ReditX is an AI-powered platform for instant image and video generation. It turns simple prompts into high-quality visuals with text-to-image, text-to-video, and image-to-video workflows, along with avatar video creation and advanced image enhancement. Creators and businesses can produce cinematic results quickly and manage output with coin-based plans across starter to enterprise tiers. Based in India and available worldwide, ReditX streamlines content production from idea to export.
Getaway Glow AI is a group travel planning tool made for women who are tired of being the unpaid travel agent for every trip. Before any bookings, the organizer sends an anonymous preference survey to the group, letting each person share their real budget, dietary needs, and activity preferences privately without fear of being singled out. The free tier handles surveys and the alignment report. For $39 per trip, the AI uses that data to generate custom itinerary options with destination recommendations and budget breakdowns based on what your group wants. The group then gets access to a trip planning dashboard for collaboration.
PS6 VS Next-Gen Xbox β Which console has the strongest specifications? The next generation of consoles is coming. Both Sony and Microsoft have started teasing whatβs coming next. Specifications for Sonyβs PlayStation 6 βOrionβ silicon and Microsoftβs Xbox βProject Helixβ (Magnus) chips have leaked. That means we can compare both systems early. Whatβs worth noting [β¦]
The post PlayStation 6 VS Xbox Next (Project Helix) β Leaked Specifications Compared appeared first on OC3D.
Chloe Varnfield, a digital marketing specialist at Atelier Studios with nearly eight years in PPC, joined me to share the mistakes that shaped her career β and the lessons every advertiser should take from them.
Chloeβs first story centers on Googleβs account-level automated assets setting β a feature so well hidden that many advertisers donβt know it exists until a client sends a screenshot asking why their headline looks completely wrong. The setting, buried behind a three-dot menu, defaults to on, meaning Google can automatically generate and serve headlines advertisers never wrote or approved. The takeaway: always audit your account-level settings, and treat every Google update as a potential default youβll need to turn off.
A client asked Chloe to narrow their campaignβs location targeting mid-call. She made the change quickly β and accidentally excluded the UK entirely while targeting only the desired regions. Campaigns stopped delivering. It took three days of head-scratching before she audited the full campaign and found the culprit. The lesson she now swears by: never make significant changes on a Friday, and when something stops working, go straight to a full audit rather than waiting for the algorithm to βfix itself.β
Chloeβs most costly story involves a campaign that was performing at its best in years. A Google rep recommended switching bid strategy from Maximise Conversions to Maximise Conversion Value. She made the switch β and performance collapsed. For small to medium-sized businesses that already struggle to hit the conversion volume thresholds needed for smart bidding to work effectively, changing bid strategy is a high-stakes decision that shouldnβt be made on the spot. It took two months to recover, with the pressure of a major seasonal sale looming. She fixed it β but the lesson stuck: donβt let enthusiasm or a repβs insistence override your judgment. Sit on big decisions. Trust your gut.
When auditing inherited accounts, Chloe consistently sees the same three problems: broken or absent conversion tracking (sometimes still pulling from Universal Analytics), broad match applied to brand campaigns β which makes it impossible to know whether results are genuinely driven by non-brand keywords β and accounts with zero negative keywords. These arenβt minor structural issues. They directly distort performance data and waste budget.
Across all three of her own stories, Chloeβs client relationships survived because she communicated transparently β explaining what had gone wrong, what she was doing to fix it, and what the next step would be if that didnβt work. Her advice to anyone mid-crisis: breathe, be kind to yourself, stay calm, and remember that no one has died. The ability to fix problems under pressure is what builds expertise β and fixing something difficult often becomes your proudest professional moment.
On AI, Chloe is clear: using it to generate ad copy or proposals without reviewing or editing the output is lazy and obvious. AI should make you faster, not replace your judgment. Always put your own voice and review back into whatever it produces.
Hitched uses AI to help couples plan unforgettable honeymoons, bachelor or bachelorette trips, mini-moons, anniversaries, and romantic getaways. Start by choosing your trip type, then get matched with a dedicated travel advisor who listens to your goals and creates a personalized plan. They support you from ideas to booking, offering stress-free planning and tailored recommendations for your celebration.
MeetingRecorder is a native macOS app that records any meeting β Zoom, Teams, Meet, Slack β without adding bots to your calls. It captures both microphone and system audio locally on your Mac, then transcribes and summarizes with one click. It offers unlimited recordings and 20 free transcriptions per month. No accounts, no cloud storage, and no data sold.
Nvidia improves its G-Sync Pulsar tech with optimised sub-90 FPS performance and a dedicated 60 Hz mode Nvidia has officially released firmware version 1.1.4 for its G-Sync Pulsar displays, supporting models from Acer, AOC, ASUS, and MSI. According to this firmwareβs release notes, this update improves the performance of Nvidiaβs monitors when operating at a [β¦]
The post Nvidia upgrades its G-Sync Pulsar monitors with new 1.1.4 firmware appeared first on OC3D.
Langulife is the language practice app for "post-beginners+" (A2βC1) who have the basics but struggle to bridge the gap to actual usage. We scrap the gamification trap of streaks and badges to focus on the absolute priority: your ability to express yourself. This is for the learner who is tired of playing games and ready to start communicating.
Instead of translating generic scripts, you engage with prompts that demand original thoughtβthe discussions you actually have in a boardroom or a bar. Express your perspective via voice and text, compare your answers with the community, and connect with language partners. Stop memorizing scripts. Start articulating what you think.
SerpApi is asking a federal court to dismiss Redditβs lawsuit over alleged scraping of Reddit content from Google Search, saying Reddit is trying to use copyright law to control user posts and public search results.
SerpApiβs argument. SerpApi CEO Julien Khaleghy, in a blog post today, argued the lawsuit fails for several reasons:
DMCA. Khaleghy said Reddit claims SerpApi violated the Digital Millennium Copyright Act (DMCA) by circumventing technical protections. SerpApi disputes that claim, saying it retrieves the same search results visible to anyone who enters a query in Google. Khaleghy argued that:
Catch up quick. Legal fights over search scraping and AI data have intensified in recent months:
Why we care. The case tests whether companies can extract information from Googleβs search results without violating copyright or the DMCA. The outcome could affect SEO tools and AI training data.
Whatβs next. The court must decide whether Redditβs amended complaint can proceed. If the judge dismisses the case with prejudice, Redditβs claims against SerpApi in this lawsuit would end.
SerpApiβs blog post. Redditβs Lawsuit is a Dangerous Attempt to Expand Platform Power
The Iran war isnβt just affecting oil prices; it could harshly impact the semiconductor industry long-term When Israel and the United States of America attacked Iran, the first thing that analysts focused on was how this would impact the price of oil and natural gas. Iran has the power to effectively close the Strait of [β¦]
The post Trumpβs Iran war could make chip shortage worse β Analysts warn appeared first on OC3D.
PortfolioTrackr is a clean, fast portfolio tracker built for serious investors. Track stocks across major global exchanges, crypto (BTC, ETH, and thousands of coins), and commodities like gold, silver, and oil, all with live prices in one dashboard. Set price targets (T1, T2) and stop losses on every position. Monitor your total P&L, per-position performance, and dividend income. Get price alerts delivered via email, Telegram, or WhatsApp the moment your targets are hit. No app to install. Works on any device. Free 3-day trial, then Starter at $9/month or Pro at $19/month with a Lifetime plan available.
