it to find the blind spots. Use these exact questions:
βWhat am I not accounting for in this plan?β
βWhat technical issues should I know about?β
βHow should data be stored so my results donβt disappear?β
That last question is critical. I didnβt ask it, and I lost a whole batch of test results because the agent hadnβt built a database to save them.
Step 3: Build one feature at a time and test it
Donβt ask the AI to build everything at once. Give it one small task, like βbuild a screen where I can upload a CSV file of prompts.βΒ
Once the agent says itβs done, test that single feature. Does it work? Great. Now move to the next one.Β
This incremental approach makes it much easier to find and fix problems.
When itβs time to connect to an API like DataForSEO, donβt assume the AI knows how it works. Find the API documentation page for what youβre trying to do, and give the URL directly to the agent.Β
A simple instruction like, βRead the documentation at this URL to implement the authentication,β will save you hours of frustration. My first attempt at connecting failed because the agent guessed the wrong method.
Step 5: Save working versions
Before you ask for a major new feature, save a copy of your project. In Replit, this is called βforking.β New features can sometimes break old ones.Β
I learned this when the agent was working on my results table, and it accidentally broke the CSV upload feature that had been working perfectly. Having a saved version makes it easy to go back and see what changed.
Nearly everything will break at some point. Thatβs part of the process. Here are the most common issues I ran into, and the lessons I learned, so you can be prepared.
Problem
The lesson and how to fix it
1. API authentication fails
The agent will often try a generic method.Β
Fix: Give the agent the exact URL to the APIβs authentication documentation.
2. Results disappear
The agent may not build a database by default, storing data in temporary memory instead.Β
Fix: In your first step, ask the agent to include a database for persistent storage.
3. API responses donβt show up
You might see data in your API providerβs dashboard, but itβs missing in your app. This is usually a parsing error.Β
Fix: Copy the raw JSON response from your API provider, and paste it into the chat. Say, βThe app isnβt displaying this data. Find the error in the parsing logic.β
4. Model responses are cut short
An LLM like Claude might suddenly start giving one-word answers. This often means the token limit was accidentally changed.Β
Fix: After any update, run a quick test on all your connected AI surfaces to ensure the basic parameters havenβt changed.
5. API results donβt match the public version
ChatGPTβs public website provides web citations, but the API might not.Β
Fix: Realize that APIs often have different default settings. You may need to explicitly tell the agent to enable features like web search for the API call.
6. Citation URLs are unusable
Geminiβs API returned long, encoded redirect links instead of the final source URLs.Β
Fix: Inspect the raw data. You may need to ask the agent to build a post-processing step, like a redirect resolver, to clean up the data.
7. Your app isnβt updated
You build a great new feature, but it doesnβt seem to be working in the live app.Β
Fix: Understand the difference between your development environment and your production app. You need to explicitly βpublishβ or βdeployβ your changes to make them live.
The real costs: Is it worth it?
Building this tool saved me a significant amount of money. Hereβs a simple cost comparison against a mid-tier SaaS tool.
Item
DIY tool (My project)
SaaS alternative
Software subscription
~$20/month (Replit)
$500/month
API usage
~$60/month (variable)
Included
Total monthly cost
~$80/month
$500/month
The biggest cost is your time. I spent a weekend and several evenings building the first version. However, I now have an asset that I can modify and reuse for any client without my costs increasing.Β
The hidden costs are real: thereβs no customer support, and you are responsible for maintenance. But for many, the savings and customization are worth it.
This approach isnβt for everyone. Hereβs a simple guide to help you decide.
Build your own if:
You need a custom testing method that no SaaS tool offers.
You want a white-labeled tool for your agency.
Your budget is tight, but you have the time to invest in the process.
Stick with a SaaS tool if:
Your time is more valuable than the monthly subscription fee.
You need enterprise-level security and dedicated support.
Standard, off-the-shelf features are good enough for your needs.
For many SEOs, the answer is clear. The ability to build a tool that works exactly the way you do, for less than $100 a month, is a game-changer.Β
The process will be frustrating at times, but you will end up with something that gives you a unique advantage. The era of the practitioner-developer is here. Itβs time to start building.
GOGβs new versions of Capcomβs original Resident Evil games are now available on Steam Capcom has officially released its Resident Evil Classic Collection on Steam, bringing the original PC versions of Resident Evil, Resident Evil 2, and Resident Evil 3: Nemesis to PCβs most popular platform. These classic game re-releases were co-developed with GOG and [β¦]
AΒ financially motivated operationΒ codenamed REF1695Β has beenΒ observed leveraging fake installers to deploy remote access trojans (RATs) and cryptocurrency miners since NovemberΒ 2023.
"Beyond cryptomining, the threat actor monetizes infections through CPA (Cost Per Action) fraud, directing victims to content locker pages under the guise of software registration," Elastic
In DecemberΒ 2025, we shared the first-ever The State of Trusted OpenΒ Source report, featuring insights from our product data and customer base on open source consumption across our catalog of container image projects, versions, images, language libraries, and builds. TheseΒ insights shed light on what teams pull, deploy, and maintain day to day, alongside the vulnerabilities and
Ubisoft has confirmed that The Division 3's roadmap is fully planned, suggesting fans don't need to worry about the game's development following recent studio changes.
Here's our guide on a super simple way to stream and watch The Housemaid for free from anywhere in the world, as Sydney Sweeney's saucy thriller hits streaming services in selected countries.
February was one of those weird months where the Steam survey saw massive changes from the status quo: the RTX 5070 became the top GPU, AMD lost CPU share, Windows 11 was down 10%, and Chinese became the most common language among participants.
Grab this Alienware Area-51 gaming PC with performance that few can rival. It's on sale with a huge $950 discount right now, shipping with an RTX 5090, AMD Ryzen 9 9950X3D, 32GB of RAM, and a 2TB SSD, all for only $5,299.99
A YouTuber resurrects a damaged RTX 4090 with a bent PCB, using a custom BIOS to disable one of its memory channels, reducing its VRAM to 20GB but stabilizing it in the process.
In a mutually beneficial deal, Intel repurchases 49% of Fab 34 from Apollo for $14.2 billion, reducing pressure on its margins, but paying a hefty $3 billion premium to the financial company.
The global semiconductor foundry market generated a record $320 billion in revenue in 2025, growing 16% year-over-year, according to Counterpoint Research's Foundry Market Supply Tracker.
FamilyFeed is a shared organizer where families manage shopping, medicines, travel lists, appointments, todos, and event plans in one place. Smart bots monitor this family data and generate helpful reminders and insights, like medicines running out, tasks needing attention, or upcoming events. The app is designed for the whole family, from kids to grandparents. It includes AI features like image scanning to create items quickly, smart tools such as cloning lists or event plans, and unique layouts like Checkout View for shopping lists and Chat View for appointments.
Meta-owned messaging platform WhatsApp said it alerted about 200 users who were tricked into installing a bogus version of its iOS app that was infected withΒ spyware.
According to reports from ItalianΒ newspaper La Repubblica and newsΒ agency ANSA, the vast majority of the targets are located in Italy. It's assessed that the threat actors behind the activity used social engineering
Tom Clancy's The Division Resurgence executive producer Fabrice Navrez is "very satisfied" with the mobile game's touch controls, but says players can choose to play with a controller if they prefer.
