The company applied for lending and payment approval in the hopes of offering its users the ability to βhold balances, receive funds and make payments within βits app,β according to Reuters.
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The feature will launch with βCoachella TVβ in April and offer 24/7 streaming access to archival performances and 2026 festival highlights.
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Updates to the video generation app included higher quality exports, music generation tools and artificial intelligence-powered avatars.
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Uploaded PDFs in a carousel format are more popular on the platform than videos, according to new benchmark data from Socialinsider.
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The update will allow creators to decide when non-followers see their content, which could help align posts with regional engagement times.
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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.
Google is introducing two new inference tiers to the Gemini API, Flex and Priority,
to balance cost and latency.
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.
AI led all cited reasons for U.S. job cuts in March at 25% of the total, according to outplacement firm Challenger, Gray & Christmas.
The post AI Leads All Reasons For U.S. Job Cuts In March, Report Says appeared first on Search Engine Journal.
Gemma 4: our most intelligent open models to date, purpose-built for advanced reasoning and agentic workflows.New AI capabilities are coming to Google Vids, powered by Lyria 3 and Veo 3.1, like high-quality video generation at no cost and more.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:
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.
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 Alex Fletcher shared the data on LinkedIn.
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.
Watch Coachella 2026 live on YouTube starting April 10. Stream stages in 4K, shop exclusive merch and join global watch parties.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.
Customer-obsessed organizations achieved 49% faster profit growth and 51% better customer retention rates than their peers, according to Forrester. The gap between what customers need emotionally and what brands deliver comes down to design.
The strain isnβt only on the customer side.
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:
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:
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.
Dig deeper: How to avoid decision fatigue in SEO
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:
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.
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.
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.
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.
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.
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.
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.
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.
Dig deeper: The secret to work-life harmony in SEO: Setting boundaries
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 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.
Dig deeper: 7 hard truths about measuring AI visibility and GEO performance
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.
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.
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.
Dig deeper: AI Overview citations: Why they donβt drive clicks and what to do
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.
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.
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.
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.
These trends require you to adjust how you think about content structure and intent. Hereβs where to focus:
Dig deeper: Want to beat AI Overviews? Produce unmistakably human 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¦]
The post Linux use hits an all-time-high on Steam, passing 5% user share appeared first on OC3D.
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.
Google and Back Market's new USB kit makes it easy to install ChromeOS Flex, a free OS to revive your PC or Mac and reduce e-waste.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.
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.
When Google has enormous reach into new audiences, treating it purely as a closing channel leaves most of that opportunity untouched.
Dig deeper: Ecommerce PPC: 4 takeaways that shape how campaigns perform
The SEO toolkit you know, plus the AI visibility data you need.
Paid social experience transfers to Google in some ways, but there are four areas where we see the biggest knowledge gaps.
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.
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.
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.
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.
Dig deeper: 7 Google Ads search term filters to cut wasted spend
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.
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.
The instinct among detail-oriented advertisers is to segment everything because it feels like control on the surface.
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.
Dig deeper: How to find and fix the root cause of low conversions
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.
Dig deeper: How each Google Ads bid strategy influences campaign success
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.
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.
Track, optimize, and win in Google and AI search from one platform.
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.
If youβre still in the early stages of making this move, my guide on how to expand from Meta Ads into Google Ads is a practical place to start. If youβve seen early success and are now looking for the next layer of optimization, find out how to avoid getting sucked into Googleβs many automation traps.
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.
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.
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.
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:
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.
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.
Dig deeper: How vibe coding is changing search marketing workflows
You can replicate this entire project with just three things, keeping your monthly cost under $100.
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.
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.
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.
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.
Before you even open Replit, create a simple text document that outlines exactly what you need. This is your blueprint. Include:
Start your conversation with the AI agent by uploading this document. It will serve as the foundation for the entire build.
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:
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.
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.
Dig deeper: How to vibe-code an SEO tool without losing control of your LLM
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.
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.
Dig deeper: Inspiring examples of responsible and realistic vibe coding for SEO
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. |
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.
Dig deeper: AI agents in SEO: A practical workflow walkthrough
This approach isnβt for everyone. Hereβs a simple guide to help you decide.