Adverse lets you own and control digital ad spaces. Buy an Ad Space once, embed it anywhere, decide who can advertise, what appears, and what to charge while keeping 90% of the revenue. Approvals happen with a click, and smart contracts automate display and payouts. Scale layouts to fit your design, combine spaces, and choose ads that match your audience. Adverse runs privacy-first with no cookies or trackers, giving publishers creative freedom, transparency, and fair monetization.
An overview of how Google Earth AI is supporting the global health communityβs work to predict outbreaks and deliver proactive care.
The days of building campaigns around long lists of keywords are fading. Today, AI-powered Google campaigns and features like Performance Max (PMax) and AI Max are changing the rules.
These keywordless campaigns lean on automation, audience signals, and machine learning to find new opportunities, often faster and at greater scale than humans can.
At SMX Next, three PPC pros β Nikki Kuhlman, VP of search at Jumpfly; Brad Geddes, founder of Adalysis; and Christine Zirnheld, director of lead gen at Cypress North β explained where PMax and AI Max fit into your broader campaign strategy, where humans still make the difference, and how to strike the right balance between automation and control.
AI Max for Search is not a new campaign type. Itβs a one-click opt-in setting within existing Search campaigns.
Without requiring you to switch to broad match, it expands your keywords β similar to broad match or Dynamic Search Ads β using your landing pages and other site assets. It then personalizes the ad copy and landing page the searcher sees.
In the old setup, you might have used a keyword like βskincare for dry sensitive skinβ that sent users to a moisturizer page with generic ad copy because you couldnβt capture every variation. With Googleβs current matching, a specific ad group no longer guarantees that keyword will trigger that ad group.
AI Max for Search addresses this by generating ad copy based on the search query, making it more relevant and directing users to a landing page that better matches their needs.
One area where AI Max for Search is seeing success beyond the norm is blog content. While DSA campaigns traditionally excluded blogs, AI Max for Search can now serve blogs as landing pagesβand theyβre converting. The key is that these blogs guide readers to specific products, not just general content.
The generated headlines are compelling and longer than what traditional RSAs allow, creating a more engaging user experience.
Do:
Donβt:
Week 1: Pick a search campaign to test (brand with brand inclusion, with budget capability, needing more volume). Review landing page URLs and add inclusions or exclusions.
Week 2: Review search queries and add negatives.
Week 3: Continue optimization and turn off AI Max at the ad group level as needed.
Experiment checklist:
A comprehensive study analyzing over 16,000 campaigns revealed surprising insights about match type performance across different bidding strategies.
Max Conversion strategies (Max Conversions, Max Conversion Value):
Most campaigns using max bid strategies have under 30 conversions per month, giving machines limited data to work with. The findings:
Recommendation: Start with exact match, then skip phrase match entirely and layer in broad match if you have more budget to spend.
Target Bid Strategies (Target CPA, Target ROAS):
Most campaigns using these strategies have over 30 conversions per month, with many at 50 or 100+, giving machines substantially more data. The findings:
Recommendation: Start with exact match, layer in phrase match with more budget, then add broad match if additional budget is available.
Why does phrase match perform poorly with limited data but better with more data?
Broad match uses additional signals, particularly previous search queries, to determine bids. When conversion data is limited (under 30 conversions monthly), broad matchβs ability to leverage previous search history makes it much stronger than phrase match.
However, with sufficient data (50β100+ conversions), Google can properly match phrase match keywords using machine-learning pattern matching.
When you combine brand and non-brand data, exact match becomes even more powerful, delivering significantly higher click-through rates, higher conversion rates, lower CPAs, and much higher return on ad spend. Thatβs why segmenting keywords by brand and non-brand is crucial when determining your match type strategy.
For ecommerce companies, broad match (and sometimes phrase match) can produce higher average order values than exact match. When someone searches for a specific product, and you carry that exact item, conversion rates are high, but theyβre usually buying a single product with a lower checkout value.
When shoppers havenβt decided on a product, they tend to match broader keywords and build larger carts β resulting in lower conversion rates but higher order values.
Thereβs a common misconception that Performance Max only works for ecommerce and is too difficult for lead generation. That couldnβt be further from the truth.
The biggest mistake you can makeβone you should avoid entirelyβis optimizing campaigns for form submissions alone. If you treat every form submission as your campaign goal, youβll end up with spammy submissions and frustrated sales teams.
The solution: integrate your Google Ads account with your CRM and import bottom-of-funnel leadsβsales-qualified leads (SQLs), marketing-qualified leads (MQLs), opportunities, or even customers if the sales cycle is short.
When you tell Google Ads what you actually want and set it as your campaign goal, Performance Max can cast a wide net while still bringing in qualified prospects.
Performance Max has significantly more controls now than at launch, making it viable for highly regulated industries:
One of the most underutilized levers for B2B and regulated industries is device control, introduced at the beginning of 2025. You can turn off any device from your Performance Max campaign.
A B2B SaaS example demonstrates the impact: Before device segmentation in January, the account had 224 SQLs from desktop at an acceptable CPA, but 33 from mobile at $319 CPA (above goal). After creating separate mobile campaigns with more aggressive target CPAs, they achieved 190 desktop SQLs and 37 mobile SQLs in a shorter month, with mobile CPA dropping to $204 and overall Performance Max CPA declining from $238 to $204.
Despite lower conversion rates from Performance Max compared to search campaigns (due to broader reach), the results speak for themselves. In September 2025, one B2B SaaS account achieved:
Performance Max cast a wider net with cheaper CPCs, bringing in not just more leads but more sales-qualified leads at a lower cost.
How they did it:
AI Max for Search brings the power of Performance Max to the search network, where bottom-of-funnel intent is strongest. This is especially valuable for lead generation accounts that spend on other networks in Performance Max but donβt generate leads from them.
A higher education financial client (loan products) showed promising early results:
Approved applications (primary KPI):
AI Max brought in qualified leads cheaper despite the highly competitive keyword environment.
Beyond the initial conversion action (soft credit check), AI Max showed superior performance throughout the funnel:
AI Max isnβt just bringing more qualified prospects at the topβlead quality remains higher throughout the entire funnel.
How they did it:
PPC success requires embracing AI-driven campaigns while maintaining strategic human oversight. Whether you use AI Max for Search, Performance Max for lead generation, or adjust match types based on bidding methods and data volume, the key is understanding how these tools work and applying best practices aligned with your business goals.
The data is clear: exact match remains powerful across scenarios, but phrase and broad match perform differently depending on bidding strategy and data volume. For lead generation, the game changer is optimizing for true bottom-of-funnel conversions rather than form submissions, combined with strategic device controls and proper campaign segmentation.
The future of PPC depends on knowing when β and how β to apply automation and control for maximum impact.
Automatic Super Resolution to come to the ROG Xbox Ally X next month At GDC 2026, Microsoft has confirmed that Automatic Super Resolution (Auto SR) is coming to the ROG Xbox Ally X in April. This is a Windows-level upscaling solution designed to improve the image quality of games when rendering at a sub-native resolution. [β¦]
The post Automatic Super Resolution is coming to the ROG Xbox Ally X next month appeared first on OC3D.
A recent Harvard Business Review piece echoes the shift weβre sseeing in the SEO industry: at a macro level, LLMs and Googleβs AI-powered SERP features, such as AI Overviews, arenβt just creating a zero-click environment, but also changing user journeys and behavior.
Theyβre collapsing what used to be multi-touch customer journeys into a single synthesized answer.
For a more visual and emphatic metaphor, the monolith of βSearchβ is crumbling.
When that happens, brands lose many of the touchpoints they once owned, and your marketing strategy must change accordingly. HBR captures this moment well, arguing that marketing now has a new audience and that algorithms increasingly shape first impressions.