The LG Gram Book is a reasonably competent middle-of-the-road laptop, good for students or office workers, but it simply doesn't do enough to justify its midrange price tag.
I asked ChatGPT how to feel less worn out by the end of the week, and its surprisingly practical advice on food, sleep, and future plans actually helped
Gigabyte Technology Co. Ltd, a leading manufacturer of motherboards, graphics cards, and hardware solutions, today marks a defining moment in computing aesthetics with the introduction of the X870E AERO X3D DARK WOODβa groundbreaking motherboard that transcends technological achievement to become a true design statement. Building on the acclaimed success of the X870E AERO X3D WOOD, the X870E AERO X3D DARK WOOD carries that legacy boldly forward, deepening the experience into something more immersive and emotionally resonant.
Guided by the philosophy of Technology with Warmth, the X870E AERO X3D DARK WOOD envelops the user in a quiet, grounded mood that feels less like a hardware upgrade and more like coming home. The authentic dark wood texture finish brings natural warmth and character into high-performance computing; the supple leather pull tab adds tactile intimacy; and the understated dark metal tones offer calm and breathing roomβa deliberate counterpoint to the noise of modern life.
Alphacool International GmbH from Braunschweig is a pioneer in PC water-cooling technology. With one of the most comprehensive product portfolios in the industry and over 20 years of experience, Alphacool is once again expanding its portfolio with the long-awaited Apex Monoblocks. The monoblocks combine high cooling performance with an elegant design that is perfectly matched to the respective motherboard. In addition to the CPU, they also cool the voltage regulators (VRMs) and the M.2 NVMe SSD located below the CPU socket. This allows the waterblock to cover all key motherboard components.
As with the Apex 1 CPU cooler, the Apex Monoblocks feature an offset cold plate. This directs the coolant flow straight to the thermal hotspot of the AM5 CPU. Combined with the proven Cross-Slot structure and 3D Jetplate 2.0, the design generates high water pressure for particularly efficient heat dissipation. The Apex Monoblocks were developed for demanding systems that require both high cooling performance and seamless integration into the motherboard design. Their combination of technical precision, targeted coolant flow and clear design language makes them ideally suited to modern custom water-cooling setups.
Across those five decades, Apple has repeatedly redefined what a personal computer looks like, from beige boxes and Bondi Blue CRTs to fanless ultraportables and super powerful handhelds.
Raspberry Pi prices for various models have increased yet again, with the flagship 500+ 16GB model increasing by nearly 50% thanks to the DRAM shortage.
Pet Video is an AI pet video generator that turns your pet photos into shareable short videos in seconds. Upload a cat or dog photo, choose from over 50 trending styles like Woolen Pet or KβPop Dance, and instantly download HD results ready for TikTok, Instagram Reels, or YouTube Shorts. It includes dedicated cat and dog generators, effortless sharing, and a gallery of viral effects to inspire your next post.
Glowlytics is an AI-powered skin health tracker that helps you understand acne, sun damage, and inflammation with insights grounded in dermatology research. Built by doctors, it analyzes daily images to surface Skin Signals, track scores over time, and deliver evidence-based, privacy-first guidance to support your routine without offering diagnoses.
AppleΒ onΒ Wednesday expanded the availability of iOS 18.7.7Β and iPadOS 18.7.7Β to a broader range of devices to protect users from the risk posed by a recently disclosed exploit kit knownΒ as DarkSword.
"We enabled the availability of iOS 18.7.7Β for more devices on April 1, 2026, so users with Automatic Updates turned on can automatically receive important security
Maxxmod is an upcoming browser extension that upgrades YouTube with 200+ controls that shape how you watch and browse. Configure player behavior, speed, quality, volume, and captions, and use tools like Focus mode, volume booster, screenshots, Picture-in-Picture, and frame-by-frame to fine-tune playback. Clean up search and feeds by hiding Shorts, promos, premieres, and other distractions, all from a centralized admin with live previews.
Sign up at maxxmod.com to get notified at launch.
Maxxmod will offer 150 features on the free plan and 50 more on Pro.
CleanSmart streamlines data cleaning by automatically removing duplicates, standardizing formats, and filling missing values so you can export with confidence. Use SmartMatch for precise merges, SmartFill to predict gaps, AutoFormat to fix capitalization and phone numbers, and LogicGuard to flag outliers and impossible values. The platform encrypts data end-to-end, isolates each customer, and scales to 10,000+ records per minute, letting you upload a file and watch your Clarity Score rise.
Intel's next-generation mobile processor for gaming notebooks and portable workstations, the Core Ultra Series 4 "Nova Lake-HX," will come in two distinct core configurations, according to a leak by Jaykihn, a reliable source for Intel leaks. "Nova Lake-HX" is segmented from the mainstream "Nova Lake-H" with a wider I/O that supports configurations with discrete GPUs. The top-of-the-line "Nova Lake-HX" processor will come with a CPU core configuration of 8P+16E+4LPE, that's eight "Coyote Cove" P-cores, and 16 "Arctic Wolf" E-cores, both of which are upgrades over the current "Cougar Cove" and "Darkmont" core architectures, respectively. The Compute tile features 8P+16E cores sharing an L3 cache, while the chip's 4 low-power island E-cores, also based on "Arctic Wolf," will be located in the SoC tile.
Intel is also planning a performance-segment "Nova Lake-HX" core configuration, with 6P+8E+4LPE under the hood. This will likely reuse the 6P+8E Compute tile from the mainstream "Nova Lake-H" processor, but with the SoC + I/O tiles Intel plans to use for the "Nova Lake-HX". This would give the chip a maximum core count of 6P+8E+4LPE. Perhaps the most interesting aspect of both these chip types is the iGPU, and its Graphics tile will be the tiniest variant in the series, with just 2 Xe cores. The iGPU of "Nova Lake" family is based on the Xe4 "Druid" graphics architecture. These chips feature a basic iGPU because they are expected to come with a full fat PCI-Express 5.0 PEG interface for discrete GPUs, and ideally you'd want the iGPU to be as small as possible.
Spectry unifies analytics, AI insights, and A/B testing to help teams understand user behavior and ship improvements faster. Install once to capture heatmaps, session replays, funnels, errors, and feedback without developer effort. Every 12 hours, AI surfaces drop-offs, UX issues, and failures with clear recommendations, then lets you launch experiments directly from the insight. Build dashboards, monitor security and performance, and stay GDPR-compliant with EU-hosted data.
WriteMail.ai is an AI-powered email writer that helps you draft and reply to emails quickly using a web app or a Chrome extension for Gmail. It analyzes your text in real time and suggests clearer wording, tone, and structure while you customize length, style, mood, and emoji. You can create emails in many languages, keep history, and reuse drafts. The Mail Assistant gives instant feedback, and one-click send opens your default client. Plans range from free to pro with higher monthly limits and priority support.
Prominent leaker KeplerL2 recently claimed that Sony's rumored handheld will feature a faster graphics chip than the Xbox Series S. The device, codenamed Canis, is expected to complement the PlayStation 6, which is not expected to arrive before late 2027.
Finch is a marketplace for financial forecasts and research where analysts publish probability estimates, attach Markdown research, and build a verified track record. Every forecast is cryptographically timestamped and locked, then scored with Brier metrics when events resolve.