Build your own if:
Stick with a SaaS tool if:
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¦]
The post The Resident Evil Classic Trilogy has arrived on Steam appeared first on OC3D.
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.
Cloudflare's WordPress competitor EmDash may be the future of CMSs, but there are six reasons why it's not that CMS today.
The post 6 Reasons Why Cloudflareβs EmDash Canβt Compete With WordPress appeared first on Search Engine Journal.
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.
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.
Interactive pixel-level editing and consistent storyboards
5-minute philosophy audio lessons to elevate your spirit
Real-time multi-agent AI that debates itself to find truth.
Build and run your business while you sleep.
Prepare your Mac screen for calls, demos, and captures
Text-to-Speech built for Voice Agents
Speak your prompts into Claude Code
Your friendly screen guide for any task!
A Tamagotchi for your desktop, terminal, and AI agents
Create custom map pins and embed anywhere
Opensource AI note taking app
Agentic AI copilot built for WordPress
Your GitHub contributions as 3D city you can drive through
Automatically turn support messages into product updates
Turn your New Tab into a fast & private Kanban board
Race to solve CAPTCHAs, last player standing wins
Know your gaps. Close them. Before it matters.
Run AI coding agents from your phone
Find creators by what's actually in their content
A new way to interact with AI beyond traditional chats
Turn scattered notes into a connected knowledge graph
Vision-to-code foundation model for real GUI automation
Run 100s of Claude agents in parallel
An essential macos productivity app
The open-source Claude Desktop with any model you want
A pixel pet for your menu bar or MacBook notch w/ Pomodoro
AI Assistant in your pocket
AI agents for modern service delivery
The native macOS sample manager built for every instrument
A more refined video player for Mac
Analyze real estate deals in seconds
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.
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.
The program launched in February for Snap Stars and offers exclusive ways to monetize content, such as subscriber-only Stories.
The emoji-based experience is available globally and mirrors similar engagement efforts from Threads and LinkedIn.
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.
The platformβs AI tool will allow users to βmake conversational queriesβ about in-app content and get in-stream responses.
The annual presentation will be hosted by comedian Trevor Noah and will include a performance by musician Chappell Roan.
While calling its short-form videos Reals was an April Foolβs Day joke, the animosity between the two companies is not.
Β
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.
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.
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.
Google.org expanded our existing collaboration with Highlights for Children.
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.
A recap of AI Literacy Day, including a New York City Public Schools event hosted at Google and updates to Google AI literacy resources.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.
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:
So if SEO tools arenβt being swapped out due to instability, whatβs driving the changes?
The survey points to three primary factors:
For the first time, the survey asked about AIβs role in replacement decisions β and the impact was significant.
This reflects a broader shift in SEO tooling, with platforms rapidly integrating AI for:
In many cases, replacing your SEO tool isnβt about abandoning SEO β itβs about upgrading to AI-native capabilities.
Cost has become a major driver of martech replacement decisions, including SEO tools:
This suggests growing pressure to optimize and rationalize your SEO tech stack, especially as you evaluate overlapping functionality across tools.
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:
That evolution is likely contributing to replacement activity β even as overall stability increases.
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:
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:
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:
This suggests that many organizations are settling into core systems while selectively updating areas β like SEO β that are changing faster.
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.
Download the 2025 MarTech Replacement Survey, no registration required.
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¦]
The post PlayStation 6 Handheld Performance Detailed β Better than Xbox Series S appeared first on OC3D.
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 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.
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.
Dig deeper: In Google Ads automation, everything is a signal in 2026
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:
These patterns create a false sense of success, leading advertisers to scale budgets prematurely and erode contribution margin.
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.
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.
When micro-conversions dominate the signal mix:
Thatβs why accounts with high micro-conversion volume often show strong platform metrics but weak financial performance.
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.
Dig deeper: Why better signals drive paid search performance
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.
Micro-conversions should be used only when real conversion volume isnβt sufficient to support stable bidding. As a general guideline:
This threshold ensures micro-conversions serve as a temporary bridge, not a permanent crutch.
A Primary action should represent a required step in the conversion journey, such as:
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.
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.
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.