That said, while the article points in the right direction on the broader trend, its tactical advice is generic and falls back on shallow tactics.
Much of the guidance returns to familiar marketing playbook ideas that sound strategic and innovative but lack real operational depth. That gap matters for the longevity and sustainability of visibility.
The narrative may be easy for you to understand and repeat at the executive level, but it glosses over the deeper structural changes you must actually make to adapt to the new search ecosystem.
The HBR article centers on schema, authorship signals, and branded concepts. These recommendations risk becoming what I call βflock tactics.β
These ideas spread quickly because theyβre easy to explain, but they offer little lasting competitive advantage once everyone adopts them.
Schema has been one of the most debated topics in LLM and AI optimization. Microsoft Bing confirmed it uses schema for its LLMs, but the relationship between Googleβs models and third-party LLMs isnβt as straightforward.
While it isnβt necessarily wrong to recommend schema as part of your overall search optimization activities (SEO and AI), positioning it as a table-stakes tactic ignores diminishing returns once competitors implement similar markup and it becomes standard.
Another gap is the role of external knowledge systems, such as Wikidata or authoritative publishers. Much of the information LLMs rely on comes from those sources rather than a single companyβs website.
This is less linear to understand, explain, and demonstrate as a single line item on an activity tracker, but these are nuances you now have to deal with, whether you like it or not.
Whatβs also missing is any exploration β or even a nod β to how models ingest and prioritize structured data compared with the many unstructured signals they rely on.
The SEO toolkit you know, plus the AI visibility data you need.
Attaching the names, credentials, and biographies of real experts follows familiar E-E-A-T logic and represents reasonable hygiene.
The problem is that the treatment remains superficial. It risks pushing you to focus on cosmetic signals such as bios, headshots, and credential lists without strengthening the underlying expertise pipeline.
There is a meaningful difference between placing an author bio on a page and cultivating a genuine expert entity whose work appears in conferences, third-party publications, standards committees, or academic collaborations.
Only the latter produces signals that models are more likely to recognize and trust.
The article also suggests creating branded frameworks or concepts β for example, something like βThe Acme Indexβ β to help models associate ideas with your company. In theory this sounds appealing, but in practice itβs extremely difficult to execute.
Unless those ideas spread into the trusted datasets LLMs tend to prioritize, they rarely gain traction.
You need those concepts and frameworks adopted and discussed by entities other than yourself, including academic journals, technical standards, widely used software ecosystems, and other prominent entities in your category.
What often results instead is a proliferation of branded labels that remain largely invisible to the models they were meant to influence.
Beyond these tactical issues, the analysis overlooks deeper structural challenges. It treats AI primarily as an external platform shift.
The implication is that you must simply adapt to it rather than actively shaping your own environment.
HBR never seriously considers the possibility of building AI into your own infrastructure. You can deploy assistants, RAG systems, and domain-specific agents within your own products and customer experiences.
These systems operate in logged-in, transactional contexts where first-party data and controlled interfaces still matter enormously.
In those environments, traditional concerns such as site architecture, structured data, and product design remain deeply relevant, though they operate differently from public search optimization.
The discussion also frames SEO primarily as a page-ranking problem tied to discovery.
That perspective misses the broader shift toward entity-level knowledge management (things, not strings).
Visibility within LLMs increasingly depends on how well you structure entities, taxonomies, and knowledge graphs, and on how those systems connect with external data sources.
Most LLMs donβt process data at the petabyte scale Google uses to understand entity relationships. There is a strong correlation that when something ranks well on Google, third-party LLMs often correlate and βtrustβ Googleβs guidance on which brands to show, for what, and when.
HBRβs phrase βengineering recallβ points directly to this deeper data engineering work, yet the implications arenβt expanded.
Another major omission is the diversity of AI systems themselves.
Different AI assistants and models rely on different training datasets, refresh cycles, retrieval mechanisms, and safety layers.
That heterogeneity means you canβt assume a single optimization strategy will work across all AI surfaces.
It also doesnβt explore the risk of broad-stroke approaches. If you try to increase visibility within AI models without accounting for safety filters, attribution errors, or hallucinations, you may gain visibility in ways that are inaccurate or reputationally damaging.
Track, optimize, and win in Google and AI search from one platform.
HBRβs article works well as a high-level explanation of how AI is changing marketing. It helps you understand that traditional SEO alone is no longer enough and that you must consider how AI systems see and describe your brand.
As a practical guide, however, the advice is thin. Most recommendations focus on surface-level tactics that many companies will quickly copy, reinforcing the echo chamber of flock tactics that are easy to sell and quantify, but risk narrowing your focus to short-term wins at the expense of longer-term strategy.
The real challenge is deeper. You need clear entity definitions, structured knowledge systems, reliable data in trusted sources AI models use, testing across how different models represent you, and AI-powered experiences within your own products.
βWinningβ in the AI era will depend less on cosmetic SEO improvements and more on the harder structural work behind the scenes.
ChatGPT retrieves far more webpages than it cites. A new AirOps analysis found that 85% of discovered sources never appear in the final answer.
Why we care. If you want your content cited in AI-generated answers, discovery isnβt enough. Most retrieved pages never become visible to users.
Key finding. In AI answers, retrieval doesnβt equal citation. Your page can rank and be retrieved yet still lose the citation to a source that better matches the prompt or supporting context.
By the numbers:
Citation rates also varied by query type:
Fan-out queries. ChatGPT often expands prompts with additional internal searches while generating an answer, creating what the report calls a βsecond citation surface.β Across the dataset:
Google ranking correlation. High Google rankings strongly correlated with citations:
About the data. AirOps analyzed 548,534 pages retrieved across 15,000 prompts to examine how ChatGPT expands queries and selects citations.
The study. The Influence of Retrieval, Fan-out, and Google SERPs on ChatGPT Citations
New third-party data shows local publishers lost national reach after Google's Discover core update.
The post Google Discover Core Update Data: Local Publishers Lost Reach appeared first on Search Engine Journal.
Microsoft is getting ready for the era of hyper-fast gaming displays With its newest Windows Insider build, Microsoft has enabled support for refresh rates above 1000 Hz. With builds 26100.8106 and 26200.8106, Windows 11 Insiders can now utilise screens that push past the kHz barrier. According to Blur Busters, who have pushed for this support, [β¦]
The post Windows 11 update enables 1000 Hz+ monitors appeared first on OC3D.
Ratio is a social prediction market that lets you trade on future events and compare your forecasts with friends. Create or join markets across various topics and watch probabilities change as the crowd trades. Available on iOS and Android, it makes forecasting engaging and social so you can sharpen your intuition together.
The standard agency reporting call is broken. Budgets are under extreme scrutiny, yet you still invest in vendors that celebrate arbitrary traffic gains while your sales pipeline stays flat.
Optimizing for raw traffic volume is a legacy mindset that hides real commercial performance. The new mandate is to build an acquisition engine that influences buyers and protects your profit and loss (P&L) long before the transaction.
To survive as a marketing leader today, you must ruthlessly challenge your internal teams and external agencies. Stop accepting reports on operational output and demand hard financial accountability: pipeline contribution, customer lifetime value (LTV) to customer acquisition cost (CAC) ratios, and reduced paid media dependency.
Chasing top-of-funnel informational traffic is a trap. If the users clicking your links arenβt actively buying, youβre paying for vanity metrics, not business outcomes.