Your cumulative score powers public rankings and a downloadable, verified history that recruiters and allocators can trust. Monetize your work, connect directly with institutions, and let the market price the value of your intelligence.
Servo is for founders who can't explain why their product is different. It asks the focused questions a $15K strategist would ask, interrogates your product and your brain, then gives you the exact words for your site, pitch, posts, and ads. Words that separate you from your competitors and online noise. $79. Money back guarantee. Built on 20 years of strategy behind Amazon Music, Twitch, Red Bull, and X Games.
You answer questions about your business. Servo's guided session figures out what makes you different and why anyone should care. You walk away with one-liners, your story, how your brand should sound, what to post, and where to start. So your homepage, pitch, and ads finally land.
Diven turns your curiosity into knowledge by delivering surprising facts, clear explanations, and detailed articles on topics you chooseβno prompts required. Open the app, browse suggested subjects, and get concise, engaging content in seconds, complete with tailored images and share-ready summaries. Create an account in 30 seconds and start with free credits. Learn and share in multiple languages, and use Diven to fuel conversations, broaden your worldview, and keep discoveries organized.
Although a new report found that about 4.7 million teen social media accounts were removed, deactivated or restricted, children still had access to the apps.
This quantum device absorbs energy efficiently, accelerates charging with scale, and stores power temporarily, though it's still limited in practical output.
Blockchain trackers put the cryptocurrency heist in the hundreds of millions of dollars and is already on track to be the largest crypto theft in 2026 so far.
AnyToURL turns any file into a short, shareable link in seconds. Drag and drop to upload, then share an instant URL with browser previews for images, PDFs, and documents. Files are delivered over a global edge network for fast access worldwide. Add password protection, keep files temporary or make them permanent on paid plans, and manage uploads via API or CLI with custom domains and branding, supporting sizes up to 10GB.
NanoMaker AI is an all-in-one creative platform that lets you generate and edit images using Nano banana AI, videos, music, and voice with the world's top AI models under one subscription. Work in a seamless workflow: turn an image into a video, add background music, and export without switching tools. Use prompt-based editing, background removal, lighting control, and style transfer to produce consistent, professional results for marketing, content creation, education, and e-commerce.
People are going gaga over Apple's Lil Finder Guy, who arrived just in time for Apple's 50th Anniversary, but there's more to him than just a cute face.
Delve faces new allegations that it violated the open source license of its customer, Sim.ai, by taking the customers's tool and passing it off as its own.
Sonder profiles are completely unstructured, encouraging users to build something that looks like a mood board or a digital collage. Think MySpace rather than LinkedIn.
The Alienware Aurora Gaming Desktop, a top-of-the-line PC built for 1440p gaming, is currently enjoying a 23% discount as part of Dell's Spring Sales event.
Dell's Spring Sale is underway, and it's brought a 22% discount to one of our favorite mid-range gaming laptops with an RTX 5060 GPU and 32GB of RAM, the Alienware 16X Aurora.
The Early Access Program invites researchers to design and propose quantum experiments that push the boundaries of what current hardware can achieve. It is a selective program β the processor will not be publicly available β and Google is setting firm deadlines for participation. Research teams have until May 15,...
Embed AI into your app or site in just 3 lines of code. Normally, building AI into mature apps or websites requires dealing with vector databases, custom integration pipelines, authentication, and brittle LLM calls, which distract core engineering teams from shipping product features. EmbedAI solves this by providing a drop-in component that abstracts away infrastructure, letting you inject AI into your app logic without restructuring your backend. It requires zero backend maintenance or database provisioning, offers seamless UI matching your brand rules, and gives you complete control with your own API keys.
Lutily gives salons a branded booking page at yourname.lutily.com that lets clients pick services, choose a staff member, and reserve a real open slot in under a minute. It never shows competitors, charges no commission, and has no perβstaff fees. Every booking is phoneβverified to reduce noβshows.
Use Lutily to stack appointments to fill gaps, run a smart waitlist that autoβoffers newly opened times, and control working hours by date. Manage a colorβcoded calendar, team permissions, client history and notes, and automatic SMS confirmations and reminders, with instant rescheduling and cancellations.
Researchers examined millions of webpages and found thousands of exposed API credentials, revealing persistent security gaps across cloud services and development environments.
Samsung's new 2026 TVs have been released, which means you can score massive discounts on existing models, and I've rounded up the 12 best deals from $249.99.
HP repackages Humane AIβs failed technology into HP IQ, combining on-device intelligence, spatial awareness, and IT control for workplace collaboration.
Memory deals are more important than ever with the current RAM pricing surges, and Woot! is currently home to today's best deal. It expires tonight (if not sooner), so don't hold out too long.
Minecraftβs latest Bedrock Edition preview shows off sulfur caves, springs, and new blocks for Bedrock Edition, while Java Edition players can try the Herdcraft April Fools snapshot that reimagines inventory entirely.
Testing Speechifyβs new Windows app proves voice typing is faster than a keyboard, though AI quirks and a $29 monthly fee remain significant hurdles.
In its MLPerf Inference 6.0 submission, AMD did not simply revisit familiar benchmarks with a faster GPU. It expanded into first-time workloads, crossed the 1-million-tokens-per-second threshold at multinode scale and showed that partners can reproduce the results across a broader ecosystem. That combination matters because our customers no longer evaluate inference platforms on one metric alone. They want competitive single-node performance, efficient scale-out, faster bring-up on new models, reproducible results across partner systems and confidence that the software stack can keep pace. MLPerf Inference 6.0 let us show all of that in one submission.
Just as important, we showed that these results are not isolated. A broad partner ecosystem submitted across four AMD Instinct GPU types that closely reproduced numbers submitted by AMD and the first three-GPU heterogeneous MLPerf submission demonstrated that AMD hardware and AMD ROCm software can orchestrate meaningful inference throughput even across systems in different geographies.
The US government has selected BlackSky to design and build the next generation of its space surveillance capabilities. The newly announced contract is an indefinite delivery/indefinite quantity (IDIQ) agreement, meaning the company will provide as many satellites and monitoring services as the Air Force Research Laboratory requires for its missions....
Experience the pinnacle of gaming technology with GCS Cheats, the industryβs leading provider of state-of-the-art gameplay modifications. It features the most intuitive interface and the lightest system footprint in its class, offering a powerful and easy-to-use level of customization. Every tool is designed for maximum stability, providing seamless integration into todayβs biggest games.
The Computer Emergency Response Team of Ukraine (CERT-UA) has disclosed details of a new phishing campaign in which the cybersecurity agency itself was impersonated to distribute a remote administration tool known as AGEWHEEZE.
As part of the attacks, the threat actors, tracked as UAC-0255, sent emails on March 26 and 27, 2026, posing as CERT-UA to distribute a password-protected ZIP archive
The Shark FlexBreeze HydroGo is the best fan around and it's currently at a record low price. Here's why you should buy it now ahead of the summer heat.
Arriving now for Windows 11 Insiders is a buff to Task Manager that provides far more information about your PC's NPU usage and performance. Here's how it works and why it makes sense in 2026.
The tests, conducted by NJ Tech, used identical hardware: an AMD Ryzen 5 5600X paired with a Radeon RX 6700 XT, alongside 16GB of DDR4 memory, a 2TB NVMe SSD, a Corsair RM1000x power supply, and a Gigabyte X570 Aorus Elite motherboard. On the software side, Windows 11 ran AMD's...