Secondary conversions provide visibility into user behavior without influencing bidding. Theyβre a useful diagnostic tool for understanding funnel performance and evaluating new signals.
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.
Secondary conversions reveal where users drop off in the funnel. For example:
These insights support more informed optimization decisions.
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.
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.
The baseline value of a micro-conversion is determined by:
For example:
The baseline value shouldnβt be used directly. Instead, apply a 25% reduction:
This discount helps prevent overbidding by ensuring the system doesnβt overvalue micro-conversions relative to actual revenue.
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.
Dig deeper: How to make automation work for lead gen PPC
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.
Across both perspectives:
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:
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.
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.
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:
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.
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:
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.
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.
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.
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.
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.
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.
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
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.
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
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
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.
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.
Dig deeper: How to use AI for SEO without losing your brand voice
Not everyone working in SEO has a developer background. AI is useful for:
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.
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.
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.
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.
Dig deeper: 6 tactical ways to responsibly use AI for everyday SEO
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.
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.
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.
Dig deeper: How to build AI confidence inside your SEO team
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.
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
It saves you an embarrassing amount of time in discovery calls.
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.
Dig deeper: How to use AI response patterns to build better content
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.
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.
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.
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.
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.
Dig deeper: Why SEO teams need to ask βshould we use AI?β not just βcan we?β
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.
Dig deeper: Could AI eventually make SEO obsolete?
DiagramDeck is a cloud-based diagramming platform that hosts and manages draw.io for your team. Import and export .drawio files, edit together in real time with comments and live cursors, and use AWS, GCP, and Azure shape libraries to design cloud architectures, flowcharts, UML, ER, and network diagrams.
It removes self-hosting overhead with managed uptime, backups, and security, and adds team management, SSO, and compliance such as SOC 2 and GDPR. Use it as a modern alternative to Lucidchart and Visio while keeping the draw.io ecosystem.
SupaSailing is the first operational ERP platform built for nautical professionals including fleet managers, charter companies, brokers, and marinas. These businesses previously used spreadsheets, disconnected tools, and email threads since no integrated system existed for this industry.
Six modules cover crew and fleet management, charter enquiries, brokerage CRM, berth management, refit projects, and ISM compliance. All modules are connected with no duplicate data, providing full operational visibility across the business.
It's easier than ever to get distracted while working on your computer. A quick email check, a Slack ping, one ChatGPT question, and boom, 30 minutes gone. "What was I supposed to be doing?"
Most focus tools either block apps you need or disappear when you switch tabs or apps. Neither works. You need an anchor, not a blocker. Focana keeps one task and a timer always visible on your screen, delivers gentle visual nudges and check-ins to keep you locked in, captures stray thoughts in the Parking Lot so they don't derail your session, and allows you to leave notes for context to pick up where you left off. All with no accounts, no sync, and no cloudβjust a calm companion for busy brains.
Google partnered with the Brazilian government on a satellite imagery map to help protect the countryβs forests.Here are Googleβs latest AI updates from March 2026Get ready for YouTube Brandcast 2026 at Lincoln Center. Host Trevor Noah joins CEO Neal Mohan and a powerhouse lineup of creators to demonstrate why YouTube is the futurβ¦
MSI GPU Safeguard+ finally allows me to trust 12V-2Γ6 GPUs If Iβm honest, Iβm not a fan of 12V-2Γ6 or 12VHPWR. There have been far too many reports of burnt graphics cards or melted power connectors to ignore. If I were to spend many hundreds, or perhaps thousands, on a new graphics card, I want [β¦]
The post MSI GPU Safeguard+ is a game-changing tech for 12V-2Γ6 GPUs appeared first on OC3D.
Barry Adams recently published βGoogle Zero is a Lieβ in his SEO for Google News newsletter, arguing that the narrative of Google traffic disappearing is false and dangerous.
His data backs it up. Similarweb and Graphite data show only a 2.5% decline in Google traffic to top websites globally. Google still accounts for nearly 20% of all web visits.
The widely cited Chartbeat figure showing a 33% decline? Itβs skewed by a handful of large publishers hit by algorithm updates. Publishers who abandon SEO in the face of this panic are making a self-fulfilling prophecy, ceding traffic to competitors who keep optimizing.