This happens because many buyers now use large language models (LLMs) to conduct deep research before they reach a search engineβs transactional layer. If you arenβt the cited authority during that AI-driven research phase, youβre invisible by the time buyers finalize their purchase decisions.
The contrast in traffic quality is staggering when you look at the data. Across our enterprise client base, traditional organic search converts at 2.75%, while AI search converts at 7.48%.
LLMs function as the ultimate trust proxy for todayβs consumers. When tools like Gemini, ChatGPT, or Perplexity synthesize dozens of reviews, whitepapers, and Reddit threads to recommend your enterprise software, users trust the LLMβs consensus more than a branded blog post.
AI engines arm consumers with comprehensive data, comparisons, and consensus. By the time a user clicks your AI citation, theyβve already made their decision based on your authority and are prepared to transact.
The SEO toolkit you know, plus the AI visibility data you need.
Want to capture this 7.48% conversion rate? Your entire approach to digital asset creation must evolve. The strategy no longer centers on ranking among a list of links, but on being cited as the definitive option.
To win the AI consensus, you must translate your marketing strategy into structured capital management.
LLMs require consensus and verifiable facts to generate confident answers. By structuring your digital assets with proprietary data and verifiable entities, you become the default recommendation.
This approach may yield only 500 highly qualified visitors, but it gives LLMs what they need to cite you in vendor comparison prompts and captures buyers at the exact moment of commercial evaluation.
Itβs time to stop viewing SEO as a siloed traffic generator. You must treat organic citation authority as a strategic financial lever to reduce overall CAC.
Align your organic assets with your highest-CAC paid campaigns. When organic search owns the AI Overview, your paid team can confidently pull back defensive ad spend.
Hereβs how to leverage paid and AI search:
If your Head of Search and Head of Paid Media arenβt in the same room once a month mapping organic citations against paid brand bidding, youβre burning capital.
Align your teams and channels. Routinely audit where youβre paying for clicks on terms where you already own the AI citation and the top organic spot.
Treat this cannibalization review as a strict financial audit. Identify wasted defensive ad spend and immediately reallocate those dollars toward net-new market expansion.
To regain control of your P&L, you must challenge your vendors to step up. Ask your agency these three questions tomorrow morning to see if theyβre true business partners or order-takers.
Challenge your team to map their organic efforts directly to the AI research phase of your most profitable products.
The answer you should hear: βWeβve mapped your 50 highest-margin queries. By securing the primary AI citation for these, weβve generated $1.2 million in pipeline this quarter at a 3:1 LTV:CAC ratio.β
Require teams to prove how their organic authority captures demand that would otherwise require paid ad spend.
The answer you should hear: βBy capturing the definitive AI citation for [category], we paused paid bidding on those terms. This reduced our blended CAC by 18% and saved $45,000 in defensive ad spend β which weβve immediately reallocated to net-new market expansion.β
Push your teams to explain their strategy for AI-driven search models. Itβs no longer enough to publish standard web pages.
The answer you should hear: βWeβve restructured your core commercial pages away from standard marketing copy, deploying answer-firstβ frameworks, proprietary data tables, and expert author entities to ensure LLMs confidently extract and recommend your brand. This structural shift has increased our inclusion in commercial AI Overviews by 40% this quarter, directly feeding our bottom-of-funnel pipeline.β
Track, optimize, and win in Google and AI search from one platform.
In a tough economy, SEO is a measurable business unit that must defend its budget with revenue data. Donβt accept operational output as proof of commercial success.
Audit your reporting frameworks immediately. Stop accepting vanity metrics as evidence of success. Demand pipeline impact, LTV:CAC ratios, and a resilient acquisition engine.
Any agency or internal team unwilling to tie its work directly to your P&L will become obsolete. Your job as an enterprise leader is to ensure your brand is cited as the authority long before the transaction begins.
Brandon Ervin, Director of Product Management for Google Search Ads, recently discussed campaign consolidation, AI Max, and what advertiser control looks like in 2026 on Googleβs Ads Decoded podcast. The conversation was serious and informed, and reflected a product team that understands advertiser concerns and is actively working to address them.
But the podcast is also incomplete. The gap between what Google said and what advertisers actually experience from their sales organization is large enough to warrant a direct response.
Ervinβs team is doing genuinely good work, but the platformβs structural incentives havenβt changed. Googleβs evolving product is creating problems faster than it can solve them. Performance is now measured on economic standards, shaping how a search ads audit is performed.
Recentish improvements are genuine:
These are meaningful. They are also solutions to issues introduced by bundling, opacity, and aggressive automation rollout.
These products have been mercilessly shopped to advertisers since 2021, and the controls that make it usable arrived years after the sales push began.
The ability to separate brand from non-brand traffic inside PMax/AI Max should not be framed as innovation. It restores a fundamental distinction that previously existed by default. The ability to see network performance inside a bundled campaign is not an expansion of control. It restores visibility that was removed.
An audit must ask whether new tools are genuinely expanding control or merely reintroducing baseline transparency.
Before the real audit begins, the fundamentals. These are uncontroversial and should already be in place:
Those are table stakes. The real audit begins after that.
With the prevalence of AI, advertisers need to focus on reconstructing economic visibility in systems designed around aggregation and automation.Β
In the podcast, Ervin says βcontrol still exists, it just looks different.β Ad controls β where, when, and to whom ads appear β are still important and changing, some think, for the worse.
The old ad controls β exact match, manual bids, network selection, and device modifiers β gave advertisers direct influence over where ads appeared and what they paid.Β
However, the new controls are indirect. Control now lives in data quality, density, and selectivity. They influence the algorithm, but the algorithm makes the final call.
An audit should focus on three questions:
With these new tactics, you only pass net-new customers or high-value customers. The majority of the time, it is better to just pass the densest and most predictive conversion set.Β Β
Google optimizes toward reported conversions, not incremental conversions. Brand search often captures existing demand. Retargeting often captures users already in motion. Pmax/AI Max frequently blends these signals.
Ervin was asked: Are AI-driven campaigns over-indexing on warm brand traffic to inflate blended ROAS (return on ad spend)?
He doesnβt dispute the problem, but points to partial solutions, including using brand controls, better theme your account, and looking at multi-campaign A/B testing.Β
If incrementality is not measured, automation amplifies non-incremental signals.
Google uses a blended cost-per-action (CPA). For example, the first $50K of spend might return a $30 CPA, while the next $50K might return $120.Β
With automation, money is spent until the blended metric falls within tolerance, meaning the last dollar is not spent efficiently. The vast majority of advertisers are bidding far beyond what they should be and have no idea it is happening.
An audit must:
A lower target makes the algorithm more selective, competing in fewer high-value auctions. Google doesnβt suggest this because that would mean less spend and lower bids are less effective in general.Β Β
On the podcast, Ervin acknowledges that some AI Max matches can βlook a little wonkyβ and says his team is working on exposing the modelβs reasoning.Β
Query mapping has gotten meaningfully worse over the past several years: queries landing in the wrong ad groups, matching to keywords with different intent, and broad match pulling in traffic unrelated to the keyword.
AI Max has accelerated this β thereβs been an increase in the volume of irrelevant queries flowing through AI Max campaigns, with no connection to the advertiserβs business or keywords in the account.Β
Meanwhile, Googleβs recommendations consistently push toward broad matching and large themed ad groups.Β Β
The issue is not whether broad match works, but whether high-value intent is being diluted in larger, broader ad groups. Fewer ad groups means that we cannot effectively or meaningfully lower targets without a massive structural negative schema, so performance differences have to be large enough to validate the new structure.Β
An audit should:
Performance Max and Demand Gen bundle multiple networks into single campaigns, but offer limited visibility into which networks drive results. This makes it hard to cut the underperforming ones. The slow rollout of network-level controls systematically benefits Googleβs less competitive inventory.