Early reviews of the AirPods Max 2 highlight improved sound quality, excellent noise cancellation, and deeper Apple ecosystem integration, powered by the new H2 chip and added smart features.
Formo makes analytics and attribution easy for DeFi apps, so you can focus on growth. Understand who your users are, where they come from, and what they do onchain. Measure what matters and drive growth onchain with the data platform for onchain apps. Get the best of web, product, and onchain analytics on one versatile platform.
Lifeplanr visualizes your entire life as 4,680 weeks and lets you plan, journal, map travel, and track finances with a built-in FIRE calculator. You can see life phases at a glance, tag moods, attach photos, and scratch off countries youβve visited.
Install it as a PWA on any device, switch between 10 themes, and use it offline. Your data stays on your device by default, with optional Pro cloud sync and easy export.
Google Ads added an auto-apply setting to experiments. Itβs on by default, so winning variants can go live without review.
How it works. You choose directional results (default) or statistical significance at 80%, 85%, or 95% confidence. One safeguard: if your chosen success metric performs significantly worse in the test arm, the change wonβt auto-apply.
Why we care. Experiments are one of the most powerful tools in your account. Automating apply can speed testing, but removes a checkpoint where you catch unintended consequences before they hit live campaigns.
The catch. Experiments allow only two success metrics. A third metric you care about β one you didnβt or couldnβt select β can decline unnoticed. Guardrails protect what you told Google to watch, not everything that matters.
Bottom line. Auto-apply is a reasonable shortcut for simple tests. For anything consequential, keep manual review. Run the experiment, reach significance, then review full data before you apply changes.
First seen. Google Ads specialist Bob Meijer shared this update on LinkedIn.
Bing appears to be testing an expanded sponsored products section in its shopping results, featuring a double-row carousel that takes up significantly more space than the current format.
The test. The format pairs a large, double-row sponsored carousel with organic cards from individual sites below.
Why we care. If this rolls out broadly, it means more screen space for sponsored products β typically leading to higher visibility and more clicks if you run Microsoft Shopping campaigns. The double-row carousel is also more visually competitive, bringing Bingβs shopping ads closer to Google Shoppingβs prominence.
The catch. The test appears limited β not all users see it. Search industry veteran Mordy Oberstein reported a more compact layout, suggesting Bing is still in early testing.
Bottom line. Bing runs many SERP experiments that never fully launch, so watch this one for now. If you run Microsoft Shopping campaigns, monitor impressions for any lift if the format expands.
First spotted. Sachin Patel shared a screenshot of the test on X.
SEO tools were the most replaced martech application in 2025 β but not for the reason you might expect.
According to the 2025 MarTech Replacement Survey, SEO platforms topped the list of replaced tools for the first time, overtaking categories like marketing automation platforms (MAPs), which had led for the past five years.
At first glance, that might suggest instability in SEO. After all, the discipline is being reshaped by LLMs, AI-generated answers, and the rise of zero-click search experiences β all of which challenge traditional keyword tracking and ranking-based workflows.
But the data tells a more nuanced story.
SEO tools: most replaced, but stabilizing
Even though SEO tools were the most replaced category in 2025, they were replaced at a slower rate than in prior years.
In other words, theyβre now the most commonly replaced β but also more stable than before.
That shift suggests a maturing category. Rather than widespread churn, you appear to be consolidating, upgrading, or refining your SEO stack as search evolves.
Meanwhile, several other major martech categories saw sharper year-over-year declines in replacements:
CRM replacements fell more than 12% from 2024 to 2025, reaching their lowest level in the surveyβs history.
MAPs, email platforms, and CMS tools also declined compared to 2024.
Why SEO tools are being replaced
So if SEO tools arenβt being swapped out due to instability, whatβs driving the changes?
The survey points to three primary factors:
1. AI capabilities
For the first time, the survey asked about AIβs role in replacement decisions β and the impact was significant.
37.1% cited AI capabilities as an important factor.
33.9% said they wanted AI capabilities when replacing a tool.
This reflects a broader shift in SEO tooling, with platforms rapidly integrating AI for:
Content generation and optimization.
SERP analysis and intent modeling.
Workflow automation.
In many cases, replacing your SEO tool isnβt about abandoning SEO β itβs about upgrading to AI-native capabilities.
2. Cost pressures
Cost has become a major driver of martech replacement decisions, including SEO tools:
43.8% of marketers cited cost reduction as a reason for replacing an application in 2025.
Thatβs up sharply from 23% in 2024 and 22% in 2023.
This suggests growing pressure to optimize and rationalize your SEO tech stack, especially as you evaluate overlapping functionality across tools.
3. Changing needs in a shifting search landscape
As search behavior changes, so do expectations for SEO platforms.
Traditional rank tracking and keyword monitoring are no longer sufficient on their own. Teams are increasingly looking for tools that can:
Surface insights across AI-driven SERPs
Track visibility beyond clicks
Integrate with broader marketing and data systems
That evolution is likely contributing to replacement activity β even as overall stability increases.
AI is reviving custom-built SEO tools
One of the more notable trends in the 2025 survey is the resurgence of homegrown solutions, including for SEO workflows.
Replacing commercial martech tools with homegrown applications accounted for:
8.1% of replacements in 2025
Up from 3.4% in 2024 and 5% in 2023
This marks a meaningful shift after years of near-total reliance on commercial platforms.
βAI-assisted coding is changing the calculus of build vs. buy,β said martech analyst Scott Brinker. βItβs easier and faster to build than ever before. Companies should still buy applications where they have no comparative advantage. But in cases where they can tailor capabilities to differentiate their operations or customer experience, custom-built software is an increasingly attractive option.β
For SEO teams, this could mean more organizations building:
Custom data pipelines.
Proprietary SERP tracking systems.
AI-driven analysis tools tailored to their specific needs.
Other martech categories show even greater stability
While SEO tools led in total replacements, the broader martech landscape is becoming more stable.
Several major categories saw declining replacement rates in 2025, including:
CRM platforms (down more than 12% year over year)
Marketing automation platforms
Email distribution tools
Content management systems
This suggests that many organizations are settling into core systems while selectively updating areas β like SEO β that are changing faster.
Methodology
Invitations to take the 2025 MarTech Replacement Survey were distributed via email, website, and social media in Q4 2025.
A total of 207 marketers responded. Findings are based on the 154 respondents (60%) who said they had replaced a martech application in the previous 12 months.
Hop on over to Amazon right now and you can score some great tech deals in its Easter sale β I've picked out the 22 best offers with prices starting at Β£9.98.
It's Apple's 50th anniversary today, and while its employees are being treated to a Paul McCartney concert, you can treat yourself to today's best Apple deals from $15.99.
The Meta-owned company said it identified around 200 users who were tricked into installing a fake version of WhatsApp that was actually Italian-made spyware.