Heβs right. And heβs looking at the wrong problem.
Humans are still clicking Google results. What has changed is that a growing share of your visitors isnβt human at all.
Automated traffic surpassed human activity for the first time in a decade, per the 2025 Imperva Bad Bot Report. Bots now account for 51% of all web traffic. Not βsoon.β Not βby 2027.β Now.
That number includes everything from scrapers to brute-force login bots. But the fastest-growing segment is AI crawlers.
AI crawlers now represent 51.69% of all crawler traffic, surpassing traditional search engine crawlers at 34.46%, Cloudflareβs 2025 Year in Review found. AI bot crawling grew more than 15x year over year. Cloudflare observed roughly 50 billion AI crawler requests per day by late 2025.
Akamaiβs data tells a similar story: AI bot activity surged 300% over the past year, with OpenAI alone accounting for 42.4% of all AI bot requests.
So while Adams is correct that human Google traffic hasnβt collapsed, something else is happening on the other side of the server logs.
Cloudflare published crawl-to-referral ratios for AI bots. Look at these numbers.
Anthropicβs ClaudeBot crawls 23,951 pages for every single referral it sends back to a website. OpenAIβs GPTBot: 1,276 to 1. Training now drives nearly 80% of all AI bot activity, up from 72% the year before.
Compare that to traditional Googlebot, which has always operated on a crawl-and-send-traffic-back model. Google crawls your site, indexes it, and sends 831x more visitors than AI systems. The deal was simple: let me read your content, and Iβll send you people who want it.
That deal is fraying even on Googleβs own turf. Queries where Google shows an AI Overview see 58-61% lower organic click-through rates, according to Ahrefs and Seer Interactive studies covering millions of impressions through late 2025.
Googleβs newer AI Mode is worse. Semrush data shows a 93% zero-click rate in those sessions. AI Overviews now trigger on roughly 25-48% of U.S. searches, depending on the dataset, and that number keeps climbing.
And when Googleβs AI features do cite sources, theyβre increasingly citing themselves. Google.com is the No. 1 cited source in 19 of 20 niches, accounting for 17.42% of all citations, an SE Ranking study of over 1.3 million AI Mode citations found. That tripled from 5.7% in June 2025. Add YouTube and other Google properties, and they make up roughly 20% of all AI Mode sources.
So the old deal is being rewritten even by Google. AI crawlers from other companies skip the pretense entirely: let me read your content so I can answer questions about it without ever sending anyone your way.
The bot traffic numbers are already here. The next wave is bigger: AI agents acting on behalf of humans.
In 2024, Gartner predicted that traditional search engine traffic would drop 25% by 2026 as AI chatbots and agents handle queries. That prediction is tracking. Its October 2025 strategic predictions go further: 90% of B2B buying will be AI-agent intermediated by 2028, pushing over $15 trillion in B2B spend through AI agent exchanges.
This isnβt theoretical.
Gartner says 40% of enterprise applications will have task-specific AI agents by the end of 2026, up from less than 5% in 2025. eMarketer projects AI platforms will drive $20.9 billion in retail spending in 2026, nearly 4x 2025 figures.
Think about what that looks like in practice. An AI agent researches vendors for a procurement team. It doesnβt see your hero banner. It doesnβt notice your trust badges. It reads your structured data, compares your specs to those of three competitors, and builds a shortlist.
That βvisitβ might show up in your analytics as a bot hit with a zero-second session duration. Or it might not show up at all.
So what do you optimize for when the visitor is a machine making decisions for a human?
Itβs not the same as traditional SEO. And itβs not the same as the AI Overviews optimization most people are focused on right now. AI Overviews are still Google. Still one search engine, still largely the same ranking infrastructure, still (mostly) one answer format.
Agentic SEO is about being useful to software thatβs pulling from search APIs, crawling directly, and using LLM reasoning to make recommendations. That software doesnβt care about your page layout. It cares about whether it can extract what it needs.
I think a few things start to matter a lot more.
Schema markup has always been a βnice to haveβ for rich snippets. When an AI agent compares your product to three competitors, structured data lets it read your specs without having to guess. Think product schema, FAQ schema, and pricing tables in clean HTML. These go from SEO hygiene to core infrastructure.