An audit must:
Combining these elements in your audit will help you succeed in this new world of ad search:Β
The cumulative effect is that the surplus value generated by your best inventory and high-intent, high-converting search queries gets redistributed across Googleβs weaker inventory (i.e., Display, YouTube, Discover, Gmail, crazy tail queries).Β Β
This is how to get a dwindling supply of valuable search queries to inflate the cost-per-clicks (CPCs) of low-quality inventory.Β
The Ads Decoded episode: Is your campaign structure holding you back in the era of AI?
Β
AMDβs Ryzen 7 X3D CPUs have received price decreases in the UK Less than two months have passed since the release of AMDβs Ryzen 7 9850X3D (review here), and the CPU is already available at a discounted price. In fact, all of AMDβs currently available Ryzen 7 X3D CPUs are now available at lower prices [β¦]
The post AMD Ryzen X3D CPU prices drop in the UK appeared first on OC3D.
TSEP (The Social Enterprise Pound) connects vetted African professionals with SMEs worldwide, enabling businesses to access reliable remote capacity while supporting ethical digital employment across Africa.
Through a structured social enterprise model, SMEs can outsource projects to trusted talent while benefiting from built-in compliance, transparent workflows, and managed delivery. TSEP helps businesses scale efficiently while creating sustainable income opportunities for skilled professionals across the African continent.
Shombo is an all-in-one case management platform built for solo personal injury attorneys. It automates the entire PI workflow from client intake and contingency fee agreements with built-in e-signatures to AI-powered demand letters, medical record summaries, and settlement statements with lien tracking. Stop paying for five different tools; Shombo replaces your fragmented stack with a single platform that tracks every case from new lead through disbursement. AI generates demand letters from your case data, extracts injuries and treatments from medical records, and drafts daily briefings, so nothing slips. Built-in reminders chase clients so you don't have to.
Google's Gary Illyes offered a candid overview of Googlebot, explaining there are hundreds of crawlers that are not publicly documented.
The post Google Says They Deploy Hundreds Of Undocumented Crawlers appeared first on Search Engine Journal.
Mock blocked APIs, passthrough ready routes, see live source
Codex Claude Gemini CLI parallel agents orchestration
Real-time AI overlay for meetings & invisible to screenshare
100% local AI Driven insights of chat messages
Reduce documentation review time by 80% instantly
Turn your phone into a brain performance coach
Extend Computerβs capabilities with repeatable instructions
One-hand OpenClaw control via voice
The AI editor Notion should have built
Generate & animate brand mascots in minutes
Step inside any building on Google Earth to see the view
Build complete WordPress sites through AI conversation
Comet is the first agentic AI browser built for mobile.
Private by architecture, not by promise.
Perfect memory for every real-world conversation
Pre makes anybody an operator.
Personal AI Agent in Your Chat
Use your webcam to fix your posture
Ask Maps questions, drive with immersive navigation.
Process your legal/medical/financial documents locally
The first AI-native recruiting agency. Fill roles in days.
Find engineers who never apply by actual github code
AI captures decisions, drafts plans, and opens matching PRs.
Turn Japan's public data into an AI-ready spreadsheet WS
Run Claude, Codex & Cursor in parallel from one dashboard
Your AI career super-connector
Meta's 3rd-gen custom AI chips for GenAI inference
Use Garry Tan's exact Claude Code setup
AI Email app that's affordable, Hit Inbox Zero
Share encrypted files that are ephemeral
Your site did 5 days of research. We did it in 60 seconds.
AI-native verification & anti-fraud Engine
Reedle is a reading app that keeps your articles and papers in one place and helps you remember them. You can import web pages, PDFs, EPUBs, and your Zotero library, read in a clean layout, ask questions about the text, get help with formulas, translate passages, and listen to them out loud. When you highlight something important, Reedle schedules it for review using spaced repetition, so you donβt forget it later. It works on iOS, Android, and Chrome, lets you search across your whole library, and exports your notes to tools like Obsidian.
initdesk is a simple, AI-powered help desk built for product creators and lean teams who handle support over email. Connect your inbox, receive AI-drafted replies, use auto-tags, and access an AI Assistant you can teach with your FAQs and documents to improve accuracy over time. Keep everything in one lightweight shared inbox, publish a support portal, and trigger workflows with Slack notifications and webhooks. It's designed to save hours, reduce costs, and maintain fast support without enterprise bloat.
BiteBurst is an AI-powered learning platform that helps kids aged 6β14 build healthy habits through short, gamified lessons on nutrition and movement. Kids follow a playful path of cards, quizzes, and challenges, tapping emojis to explore foods and activities and earning collectible cards that keep motivation high. Friendly mascots guide every step while parents track progress, streaks, and knowledge growth with clear insights and multi-child support.
Serpdino is a rank tracking and competitor insights tool designed to give you more for less. It features unlimited keyword tracking, automated competitor discovery, built-in Similarweb and PageSpeed Insights, four-year historical volume trends, and AI overview and SERP snapshot tracking.
Dream Select Boot Camp is an all-in-one platform for aspiring and active real estate agents in all 50 states. Prepare for your state exam with mock exams, topic quizzes, and speed rounds, and keep momentum with a 24/7 voice AI mentor for on-demand guidance.
Use the Agent Toolkit to manage leads with a built-in CRM, generate listings, and follow a social media playbook. Join The Bullpen community to swap advice and stay motivated. Start with a 14-day free trial and get support anytime.
ChatReact is an AI-powered customer support platform that learns over time. When customers mark an answer as wrong, the Improvement Agent analyzes the issue and suggests fixes automatically. It crawls your website, builds a Knowledge Base, generates SEO-friendly FAQs, and supports 24 languages. Built for businesses seeking more than a basic chatbot, ChatReact includes a full REST API and MCP Server for developers and is GDPR compliant. A free tier is available.
New features include artificial intelligence suggestions for listing summaries, prepaid shipping labels and AI-generated profile summaries.
The professional platform outlined a series of revamped systems to cater more effectively to user interests and better understand contextual relevance.Β
Β
The app partnered with the portrait artist to showcase the potential for interactive creation and give visitors a chance to test Snapβs AR Specs.
The platform is in the early stages of implementing a feature that will allow hiring professionals to manage initial candidate assessments using artificial intelligence interviewers.Β
Β
The platform published a new guide outlining how users can enhance their content to improve their chances to appear in artificial intelligence answers.
Β
Users on the app are two times more likely to sign up with a service provider after seeing in-app content from a creator, according to a new report from the company.
Β
Crowdfunding AI helps founders plan, budget, and execute successful crowdfunding campaigns. It generates an instant strategy with timelines and tasks tailored to your product and niche, showing what worked in similar launches so you can model proven tactics. It blends expert frameworks with data-driven guidance and free resources to take you from validation through post-campaign with clarity and confidence.
Nabbed.io is a career CRM built exclusively for revenue professionals β SDRs, AEs, Account Managers, and sales leaders. It reframes the job search as a sales motion. What separates Nabbed from other job search tools is what happens after you get the job. Career Mode activates when you land the role. It keeps your network warm, tracks compensation benchmarks against the market, surfaces hiring signals at target companies, and logs wins as resume-ready bullets in real time.
Formidable Forms WordPress vulnerability enables unauthenticated attackers to pay a small amount and have a larger purchase marked as paid.