Sony hopes to deliver a better-than-Xbox Series S experience on its next-gen handheld with next-gen upscaling According to the leaker KeplerL2, Sonyβs next-generation PlayStation 6 Handheld should feature a GPU that surpasses the Xbox Series S and Nintendo Switch 2. In fact, the leaker thinks that the systemβs GPU will be βa bit ahead of [β¦]
EK by LM TEK, is proud to introduce the EK-Quantum VectorΒ³ TUF RTX 5070 Ti/5080 - Plexi, a high-performance full-cover water block compatible with both the ASUS TUF Gaming GeForce RTX 5080 and the ASUS TUF Gaming GeForce RTX 5070 Ti. Designed to deliver exceptional thermal performance across the GPU core, VRAM, and power stages, the EK-Quantum VectorΒ³ TUF RTX 5070 Ti / 5080 features an optimized open split-flow cooling engine, next-gen pre-cut thermal pads,and a full-coverage black anodized aluminium backplate. Now available now at the EK Shop and local resellers.
Engineered for the ASUS TUF Gaming GeForce RTX 5080 and the ASUS TUF Gaming GeForce RTX 5070 Ti, the EK-Quantum VectorΒ³ delivers high performance liquid-cooling for enthusiasts who demand more. Featuring EK's expanded next-gen cooling engine, pre-cut high-performance thermal pads, and an advanced gasket design, this block ensures your GPU stays cool and efficient - even under the heaviest gaming, rendering, or overclocking loads.
Google officially announced its new anti-ransomware protections in September 2025, and the company is now making these tools available to Workspace customers using Google Drive for Desktop. The security features leverage a specially trained AI model, which has been further developed and refined over the past few months.
A diving accident at age 16 left Buckwalter paralyzed from the chest down. In 2024, he enrolled in a Caltech brain-computer interface study and underwent a craniotomy to have six Blackrock Neurotech chips implanted in his motor cortex.
Local chip makers take 41% of the Chinese AI semiconductor market, while Nvidia's market share falls to 55% β down from the company's claimed high of 95%.
The Nvidia App can now automatically recompile shaders for you in the background after every GPU driver update. This should save gamers several minutes across different titles, especially blockbuster ones, where shader compilation can often delay your session. You still need to compile shaders for the first time after a new install, however.
AI-powered ad bidding systems are highly sophisticated, but conversion tracking hasnβt kept pace. Ad platforms encourage advertisers to track more actions, while many experts argue for tracking only final outcomes.
Both are partly true. Neither is universally correct.
In practice, both over- and under-signaling can hurt PPC performance. Too many loosely defined micro-conversions introduce noise. Bidding shifts toward easy, low-value actions, inflating reported performance while eroding real results. Too few signals leave the system without enough data to learn.
This dynamic is most visible in Performance Max and Search plus PMax setups, where the system optimizes toward whatever signals itβs given β regardless of whether they reflect real business value.
Hereβs what happens when micro-conversions outnumber real conversions, why bidding systems behave this way, and how to build a conversion framework that aligns signal volume with business impact.
The myth of the βdata-hungryβ PPC algorithm
The idea that algorithms need as much data as possible has been repeated so often that itβs become an assumption. Platform documentation, automated recommendations, and many PPC blog posts reinforce the same message: more signals equal better learning.
Bidding systems require a minimum level of signal density to function, but they donβt benefit from indiscriminate micro-conversion signals. More data isnβt always better data.
Adding low-intent or loosely correlated actions often degrades performance by shifting optimization toward behaviors that donβt correlate with revenue.
Machine learning systems donβt evaluate the strategic relevance of a signal. They evaluate frequency, consistency, and predictability.
When an account includes a mix of high- and low-intent micro-conversions β purchases, add-to-carts, pageviews, video plays, and soft leads β the system doesnβt inherently understand which actions matter most to the business.
Without a clear value hierarchy, the bidding algorithm treats all signals as valid optimization targets. This creates a structural bias toward high-frequency, low-value actions because theyβre easier and cheaper to achieve. The result is a bidding pattern that maximizes conversion volume while minimizing business impact.
Why value-based bidding helps, but canβt fix everything
Many practitioners advocate for value-based bidding, where each micro-conversion is assigned a relative financial or hierarchical value. In theory, this helps the system understand which signals matter most. You can also instruct the platform to maximize conversion value, which should push the algorithm toward higher-value purchases or sales-qualified leads (SQLs).
But value-based bidding isnβt a complete solution. When too many micro-conversions are included β even with assigned values β the system can still become overwhelmed. A high volume of low-intent signals can dilute intent and distort the value hierarchy.
The issue isnβt just a lack of context.
Every signal becomes part of the optimization math. If the model weighs signals by volume rather than business importance, low-intent micro-conversions will dominate. Assigning values helps clarify priorities, but it canβt override signal imbalance. At a certain point, the math wins.
How PPC bidding follows the path of least resistance
In practice, this shows up as a βpath of least resistanceβ problem.
Even with values assigned, bidding algorithms still optimize toward the signals theyβre given. When low-intent micro-conversions are included as Primary actions, the system treats them as efficient ways to increase conversion volume. This isnβt an error. Itβs expected behavior for a model designed to maximize conversions within a set budget.
When those signals occur more frequently, the system gravitates toward them. A signal that fires hundreds of times a day will exert more influence than a high-value action that fires only a handful of times per week.
This dynamic is especially visible in PMax. The system evaluates signals across channels, audiences, and placements, and pursues the cheapest, most abundant path to conversion. If a contact page visit or key pageview is treated as a Primary signal, PMax may prioritize it over a purchase or SQL because itβs easier to achieve at scale.
Thatβs why PMax often reports strong conversion volume and low CPA while revenue remains flat or declines. The system is performing as instructed, but the inputs lack a disciplined signal hierarchy. Value-based bidding improves structure, but without restraint in the number and type of signals, it canβt fully prevent the problem.
When low-value actions are tracked as Primary conversions, platform-reported performance becomes disconnected from business outcomes. Metrics such as CPA, ROAS, and conversion rate may improve, but those gains are often illusory.
For example:
A campaign may show a 40% reduction in CPA because the system is optimizing toward pageviews rather than purchases.
ROAS may increase because the system attributes inflated value to actions that donβt correlate with revenue.
Conversion volume may spike due to high-frequency micro-conversions.
These patterns create a false sense of success, leading advertisers to scale budgets prematurely and erode contribution margin.
Diluted intent and double-counting
When multiple micro-conversions are tracked as Primary, a single user journey can generate multiple wins for the algorithm.Β
For example, a user who views a product page, signs up for a newsletter, and adds an item to cart may be counted as three conversions from a single click. If values are assigned to each step, conversion value and ROAS become inflated as well.
This inflates conversion volume, inflates conversion value, and distorts bidding behavior. The system interprets this as a high-value user and begins overbidding on similar traffic, even if the user never completes a purchase.
In many accounts, micro-conversions outnumber real conversions by a ratio of 500 to 1 or more. This imbalance has significant implications for bidding behavior.
When frequency overwhelms value
If an account records 500 pageviews, 200 add-to-carts, 50 lead form starts, 10 purchases, and all actions are treated as Primary, the system receives 760 signals for every 10 that actually matter.
Without distinct values, the algorithm canβt differentiate between a $0.05 action and a $500 action. It optimizes toward the most frequent signals because they provide the clearest path to increasing conversion volume.
Even when values are assigned, overvaluing micro-conversions teaches the algorithm to pursue easy wins. The result is a maximized conversion value metric that looks strong in the dashboard but isnβt reflected in actual sales.
The consequences of signal imbalance
When micro-conversions dominate the signal mix:
Bidding shifts toward low-intent traffic because it produces more conversions.
Budgets are allocated inefficiently as the system chases cheap signals.
Real ROAS declines, even as platform-reported ROAS appears strong.