Dig deeper: How schema markup fits into AI search β without the hype
AI agents donβt search for βbest CRM for small business.β They ask compound questions: βWhich CRM under $50/user/month integrates with QuickBooks and has a mobile app with offline capability?β If your content only answers the first version, youβre invisible to the second.
A human might not notice your pricing page is 8 months stale. An AI agent cross-referencing your pricing against competitors will flag the discrepancy. Or worse, use the outdated number in its recommendation and cost you the deal.
Blocking AI crawlers feels protective, but it means AI agents canβt recommend you. Allowing them means your content trains models that may never send you traffic. Thereβs no clean answer.
But pretending itβs just a technical setting is a mistake. New IETF standards are emerging to give publishers more granular control, but theyβre not widely adopted yet.
Dig deeper: Technical SEO for generative search: Optimizing for AI agents
Most analytics setups canβt tell the difference between a human visit, a bot crawl, and an AI agent evaluating your site on someoneβs behalf. GA4 filters most bot traffic. Server logs show the raw picture, but take work to parse. Even then, figuring out whether an AI agentβs visit led to an actual sale is basically impossible right now.
This is where the βGoogle Zeroβ framing does real damage.
If youβre only measuring organic sessions from Google, youβre blind to a channel that doesnβt show up in that number. Your traffic could look stable while an AI agent steers $50,000 in annual spend to your competitor because their product schema was more complete.
I donβt think we have good measurement for this yet. Nobody does. But ignoring the problem because Google sessions look fine is like checking your print ad response rate in 2005 and deciding the web wasnβt worth paying attention to.
I donβt have a playbook for this. Itβs too new. But I can tell you what weβre doing at our agency.
The βGoogle Zeroβ argument pits one extreme against another, even as the actual shift is quieter and more important.
The web is becoming a place where the majority of visitors are machines. Some send traffic back. Most donβt. Some of them make purchasing decisions on behalf of humans. That number is growing fast.
The SEOs who do well here wonβt be the ones arguing about whether Google traffic moved 2.5%. Theyβll be the ones who figured out how to be useful to both human visitors and the AI agents acting on their behalf.
Weβve spent 25 years optimizing for how humans find things. Now we need to figure out how machines find things for humans.
Thatβs not Google Zero. We donβt have a name for it yet. But itβs already here.
If you want to go deeper on GEO and agentic SEO, Iβm teaching an SMX Master Class on Generative Engine Optimization on April 14. It covers structured data implementation, AI visibility measurement, content optimization for AI systems, and the practical side of everything in this article.
Nvidia aims to tackle shader stutter with its Auto Shader Compilation Beta Nvidia is taking action against shader compilation stutter. With its new Auto Shader Compilation (ASC) feature, Nvidia are giving gamers the option to rebuild game shaders outside of runtime to deliver a smoother gaming experience. When your PC is idling, it can be [β¦]
The post Nvidia adds βAuto Shader Compilation Betaβ to the Nvidia App appeared first on OC3D.
Gen AI gives us productivity superpowers, but the risk is mental fatigue. Ascenda helps you track how your mind is performing day to day. With a quick daily check-in, it shows patterns in clarity, energy, mood, recovery, focus, and decision load so you can protect your best work.
Built with input from a psychologist, neuroscientist, and engineer-founder with lived experience, Ascenda is a Whoop-like layer for the mind: signals, patterns, and early awareness before stress leads to poor decisions, lost focus, or burnout.
LinkedIn is one of the most powerful platforms for recruiting top-tier talent. Itβs also one of the easiest places to waste budget if campaigns arenβt structured correctly.
Many recruitment campaigns fail because they prioritize visibility over intent. More impressions donβt equal better hires. Broad targeting and generic messaging often lead to an influx of unqualified applicants, driving up cost-per-hire and slowing down hiring timelines.
The most effective LinkedIn recruitment strategies focus on one thing: attracting and converting high-intent candidates while filtering out poor-fit applicants before they ever click. Letβs break down exactly how to do that.
The biggest mistake advertisers make on LinkedIn is targeting based solely on job titles, industries, and years of experience.