The post Formidable Forms Flaw Lets Attackers Pay Less For Expensive Purchases appeared first on Search Engine Journal.
Agent that improves your website 24/7
Google is leaving the door open to advertising in its Gemini AI app, with a senior executive telling WIRED the company is βnot ruling them outβ β a notable shift from the flat denials made just months ago.
Whatβs changed: In January, Google DeepMind CEO Demis Hassabis told reporters at Davos that Google had no plans to put ads in Gemini. Now, SVP Nick Fox is saying otherwise β noting that learnings from ads in AI Mode will βlikely carry overβ to Gemini down the road.
The current strategy. Rather than rushing into Gemini, Google is using AI Mode β its Gemini-powered Search product β as a testing ground for ad formats in AI experiences.
Why we care. Googleβs entire business is built on advertising. How and if they bring ads into AI products will shape the future of the industry β and set the tone for every AI company trying to figure out how to monetize free users. The brands that figure out how to show up relevantly in conversational AI environments now β before the auction gets competitive β will have a significant first-mover advantage.
The bigger picture. Google is in a stronger position than its rivals to take its time. The company crossed $400 billion in revenue in 2025, giving it the luxury of patience. OpenAI, by contrast, is under pressure to more than double its $30 billion in revenue this year β and has already started testing ads in ChatGPTβs free tier.
Between the lines: Foxβs framing is careful but revealing. By positioning Gemini ads as a βprioritization questionβ rather than a values question, Google is signaling itβs a matter of when β not if.
What to watch: Personal Intelligence β Geminiβs feature that pulls from a userβs Gmail, Photos, and Calendar β is the sleeper story here. Fox called personalization his βholy grailβ for Search, and hinted it could eventually roll into the broader Search experience. If it does, advertisers would gain access to an entirely new layer of contextual targeting β though Fox was quick to add that user data will not be sold or shared.
Whatβs next. Advertisers should start preparing now. As Google refines its AI ad formats in AI Mode, those learnings will eventually migrate to Gemini. Brands that understand how to show up relevantly in conversational, context-rich AI environments will have a significant head start when the floodgates open.
Dig deeper. Google Is Not Ruling Out Ads in Gemini (registration needed)
Adeux helps couples stay close with private messaging, a shared photo album, a joint calendar, and time capsules. It adds daily questions, location sharing, date ideas, achievements, streaks, and a bucket list to spark connection and plan moments together. Create a couple with a 6-digit code, chat in real time, relive memories with Memory Lane, and never miss anniversaries with reminders.
Microsoft have launched their first preview of DirectStorage 1.4 At GDC 2026, Microsoft released the public preview of DirectStorage 1.4 and the Game Asset Conditioning Library. These tools arrive as part of Microsoftβs next-generation feature set for βProject Helixβ, Microsoftβs next-generation Xbox. With DirectStorage 1.4, Microsoft has officially added support for Zstandard compression. This popular [β¦]
The post Microsoft unveils DirectStorage 1.4, and it aims to transform gaming appeared first on OC3D.
Googleβs AI Overviews may be reducing traditional search clicks, but publishers still have meaningful growth opportunities in breaking news and Google Discover, according to new data from Define Media Group.
Why we care. AI-generated answers are reshaping search traffic. Evergreen content is losing clicks, while real-time news coverage and Discover distribution are emerging as stronger traffic channels for publishers.
By the numbers. Across Google Search, Discover, and Google News, breaking news traffic grew 103% from November 2024 through early 2026 in the companyβs dataset. Losses were concentrated in informational and evergreen content:
Discoverβs role: Google Discover, which grew 30% across the portfolio, is now the main growth engine for breaking news distribution. Discover traffic rose steadily as web search traffic fell. For the first time in the dataset, Discover and web search now drive roughly equal traffic.
Why is this happening? AI Overviews appear less often for news queries than for other topics. AI Overviews appeared for about 15% of news queries β nearly three times less often than in categories such as health and science β according to Ahrefs data cited in the report.
The report. BREAKING! News Thrives in the Age of AI
Google is launching Ask Maps, a conversational AI feature powered by Gemini that lets you ask Google Maps complex, real-world questions and get personalized, actionable answers.
Whatβs new. You can now ask Maps questions like βIs there a public tennis court with lights where I can play tonight?β or βMy phone is dying β where can I charge it without a long wait?β and get a conversational answer with a customized map view.
Key capabilities:
On ads. Ask Maps doesnβt include ads yet, but Google isnβt ruling them out, the Gemini team told SEO consultant Glenn Gabe. Because ads are already common in local search, it wouldnβt be surprising to see them appear here eventually.
Why we care. Ask Maps changes how you find places, shifting discovery from keyword searches to AI-generated recommendations. The businesses that get picked will have rich, accurate, up-to-date Maps profiles and strong community engagement, because thatβs the data Googleβs AI uses to make its picks.
Availability. Ask Maps is rolling out now in the U.S. and India on Android and iOS, with desktop coming soon.
Whatβs next. Advertisers and local businesses should pay close attention. When AI mediates how people discover places, visibility in Maps becomes more critical than ever. Keep your business listings accurate, complete, and review-rich as Gemini draws from that data to power recommendations.
The announcement. How weβre reimagining Maps with Gemini
Racine helps families and groups inventory belongings, capture stories, and distribute items fairly. You can invite members with roles, scan objects with automatic recognition, and let everyone privately rank their preferences before allocation. The app uses an equitable algorithm to assign items or lots, supports remote participation for scattered families, and offers flexible rules, notifications, and configuration to match how your group organizes things.
The first AI-native platform that builds your online store
Google redesigned the Asset Optimization section in Google Ads for Demand Gen campaigns, consolidating AI-powered creative controls into a single, cleaner interface.
Why we care. Advertisers managing creative at scale now have a centralized panel to toggle automated features on or off β making the process less manual and time consuming.
Whatβs new. The redesigned layout groups three key automation capabilities together:
How it works. The new panel surfaces simple toggles for features like Resized videos and Image assets, letting advertisers quickly enable or disable each automation without digging through multiple menus.
Bottom line. Advertisers running Demand Gen campaigns should head into the Asset Optimization panel now and audit which automations are enabled. Turn on video resizing and landing page image pulls if you havenβt already β these are low-effort wins that can meaningfully expand reach without additional creative production.
Also make sure your landing pages are clean and visually strong, since Google will be pulling from them directly. And as Google continues rolling out more AI-driven creative tools, start shifting your workflow toward providing high-quality source assets and letting the platform handle format and placement optimization from there.
MoveAlerts surfaces market-moving news from across the web, analyzes it with AI, and delivers real-time alerts. Use the live dashboard to see sentiment, importance, and bullish/bearish scores for 10,000+ tickers, with stock-level summaries. Get Discord alerts with summaries and market sentiment, build watchlists, and track trending stocks from news and Reddit.
An analysis of ChatGPT conversations found the default and premium models cite almost entirely different sources for the same queries.
The post ChatGPTβs Default & Premium Models Search The Web Differently appeared first on Search Engine Journal.
AI agents for PMs to automate check-ins and tasks
Cursor for film-Consistent control, virtual sets, BYOC, Open
With AI-driven search and hyper-fragmented media channels reshaping how people discover brands, the βset it and forget itβ approach to marketing measurement is officially dead.Β
Measuring impact isnβt a static check of dashboard data. Used strategically, measurement is a virtuous cycle where data informs your ad platform settings and those settings, in turn, generate better data (and business outcomes).