Scaling becomes risky because the system is optimizing toward the wrong outcomes.
Thatβs why accounts with high micro-conversion volume often show strong platform metrics but weak financial performance.
When microβconversions stop helping
Micro-conversions are useful when an account lacks enough real conversion volume to support stable bidding. However, once a campaign consistently reaches 30 to 60 real conversions per month, they no longer provide meaningful benefit.
At that point, the system has enough high-quality data to optimize effectively. Continuing to rely on micro-conversions introduces unnecessary noise and increases the risk of misaligned bidding.
This is the point to transition from tCPA to tROAS and let real revenue guide optimization.
Primary actions influence bidding, while Secondary actions provide visibility without affecting optimization. This four-part litmus test helps determine which actions should be treated as Primary.
1. The volume threshold
Micro-conversions should be used only when real conversion volume isnβt sufficient to support stable bidding. As a general guideline:
Below 30 real conversions per month: A high-intent micro-conversion may be needed to give the system enough data.
30 to 60 real conversions per month: Begin reducing reliance on micro-conversions.
60 or more real conversions per month: Remove micro-conversions from Primary status and rely on revenue-based optimization.
This threshold ensures micro-conversions serve as a temporary bridge, not a permanent crutch.
2. The necessary step test
A Primary action should represent a required step in the conversion journey, such as:
Add to cart.
Begin checkout.
Start lead form.
Actions that arenβt required steps β such as contact page visits, whitepaper downloads, or time on site β shouldnβt be treated as Primary. These may indicate interest, but they donβt reliably predict revenue.
3. The valuation test
If an action canβt be assigned a realistic financial value, it shouldnβt be used as a Primary conversion. Assigning arbitrary values introduces risk and can distort bidding behavior.
Actions such as time on site or scroll depth fail this test because they donβt consistently correlate with revenue. However, if CRM data shows a reliable statistical correlation with revenue, that can justify including the action.
4. The simplicity test
Even if multiple actions pass the first three tests, only the strongest one or two should be designated as Primary. Including too many Primary actions increases the risk of double-counting and overbidding.
A streamlined Primary set ensures the system focuses on the most meaningful signals.
Use Secondary conversions as a diagnostic tool
Secondary conversions provide visibility into user behavior without influencing bidding. Theyβre a useful diagnostic tool for understanding funnel performance and evaluating new signals.
Visibility without optimization risk
Tracking actions such as newsletter signups, video views, or soft leads as Secondary lets you monitor engagement without shifting bidding toward low-value behaviors.
This approach preserves data integrity while maintaining control over optimization.
Funnel analysis and bottleneck identification
Secondary conversions reveal where users drop off in the funnel. For example:
High Add-to-Cart volume but low purchase volume indicates checkout friction.
High MQL volume but low SQL volume suggests targeting or qualification issues.
These insights support more informed optimization decisions.
Safe testing environment
New signals should be tracked as Secondary for several weeks before being considered for Primary status. This allows you to evaluate frequency, correlation with revenue, stability, and predictive value.
Only signals that demonstrate consistent value should be promoted to Primary.
Assign micro-conversion values using a safety discount
When micro-conversions are used, they must be assigned values that reflect their true contribution to revenue. Overvaluing micro-conversions is a common cause of inflated platform performance and misaligned bidding.
Calculating baseline value
The baseline value of a micro-conversion is determined by:
Baseline value = Conversion rate to sale x Average order value (AOV) or profit
For example:
Ecommerce: If 25% of add-to-carts convert and AOV is $1,600, the baseline value is $400.
Lead generation: If 10% of demo requests convert to $5,000 profit, the baseline value is $500.
Applying the 25% safety discount
The baseline value shouldnβt be used directly. Instead, apply a 25% reduction:
$400 becomes $300.
$500 becomes $375.
This discount helps prevent overbidding by ensuring the system doesnβt overvalue micro-conversions relative to actual revenue.
Undervaluing is safer than overvaluing
Undervaluing micro-conversions may slightly slow learning, but it doesnβt distort bidding. Overvaluing them can push the system toward low-intent traffic, leading to rapid budget misallocation.
The safety discount provides a buffer that protects contribution margin while still supplying useful data.
Where PPC experts draw the line on micro-conversions
Practitioners consistently point to the same principle: signal discipline matters more than signal volume.
Julie Friedman Bacchini emphasizes that every conversion action becomes a signal the system optimizes toward. Using more than one Primary action introduces ambiguity β βitβs suddenly muddierβ β and skipping values makes it easier for the system to latch onto lower-value signals. Values donβt need to be exact, but they must be relative.
She also notes that micro-conversions can help low-volume campaigns reach data thresholds, but they arenβt a substitute for real Primary conversions. Removing them later can mean βstarting over to a large extent on system learning.β
Jordan Brunelle takes a similarly disciplined approach: βThere can definitely be too many.β He recommends starting with one strong signal of intent and watching the ratio between micro-conversions and real outcomes. If volume is high but outcomes are low, it often signals a targeting or signal issue.
Signal discipline is the real competitive advantage
The debate around micro-conversions often focuses on quantity. But the real differentiator isnβt volume, but discipline.
Bidding systems optimize toward the signals theyβre given. When the signal mix is cluttered, performance drifts. When itβs clear and intentional, the system aligns with real business outcomes.
Micro-conversions should be selectively used and continuously evaluated. Start with a simple audit:
Identify all Primary conversions.
If more than two or three actions are Primary, the account is likely over-signaled.
Apply the litmus test.
Remove any Primary actions that fail the volume, necessary step, valuation, or simplicity tests.
Move nonessential actions to Secondary.
Assign conservative values to remaining micro-conversions.
Use the safety discount to avoid overbidding.
Monitor performance for 30 days, focusing on revenue, contribution margin, and signal distribution.
Micro-conversions should be a temporary bridge. Once real conversion volume is sufficient, optimization should be guided by revenue. A disciplined signal architecture gives automation what it needs to perform as intended: efficient, predictable, and aligned with real business outcomes.
If youβre a lawyer, college administrator, or financial services provider, youβve likely seen the frustrating βEligible (Limited)β status in your Google Ads account. It can feel like youβre fighting Google with one hand tied behind your back when your remarketing lists, exact match keywords, and more donβt work as intended.
While it might feel like Google Ads is out to get you when you operate in a so-called βsensitive interest category,β there are specific reasons for these rules. More importantly, there are specific ways to succeed despite them.
This article will cover what the personalized advertising policies are, what they mean for your account, and five specific tactics you can use to succeed with Google Ads.
Why does Google have personalized advertising policies?
Google provides detailed explanations in its official policy documentation, but it comes down to two things: legal requirements and ethical standards.
In the United States, for example, the Fair Housing Act and employment laws prevent discrimination based on age, gender, or location. If youβre advertising a job opening or a new apartment complex, Google canβt allow you to exclude people based on those demographics because doing so would be against the law.
Then thereβs the ethical side. Imagine youβre running a rehab center. If someone visits your site, Googleβs βsensitive interestβ policy prevents you from following them around the internet with targeted banner ads like, βStill struggling with addiction? Come to our clinic.β
That kind of remarketing is intrusive and, frankly, predatory when it targets someoneβs health and struggles. To protect the user experience and maintain a sense of privacy, Google limits how personal data can be used in these high-stakes industries.