While this may generate volume, it rarely produces efficiency. Instead, high-performing campaigns are built around intent-based targeting β reaching candidates who are qualified and more likely to consider a new opportunity.
This requires a layered approach:
By combining these layers, you move beyond βwho they areβ and begin targeting why they might be ready to make a change β which is where real performance gains happen.
Your ad creative isnβt just there to attract attention. It should actively filter your audience. One of the most effective ways to control cost-per-hire is to discourage unqualified candidates from clicking in the first place.
Strong recruitment ads follow a structured approach:
This combination of attraction and exclusion ensures that the candidates who do click on your ads are far more likely to convert.
Dig deeper: LinkedIn Ads on a budget: How one playbook drove sub-$10 CPL
Rather than running a single campaign, high-performing LinkedIn strategies segment audiences based on intent.
These are active job seekers who offer the highest conversion opportunity. Following this structure:
These candidates arenβt actively applying but are open to change.
These are long-term potential candidates to start building your pipeline, with the intent to move them to the middle of the funnel and eventually the bottom of the funnel.
LinkedInβs ad platform can quickly become expensive without proper controls. Start with manual CPC bidding to maintain control, then test automated delivery once performance data is established.
More importantly, optimize for the right metrics. Focus on qualified applications instead of clicks. Track downstream actions, such as interview and hire rates.
Be prepared to make fast decisions. Ads with high click-through rates but low application rates often indicate poor alignment. Ads that generate many applications, but few interviews signal weak pre-qualification.
Efficiency comes from eliminating wasted spend earlier, rather than later. It conserves ad spend and minimizes overlapping audiences and hitting the wrong targets.
Dig deeper: LinkedIn Ads retargeting: How to reach prospects at every funnel stage
A common but costly mistake is sending candidates directly to long, complex application forms. Instead, use a two-step funnel:
This approach sets expectations, filters candidates, and significantly improves application quality β often reducing cost-per-hire by 30-50%.
Not every qualified candidate applies on the first interaction. Retargeting allows you to re-engage high-intent users who have already shown interest.
Build audiences from:
Then serve follow-up messaging such as:
Retargeting campaigns are often the most cost-efficient part of your entire strategy.
Once the fundamentals are in place, there are several advanced tactics that can further improve performance:
Hereβs an example of a successful LinkedIn InMail message that recently drove over 70% high-intent applications for an HVAC sales client:
Message body:
Hi [First Name],
This might be a stretch β but your background in HVAC sales caught my attention.
Weβre hiring experienced sales reps who are tired of unpredictable commissions and weekend-heavy schedules.
This role is built for reps who:
- Have 3+ years in HVAC or home services sales
- Are comfortable running in-home consultations
- Want a more stable, high-earning structure
Whatβs different:
- No weekend appointments
- Pre-qualified, inbound leads (no cold knocking)
- Six-figure earning potential with consistency
That said, this isnβt a fit for entry-level reps or those new to sales.
If youβd be open to a quick 10-minute conversation to see if itβs worth exploring, Iβm happy to share more.
If not, no worries at all β appreciate you taking a look.
β [Name]
Stating upfront the need for βexperienced sales repsβ immediately establishes relevance and increases response rates while reducing irrelevant replies.Β
Focusing on what matters to potential candidates, such as no weekend appointments and compensation structure, speaks to the audienceβs needs versus the companyβs.
Closing the conversation with the reminder that this isnβt an entry-level position weeds out wasted conversations and reduces cost-per-hire.
Dig deeper: LinkedIn Message Ads: Everything you need to know
The most effective LinkedIn recruitment campaigns rely on better strategy.
When you focus on intent-based targeting, pre-qualification within ad creative, funnel segmentation, and conversion optimization, you create a system that attracts the right candidates while minimizing wasted spend.
Ultimately, reducing cost-per-hire is about reaching the right people, at the right time, with the right message.
Napster made digital music feel limitless for the first time, then vanished in lawsuits, rebrands, and sales. Its name faded, but its ideas still shape how the world listens.