Hereβs how to build a measurement flywheel that keeps your growth efficient.
Imagine a Bay Area SaaS company, PowerLoop, selling an AI-powered analytics platform. Theyβre investing heavily in Google Search, LinkedIn, and some emerging AI publication sponsorships.
Their problem? Google Ads is reporting fantastic ROAS, but their internal CRM shows a significant number of leads and opportunities that canβt be directly attributed to any specific ad campaign, making it hard to prove marketingβs true impact to the board.
This is your in-engine reality. Whether itβs Google Ads or Meta, platform ROAS uses pixel and conversion API data to tell you what the platform thinks happened. This might go without saying, but platforms donβt have a habit of underestimating their own impact.
The ideal: Use this for real-time optimization.
The limitation: These signals feed your tCPA (target cost per acquisition) or tROAS (target return on ad spend) bidding strategies. Itβs the fastest feedback loop you have, but itβs rarely the full truth. This leads us toβ¦
What it looks like in practice (example): PowerLoopβs Google Ads account is configured with a tCPA bid strategy for βFree trial sign-ups.β
Google Ads reports a healthy $50 CPA, well within their target. LinkedIn also shows strong engagement and click-through rates. This looks great on paper, but the unattributed leads are a nagging concern.
Dig deeper: How to avoid marketing mix modeling mistakes that derail results
Platform data is optimistic. Your bank account is realistic.
Back-end ROAS, coming from your CRM of choice (Salesforce, Shopify, HubSpot, etc.), connects your ad spend to your actual CRM or internal database. Itβll likely require some data engineering work to properly map back-end performance against ad platform spend, but the effort is well worth it.
The ideal: Clean out the βnoiseβ (refunds, fake leads, or credit card declines), and evaluate marketing efficiency based on your own first-party data.
The benefit: You can use back-end ROAS to validate your account structure. If the platform says a campaign is winning but the back end shows low-quality leads, itβs time to restructure your targeting or creative.
What it looks like in practice (example): When PowerLoop connects their ad spend to Salesforce, they find that many of the βFree trial sign-upsβ from Google Ads are either incomplete profiles or come from IP addresses outside their target market and never convert to qualified sales opportunities.
LinkedIn, while showing engagement, has a lower conversion rate than expected. This insight leads them to refine their Google Ads audience targeting and adjust LinkedIn campaign objectives to focus more on high-intent lead forms.
This is the βSo what?β metric. iROAS answers the question: How many of these sales would have happened even if we didnβt show the ad? This is where marketing mix modeling (MMM) and incrementality testing (geo-lift tests or holdout tests) come into play.
The goal: Identify true value and βhalo effectsβ across channels.
The action: MMM insights tell you where to double down and where youβre just paying for customers who would have converted anyway. Use these insights to prioritize your next round of incrementality tests.
What it looks like in practice (example): PowerLoop conducts a geo-lift test by pausing Google Ads in select non-core markets for a few weeks and measuring the difference in sign-ups between dark areas and similar areas where ads are still running. They discover that while Google Ads drives some incremental sign-ups, a significant portion of those attributed by Google would have signed up organically anyway, through direct traffic or referrals.Β
Conversely, their MMM suggests that the AI publication sponsorships, while not driving direct βlast-clickβ conversions, are significantly contributing to brand awareness and reducing the overall CPA across all digital channels by driving more organic searches for their brand. This reveals that the sponsorships have a higher iROAS than initially thought.
Hereβs an example of overvalued and undervalued channels:
The greater the incrementality factor, the more undervalued this channel has been, such as YouTube and podcasts in this example. The lower the incrementality factor, the more overvalued these channels have been, such as paid review sites in this case.
Dig deeper: Why incrementality is the only metric that proves marketingβs real impact
The final frontier is understanding where to spend the next dollar. Every channel eventually hits a plateau where efficiency craters. This truism is called the law of diminishing returns. Understanding when you hit that mark is key to efficient budgeting.
The goal: Estimate the βroom for growthβ before hitting a performance ceiling.
The benefit: By monitoring mROAS, you know when to pull back on a saturated channel and reallocate that budget into emerging spaces.
What it looks like in practice (example): PowerLoopβs analysis shows that after spending $100,000/month on Google Ads, another $10,000 yields a marginal return of $0.80 for every dollar spent β meaning theyβre essentially breaking even or losing money on additional spend.Β
However, for their AI publication sponsorships, every additional dollar spent is still returning $2.50 in incremental value, indicating significant room for growth. They decide to reallocate 15% of their Google Ads budget to expand their sponsorship program.
Marketing measurement is a work in progress because the landscape is constantly shifting. Today, you might be perfecting your Google Search strategy. Tomorrow, youβre figuring out how to measure the impact of a mention in a ChatGPT or Perplexity response.
The hypothetical PowerLoop team understands this. Theyβre constantly evaluating new AI-driven channels and planning how to integrate them into their measurement cycle. They know that what worked last quarter might not work this quarter and that relying solely on platform data is a recipe for wasted spend.
The goal isnβt to find a βperfectβ number that stays set in stone. The goal is to use this cycle to stay agile. When your iROAS reveals that a channel is more incremental than you thought, you push your tROAS targets in the platform (Step 1) more aggressively. When mROAS shows youβre hitting a plateau, you start testing new, unproven channels to find different audiences.
Dig deeper: Break down data silos: How integrated analytics reveals marketing impact
Vora is an AI health coach that connects your wearables, calendar, and logs to build personalized training, nutrition, and recovery plans. It syncs with Apple Health and 500+ platforms, tracks sets and meals with voice and photo logging, and monitors HRV, sleep, and strain by muscle group. Use Vora to follow daily plans, guided breathwork and meditation, and cycle-aware training. The iPhone and Apple Watch apps deliver real-time coaching and multi-source insights that adapt to your goals and readiness.
Repovex gives engineering teams GitHub repo scorecards across security, process, and documentation, then points directly to the fix. You can install the GitHub App in minutes, scan every repo in your organization, and get nightly checks with weekly email or Slack digests. It tracks branch protection, reviews, secret scanning, Dependabot, CI/CD, stale PRs, and key docs, letting you fix issues with one click. Start free for up to 5 repos or upgrade for unlimited repositories.
Google Maps launches Ask Maps, a Gemini-powered conversational feature for local discovery in the U.S. and India, plus a navigation overhaul in the U.S.
The post Google Maps Launches AI Conversational Search With Ask Maps appeared first on Search Engine Journal.
V-COLOR has started selling single DIMM DRAM kits with an extra βfillerβ module V-Color has officially released new β1 +1 Value Packβ DDR5 memory kits for AMD Ryzen systems. These kits include a single DDR5 DRAM module and a second βRGB Filler NON-DRAM solutionβ. This gives users the appearance of a dual-channel memory system with [β¦]
The post DRAM desperation β V-Color launches 1+1 DDR5 kits with one dummy module appeared first on OC3D.
Vibe Training AI models
Agents that gather feedback, scan code, & produce roadmaps
A growing share of search interactions now begins inside generative systems. Users open AI tools and ask questions the same way theyβd ask a colleague: in full sentences, with context, and often across multiple follow-up prompts.
Generative systems synthesize answers from sources they interpret as credible and relevant to the prompt. Visibility increasingly depends on whether a brandβs content aligns with the questions people ask AI systems, not just the keywords they type into search engines.
Traditional search results havenβt disappeared. Todayβs discovery environment blends ranked results, AI-generated summaries, and conversational assistants.
This shift introduces a new research layer: prompt research. Itβs quickly becoming a foundational practice for SEO and generative engine optimization (GEO).