What canβt you do in a sensitive interest category?
If you fall into one of these categories β housing, employment, credit, healthcare, or legal services β the biggest impact is usually on your audience targeting.
Hereβs what you canβt use:
Website or App Remarketing Lists, including the Google-engaged audience: You canβt target people who have previously visited your website or used your app.
Customer Match: You canβt upload your own email lists or phone numbers to target existing clients.
YouTube Audiences: You canβt target people based on how theyβve interacted with your videos.
Custom Segments: You arenβt allowed to build specialized audiences based on specific search terms or types of websites people visit
For certain categories in certain countries, like housing, credit, and employment in the United States, thereβs further βdemographic strippingβ β you canβt target by age, gender, parental status, or ZIP code. Your Smart Bidding strategies wonβt use these signals as inputs either.
The good news: What can you do in a sensitive interest category?
Itβs easy to focus on whatβs gone, but what still works is a much longer list. Even in a restricted industry, you still have access to the core engine of Google Ads. You can still use:
Keywords, feeds, and keywordless technology: These rely on intent (queries) rather than identity, so they are perfectly fine in Search, Shopping, and Performance Max.
Googleβs audiences: Affinities, In-Market, Detailed demographics, and Life Events segments are still fully at your disposal, where eligible, in Demand Gen, Display, Video, Search, and Shopping.
Optimized targeting: Googleβs AI can still find people likely to convert based on your historical converters, in Demand Gen, Display, and Performance Max.
Content Targeting: You can choose to show your ads on specific keywords, topics, and placements in Display and Video campaigns.
Conversion tracking: Yes, you can still track conversions and use features like Enhanced Conversions, Offline Conversion Import, and Consent Mode. While your internal legal team may have reservations or restrictions around your website tracking, Googleβs Personalized advertising policy doesnβt restrict any conversion tracking.
5 strategies to win in sensitive categories
If you want to move the needle without relying on remarketing, you need to rethink your account structure and messaging. Here are five things you can do right now.
1. The βSeparate Domainβ strategy
If your business offers a mix of services β some sensitive, some not β donβt let the sensitive ones βpoisonβ your whole account. Think of a spa that offers haircuts, pedicures, and Botox. Haircuts are fine; Botox is a medical procedure that triggers sensitive category restrictions.
If you put them all on one site, your entire remarketing capability might get shut down. Consider putting the sensitive service on a separate domain and a separate Google Ads account. This lets you use every available tool for your main business while the sensitive portion operates under the necessary restrictions.
2. Choose Demand Gen over Display
If you want to use image or video ads, use Demand Gen instead of the standard Display Network. In my experience, Demand Gen delivers higher-quality audiences and tends to perform better in restricted niches.
3. Lean into phrase and broad Match
You might be tempted to stick to Exact Match keywords to keep things tight. However, in sensitive categories, Google may restrict ads on very narrow, specific queries for privacy reasons. If your Exact Match keywords arenβt getting impressions, try Phrase or Broad Match. This gives the algorithm more room to find users searching for the same thing with slightly different phrasing that may be less restricted.
Think of it like fishing: if you canβt use a spear, use a net. Youβll catch some fish you donβt want, but that tradeoff helps you catch the ones you do want more easily.
4. Feed the AI with offline conversion tracking
Most businesses in these categories, such as law firms or banks, donβt make sales on their websites. The website generates a lead, and the sale happens over the phone or in an office.
If you want Google to find better users, you must feed that real-world data back into the system. Use Offline Conversion Tracking (OCT) to show Google which leads became customers. Even if you must navigate HIPAA or other privacy regulations, there are ways to do this safely.
Consult your legal team, but donβt skip this step. Itβs the best way to train the algorithm when you canβt use your own audiences and to ensure Smart Bidding works at its full potential.
5. Creative-Led targeting
When you canβt tell Google who to target with a list, you have to tell the user who the ad is for through your creative. Your headlines and images should qualify the lead.
Be specific in your copy. For example, instead of βNeed a Lawyer?β try βDefense Attorney for Small Business.β This attracts your target audience and encourages people who arenβt a fit to scroll past, saving you money and improving your conversion rate.
Running Google Ads in a sensitive category is a challenge, but itβs far from impossible. By shifting your focus from who the person is to what theyβre looking for and how you speak to them, you can still drive incredible results.
This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature, and what you need to know to get the best results from it β all in a quick 3-minute read.
AI has changed how I work after nearly two decades in digital marketing. The shift has been meaningful, freeing up time, reducing the grinding parts of the job, and making some genuinely hard tasks faster.
That doesnβt mean it does the work for you, transforms everything overnight, or saves you 40 hours a week. In real-world SEO, with real clients and real deadlines, itβs a tool that makes parts of the job easier, not something that replaces the work itself.
Here are 20 ways I actually use it. Some are specific to SEO. Some are broader, but relevant to anyone working in the industry. All of them are practical, tested, and honest about their limitations.
Content creation and copywriting
1. Writing first drafts
The single best way to use AI for content is to stop expecting it to produce something publishable and start treating it as a very fast first-draft machine.Β
Feed it your brief, your target keyword, your audience, and your angle. Get a structure back.Β
Then rewrite it in your voice. Add in the expertise that only you know, not a vanilla version of whatβs online.
The content AI produces out of the box is average. Your job is to make it good. Reference real-life stories, case studies, and statistics, and showcase your personal viewpoint and expertise.
The time savings are in not starting from a blank page.
2. Generating meta title and description variations
Give Claude or ChatGPT your target keyword, page topic, and character limits. Ask for 10 variations of your meta title and descriptions. Youβll use one, maybe combine two, but the process takes two minutes instead of 20. For large sites with hundreds of pages, this alone is worth the subscription.
Many tools allow you to upload CSV files, add AIβs suggested ideas, and download them for review. Donβt skip this step. A human eye is where the value sits
3. Refreshing underperforming content
Paste an existing page or blog post that has dropped in rankings. Ask AI to identify whatβs missing, what could be expanded, and what feels outdated.Β
It wonβt always be right, but it gives you a starting point instead of reading the whole thing yourself with fresh eyes you donβt have at 4 p.m. on a Thursday.
Make sure to give context. Long prompts with lots of detail will produce much better results than pasting a page in cold.Β
4. Generating FAQ sections
Prompt AI to generate the 10 most common questions for your target keyword. Cross-reference with People Also Ask and your own research.Β
Answer them, and you now have an FAQ section, featured snippet opportunities, and a content gap analysis in about 10 minutes.
5. Writing alt text at scale
Nobody enjoys writing alt text for 200 product images. Describe the image, give it the context of the page it sits on, and include the target keyword. Then ask for alt text thatβs descriptive and naturally includes the term where relevant. Itβs not glamorous, but itβs necessary and faster.
You can also run a website through Screaming Frog, export it to a CSV file, upload it to your AI of choice, and ask it to write the alt text. This only works well if the file names are descriptive, and again, a human eye is key. This is about increasing speed, rather than handing it over to AI completely.
Not everyone working in SEO has a developer background. AI is useful for:
Translating technical error messages.
Explaining what a server log is telling you.
Helping you understand why a page is excluded from indexing.Β
Paste in the output, ask it to explain it in plain English, and then ask what the fix should be. Verify the answer, but it gets you most of the way there.