FlowCastle is a visual platform for building AI-powered chatbots without code. Use a drag-and-drop editor to design flows, then extend with TypeScript actions, HTTP requests, and integrations like Google Sheets. Launch on Telegram and reuse logic across brands with white-labeling. Accept payments, manage catalogs, and track orders inside the bot. Hand off to humans with live chat, run smart broadcasts, and monitor funnels with goal-based analytics. An AI copilot helps generate flows, write copy, and optimize automations.
HankRing helps you find the best versions of the specific dishes and drinks you crave. Choose your Hanks, see verified, likely, and potential spots on a map, and rate the dishβnever the venueβto build consensus for the community. Browse Top 50 and trending categories, add missing spots, and keep a private journal of every rating and verification. With thousands of curated places preloaded, you can discover great food from day one and plan where to hunt next.
Narratex is a writing workspace for fiction authors that unifies your Story Blueprint, a full editor, and an AI collaborator that remembers context across sessions. It keeps track of your characters, plot threads, settings, and themes so you never need to re-explain your magic system or paste cast lists again.
Start by importing your existing work and building your blueprint, then write with an assistant that's already read everything you've created, keeping you consistent and focused chapter to chapter.
See, verify, and govern every agent action.
Trigger macros with rhythmic taps on your trackpad or mouse
Google's John Mueller answered a question about the nature of core updates: Are they rolled out in steps or all at once then refined?
The post Google Answers Why Core Updates Can Roll Out In Stages appeared first on Search Engine Journal.
AI voice feedback that catches complaints before bad reviews
Run a flock of Claude Code (or other agents) in one window.
Browse, search & track costs across Claude Code sessions
Turn your camera roll into group chat chaos
Axios / LiteLLM hacks behavioral detector app for Mac/PC
Rent real keyboard keys that redirect to your link
Skip ahead & chat with any YouTube video using AI
Meta's first AI glasses built for prescriptions
The spreadsheet rebuilt for AI
Formo makes analytics simple for DeFi so you can grow.
Speed of Voice. Power of AI.
One command to deploy Docker containers to your own server
Link-in-bio, but worse.
Cursor highlight, screen draw, zoom & spotlight
Know what's happening inside your NemoClaw sandboxes
AI-assistant native self-hosted deployment platform
A community idea board that looks like Windows 95
Your Network has Secrets, Now you can see Them.
Google's most cost-effective video generation model
AI captures feedback and tells you what to build next
Everything at your cursor in a single gesture
Crack an Easter egg to generate an AI voice
Team up with your AI teammate in the all-new Slack
Control Codex on your iPhone
Pine doesnβt just draft or organize β it emails, calls, researches, plans, follows up, and persists until the job is done. For companies, Pine acts as an execution arm across CEOs, operations, finance, sales, marketing, and executive assistants β closing open loops, renegotiating contracts, chasing invoices, coordinating vendors, and unblocking stalled deals. For individuals who value time more than money, Pine handles lifeβs friction β negotiating bills, canceling subscriptions, filing claims, and waiting on hold. Pine turns decisions into outcomes β autonomously, persistently, and without expanding headcount.
Claras lets you get instant transcripts and chat with any YouTube video using AI. It analyzes full videos to answer questions, generate summaries, and build a clickable table of contents so you can jump to key moments with confidence. You can highlight insights, save notes, and export to TXT or PDF. Use transcripts to power ChatGPT, Claude, or custom agents, and collaborate with teammates.
OpenClaw Hosting is a managed cloud platform for running OpenClaw, the open-source autonomous AI agent, 24/7 without dealing with servers or Docker. It supports any OpenAI-compatible model, including Claude, GPT, Gemini, and local models via Ollama, and includes free access to Kimi K2.5. Connect your agent to Telegram, WhatsApp, Slack, Discord, Signal, or iMessage, and keep data private with isolated containers and local-first storage. The platform handles updates, monitoring, and scaling so your agent stays online and productive.
Open-source LLM tracing that speaks GenAI, not HTTP.
The PDF Library that automatically organizes itself
Preview GIS files directly in Finder (GeoJSON, SHP, GPKG)
Turn Website Visitors into Customers with AI Conversations
App cleaner that lives in your MacBookβs notch
Email Infrastructure for Modern Product Teams
Massive local model speedup on Apple Silicon with MLX
Science-backed breaks to protect your vision & prevent RSI
Orchestrate your AI coding agents
Source leads, send outbound, grow pipeline. All in your CRM.