7. Writing schema markup
Schema is one of those things everyone knows they should be doing more of, and nobody finds especially enjoyable.Β
Describe the content of your page to your AI of choice, tell it what schema type is relevant (FAQ, Article, LocalBusiness, Product, etc.), and ask it to generate the JSON-LD.Β
Check it in Googleβs Rich Results Test before implementation. This used to take me 20 minutes per page type. Now it takes five.
8. Creating regex for Google Search Console
If you use regex in GSC filters and youβre not a developer, AI is your new best friend. Describe what youβre trying to filter, for example, all URLs containing a specific subfolder, or all queries including a particular term, and ask for the regex string.Β
It gets it right more often than not, and you can ask it to explain the logic so you actually understand what youβre implementing.
9. Analyzing crawl data with prompts
If you export a crawl from Screaming Frog or Sitebulb and youβre not sure what to prioritize, paste the summary data into your AI tool and ask it to help you identify the highest-priority issues based on the siteβs goals.
It wonβt replace your expertise, but itβs a useful sounding board when youβre staring at a spreadsheet with 47 issues and a client call in an hour.
This is one of the most underrated uses of AI in SEO work. You have the data. You have the graphs. What takes time is writing the commentary that explains what happened, why, and what comes next.Β
Feed AI your key metrics and the context of what was happening that month (algorithm updates, campaign launches, seasonality), and ask it to draft the narrative section of your report. Edit it, add your actual insight, but stop writing it from scratch every month.
You can even upload reports from various data sources and ask it to combine and summarize them. This saves me hours every month when Iβm putting together reports.
11. Summarizing long reports for clients
Not every client wants to read a 12-page report. Ask AI to summarize your report into a five-bullet executive summary. Give it to clients at the top of the document.Β
The ones who want details will read on. The ones who donβt will feel informed without asking you to talk them through every chart on the next call.
Ask AI to create the executive summary for someone who doesnβt know anything about SEO, and itβll give you something simple and easy to understand.
12. Identifying anomalies in data
Paste a table of your keyword rankings or traffic data, and ask AI to flag anything that looks unusual, including significant drops, unexpected gains, or patterns that donβt match the previous period.Β
It wonβt replace proper analysis, but itβs a useful first pass when youβre managing a large amount of information and canβt give every dataset the attention it deserves.
List your top three competitors and your own site. Ask AI to help you think through what content topics theyβre likely covering that youβre not, based on their positioning and audience.Β
Then, validate that with actual keyword research tools. AI canβt see competitor data directly, but itβs useful for hypothesis generation before you do the manual work.
14. Understanding a new industry quickly
When you take on a client in an industry you donβt know well, you need to get up to speed fast. Ask your AI to give you a primer on the industry:Β
Key terminology.
The main players.
The buying cycle.
How people typically search for solutions in this space.
What the common pain points are.Β
It saves you an embarrassing amount of time in discovery calls.
15. Identifying search intent mismatches
Paste a list of your target keywords and ask AI to categorize them by search intent: informational, navigational, commercial, and transactional. Then compare that against the page type youβre targeting them with.Β
Youβll almost certainly find mismatches. This is a task thatβs straightforward to describe, but tedious to do manually across hundreds of keywords.
Everyone has had to write a difficult email, whether itβs explaining why rankings have dropped, why a deadline was missed, or why they need to do something you know they donβt want to do.Β
These emails take a disproportionate amount of emotional energy to write. Give your AI the situation, the context, and what you need the client to understand or do, and ask for a draft thatβs clear, professional, and honest.
Edit it. Send it. Move on.
17. Writing SOPs and process documentation
If youβve been meaning to document your processes and just havenβt gotten around to it, AI removes the excuse.Β
Describe a process out loud (or in rough notes), paste it in, and ask for a structured SOP with numbered steps, decision points, and notes.Β
The first version will need editing, but having a framework to work from is the difference between getting it done and it sitting on the to-do list for another quarter.
18. Preparing for client calls
Before a client call, paste in your recent report data, any issues from the previous month, and what you need to cover.Β
Ask your AI to help you structure the agenda and anticipate questions the client might ask based on the data. Youβll go into the call more prepared and less likely to be caught off guard.
Productivity and admin
19. Processing your own thinking
This one sounds vague, but itβs one of the ways I use AI most.Β
When I have a problem I canβt get clear on, a strategy decision Iβm going back and forth on, or a piece of work I canβt find the right angle for, I talk it through with Claude (my AI buddy of choice) to clarify my own thinking. It asks questions, reflects things back, and helps me arrive at a point of view faster than I would staring at a blank document.
Ask your AI to be brutally honest with you. Otherwise, itβll just keep agreeing with you and telling you that youβre truly an expert on every topic.
20. Building prompts you actually reuse
The biggest productivity gain from AI isnβt any individual use. Itβs building a library of prompts that work for your specific workflow and reusing them consistently.
Every time you get a good result from an AI tool, save the prompt. Over time, you build a system, rather than starting from scratch every time. This is the thing most people skip, and itβs the thing that compounds.
Top tip: In the paid version of many AI tools, you can create projects and have specific instructions for each one. This is invaluable for saving time by not having to include all of this information in every prompt you use.
None of these tips replace the expertise, judgment, and client relationships that make a good SEO professional.
AI doesnβt know the business the way you do. It doesnβt understand the nuance of an industry, the history of an account, or the particular quirks of a contact you deal with regularly.
AI reduces the time spent on tasks that donβt require that expertise, so you have more of it available for the work that does.
Use AI as a tool. Stay skeptical of the hype. And for the love of good search results, edit everything before it goes anywhere near a client.
OpenClaw Skills are modular extensions that tell your AI agent what tools to use and how. Here's how they work, what they're good for, and the risks to consider before you install.
Cybercriminals are impersonating top brands like Meta, Disney, and Spotify in a highly sophisticated new phishing campaign designed to hijack your Facebook account. Here is everything you need to know to stay safe.
The American toy-making giant noted that it was continuing to "implement measures to secure its business operations," suggesting that the hackers may still be in the company's systems.
Early tests of NVIDIA's new DLSS 4.5 features, Dynamic Multi Frame Generation (MFG) and MFG 6X, have shown positive results. It certainly seems like AI-assisted software is the new GPU frontier, and one day that won't be controversial.
Nintendoβs legal crusade against Palworld just hit a massive roadblock in the United States. Following a rare, high-level review by the U.S. Patent and Trademark Office (USPTO), a patent examiner has issued a "non-final" rejection of all 26 claims in Nintendoβs controversial "summon and fight" patent.
Worldwide 300 mm fab equipment spending is expected to increase 18% to $133 billion in 2026 and 14% to $151 billion in 2027, SEMI reported today in its latest 300 mm Fab Outlook. This strong growth reflects surging AI chip demand for data centers and edge devices, as well as the growing commitment to semiconductor self-sufficiency across key regions through localized industrial ecosystems and supply chain restructuring. Looking further out, the report projects investment will continue to increase 3% to $155 billion in 2028 and another 11% to $172 billion in 2029, respectively.
"AI is resetting the scale of semiconductor manufacturing investment," said Ajit Manocha, President and CEO of SEMI. "With global 300 mm fab equipment spending projected to exceed $150 billion in 2027 for the first time, the industry is making historic, sustained commitments to the advanced capacity and resilient supply chains needed to power the AI era."
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