Get direct, unfiltered access to the People's House
Grails provides domain intelligence to help VCs, founders, and operators evaluate company domains, discover naming opportunities, and connect with owners. Use domain health audits, industry and funding-stage benchmarks, valuations, risk scoring, and curated lists to spot gaps and acquisition targets. Post a domain request and get responses from owners, or browse available strategic names and work with verified brokers to move fast and avoid costly mistakes.
WhatNext is an AI-powered planner that instantly builds complete itineraries for date nights, friend hangouts, day trips, and weekend adventures using real places, venues, and live events near you. Enter your location and vibe, and it assembles dinner, activities, dessert, and drinks with Google Maps links. Customize budget and preferences, regenerate alternatives, save favorites, and use it across 50+ US cities β free to start
Accomplish It helps you capture, organize, and showcase your career accomplishments. Connect work sources like GitHub and Jira or reply to periodic prompts, and its AI records, categorizes, and turns results into resume-ready statements. Build a living resume to share a timeline, export polished resumes and career artifacts, and benchmark progress by role to stay ready for reviews and new opportunities.
The new Comments to Heart option will automate positive engagement and simulate personalized interaction on high-volume channels.
Instagram had been using the proprietary ratings scales without permission and will now include a disclaimer saying it βdidnβt work with the MPA.β
A new survey from the app and research platform Suzy found that Snapchatters also use their content to influence travel decisions in their social circles.
The updated Adaptive Ranking Model will use less computing power to deliver more relevant ads and drive better return on ad spend.
The latest artificial intelligence-enhanced designs support all-day wear, allowing users to capture and record images and video in virtually any situation.
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Integrating the two apps should offer a new monetization pathway for creators and make it easier for fans to request personalized messages.
Return of the King β Multi-GPU PC gaming is ready for a comeback During the companyβs DLSS 5 reveal, Nvidia teased something massive. When demoing their next-generation DLSS features, Nvidia were running multi-GPU systems. While Nvidia confirmed that DLSS 5 will be usable on single-GPU systems later this year, this demo highlighted something bigger: the [β¦]
The post Multi-GPU Returns β Nvidia unveils βAI SLIβ to power DLSS 5 appeared first on OC3D.
Agents can generate outdated Gemini API code because their training data has a cutoff date. We built two complementary tools to fix this.The Gemini API Docs MCP (https:/β¦
dubltap.io is an ecosystem of 8 single-purpose AI web apps. Each one solves one problem well. Market Maven offers competitive intelligence. Bad Mutha Forker transforms recipes. CLIFF NOTEZ analyzes documents. There are 5 more tools for sales, design, music, side hustles, and cognitive enhancement. All are free to try.
Painkiller Ideas helps founders find ideas worth building and validate them fast. It scrapes Reddit, Hacker News, GitHub, and Product Hunt for real complaints, then uses AI to score pain intensity, market size, and competition. Submit any concept to get market sizing, competitor analysis, ideal customer profiles, pricing strategy, and a prioritized validation roadmap. Access playbooks, prompts, landing page wireframes, and brand assets, and join a community of builders to source problems and compare notes.
Lyn Career is a career intelligence platform that turns your job search into a strategic plan. It lets you track every application in one dashboard and extract job details from URLs, screenshots, or PDFs. You get match scores, skill gap insights, and rejection pattern analysis. It offers CV intelligence with actionable rewrites, role-specific interview prep, offer comparisons, and smart follow-up reminders with ghost detection. A built-in kanban, calendar sync, and contact CRM help manage pipelines and relationships clearly.
Valyris helps founders find and fix weak points in a campaign or investor pitch before high-stakes reviews. It tests narrative clarity, proof strength, internal consistency, timing and exposure, ask/raise logic, and delivery credibility to reveal blind spots and rank priorities. Start with a free 8-question check, then upgrade to an Audit or Deep Audit for a fast PDF diagnosis with key fragilities, likely objections and responses, contradiction mapping, evidence scoring, and a concrete fix plan. It's designed for Kickstarter, Indiegogo, Seedrs, Crowdcube, Y Combinator, Techstars, and direct investor outreach.
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