Hereβs how prompt research works, why it matters, and how to incorporate it into content planning.
Search queries are becoming more context-rich as generative AI platforms encourage users to ask questions in natural language and refine them through follow-up prompts.
Many searches now unfold as a sequence rather than a single query. A user asks an initial question, reviews the generated response, then adds clarifying prompts with new constraints, comparisons, or context.
In these environments, search behaves more like a conversation than a lookup. Each prompt builds on the previous response, creating a chain that gradually clarifies intent.
Several shifts reinforce this pattern:
As a result, the unit of search interaction is shifting. Instead of optimizing for isolated queries, you increasingly need to understand how prompts are phrased, sequenced and refined within AI-driven search sessions.
Understanding those prompt patterns is the goal of prompt research.
Dig deeper: A smarter way to approach AI prompting
Prompt research analyzes the questions people ask generative AI systems and how those prompts shape the answers those systems produce.
In practice, it functions as the AI-era extension of keyword research:
This changes the research process. Instead of mapping keyword variations alone, teams need to:
For example, someone researching email marketing software might begin with a prompt like:
Follow-up prompts extend the conversation:
Prompt research identifies these patterns so you can structure content around how users explore topics through AI search.
Prompt research expands the scope of content strategy beyond ranking individual pages to clusters of related questions.
For SEO, that means ensuring content covers the full topic landscape rather than a single query. For GEO, it means ensuring content provides the context generative systems need to synthesize answers.
Several strategic priorities follow.
Prompt clusters reveal the full range of questions users ask about a topic. Content that addresses those related questions is more likely to rank in traditional search and surface in AI-generated answers.
Search engines and generative systems rely on entities to understand context. Clearly referencing relevant companies, products, technologies, and concepts helps them interpret how information fits together.
Well-organized content is easier for systems to work with. Clear headings, concise explanations, and logical sections help search engines index pages and help generative systems extract key points.
Prompt research often shows that users ask questions in natural language. Content that answers those questions directly β through explanations, comparisons, and FAQs β aligns better with search queries and AI prompts.
Together, these practices help content perform across the modern search environment.
Dig deeper: How generative engines define and rank trustworthy content
Organizations can integrate prompt research into their SEO and GEO workflows through four stages.
Prompt discovery focuses on identifying the questions users ask across generative platforms and AI-assisted search.
Useful sources include:
The goal is to surface prompts with clear intent β especially questions that require explanations, comparisons, or recommendations.
Once prompts are collected, they can be grouped into intent-based clusters. These clusters reveal how users explore a topic across multiple questions.
Common prompt clusters include:
Informational prompts
Comparative prompts
Transactional prompts
Strategic or multi-step prompts
Prompt clustering helps identify patterns and prioritize content topics.
Prompt mapping connects prompt clusters to content strategy.
This typically involves:
For SEO, this helps expand coverage across related queries. For GEO, it helps ensure content addresses the types of prompts that trigger AI-generated answers.
The final step focuses on structuring content so search engines and generative systems can interpret it clearly.
Effective response optimization often includes:
Clear, structured answers improve reader usability while increasing the likelihood that content surfaces in search results and AI-generated responses.
Dig deeper: How to use AI response patterns to build better content
Prompt research introduces new complexities for teams working across SEO and GEO:
Despite these challenges, the underlying opportunity remains clear: understanding prompt patterns helps you anticipate how AI systems assemble answers.
The example below illustrates how that process can shape a content strategy.
Consider a hypothetical SaaS analytics company looking to expand its visibility across AI-generated answers and traditional search.
Initial prompt research reveals several clusters around predictive analytics:
Rather than targeting these prompts with isolated pages, the company builds a content structure around the broader topic.
Each article includes structured explanations, FAQs that mirror common prompts, and citations from industry research.
This structure supports SEO and GEO. The foundational guide captures informational search demand, while supporting and comparison content addresses follow-up prompts users ask as they explore the topic.
Over time, the content appears in both traditional search results and AI-generated answers, expanding visibility in the new search environment.
Dig deeper: Advanced AI prompt engineering strategies for SEO
Brands that begin analyzing prompt patterns today will gain insight into emerging discovery behaviors. A practical starting point involves auditing existing content through a new lens:
Search visibility increasingly depends on how well content participates in AI-generated knowledge systems.
Prompt research helps ensure that participation happens by design rather than by chance.
Make your vibe coded site actually look nice
Get push notifications when your AI agent finishes
Groundsource is a new AI-powered methodology from Google Research that transforms millions of public records into actionable data.
Google Maps has two new AI features: Ask Maps and Immersive Navigation.
The Growing Up in the Digital Age Summit was hosted by Dublinβs Google Safety Engineering Center (GSEC).
Imagine your ideal customer going to ChatGPT and asking, βIs [BRAND] worth it?β
Theyβre not getting a vetted list of links in response. Theyβre getting a synthesized answer, most likely summarizing who you are, what youβre known for, and whether youβre credible. Theyβll get a confident answer to the nebulous question of assigning worth.
You donβt control that summary. But it will shape their decision before they convert, possibly before they ever visit your site.
This is the new reality of search. SEO has traditionally been a discovery channel: higher rankings led to more traffic, which led to more conversions. But AI-powered search experiences, from AI Overviews to ChatGPT, Gemini, and beyond, are changing the game.
Narrative is now the goal. Brands have to actively monitor and shape how theyβre described, evaluated, and synthesized in AI-powered search experiences.
SEO has officially entered its defensive era. Protecting brand narrative in the new search landscape is quickly becoming table stakes.
Youβre probably asking: Isnβt this just reputation management? Or isnβt this what good SEO has always done? Not exactly.
Traditional SEO has focused on visibility: earning rankings, driving traffic, and increasing conversions. Defensive SEO focuses on something slightly different: how your brand is perceived once itβs visible.
Today, perception matters as much as placement. Defensive SEO is the practice of shaping that narrative. It means paying close attention to how AI tools describe your brand and where evaluation-based queries influence buying decisions.
In practice, defensive SEO is:
Just as importantly, defensive SEO is not:
Itβs not about hiding weaknesses. Itβs about reducing ambiguity.
When your positioning is unclear, AI fills in the gaps with whatever signals are readily available: reviews, old content, aggregator summaries, and competitor comparisons. Defensive SEO ensures the strongest and most accurate version of your brand gets reinforced.
At its core, defensive SEO is structured, proactive brand narrative management across the modern search landscape.
Dig deeper: Why SEO is your best defense against declining organic traffic
Several forces are converging to make defensive SEO necessary today.
Traditional search results allowed users to explore multiple perspectives. Someone researching a brand could read reviews, scan articles, and evaluate different viewpoints before forming an opinion.
AI-generated answers compress that process. Nuanced positioning, evolving messaging, and subtle differentiation can all be condensed into just a few sentences. Those sentences become a prospectβs first impression of your brand β a simplified version of your reputation.
Search behavior is shifting toward evaluation-driven questions. Users are increasingly searching for things like βIs [BRAND] worth it?β or β[BRAND] reviews and complaints.β
These are high-intent, high-impact queries. They signal real conversion consideration.
If brands avoid these topics, outside sources step in to answer them. Review sites, forums, and aggregator pages become the dominant narrative. Ignoring these evaluation queries doesnβt prevent them from shaping perception. It simply removes your voice from the conversation.
Generative engines donβt invent brand reputations. They amplify patterns that already exist.
They rely heavily on reviews and ratings, authoritative third-party mentions, and frequently cited claims or descriptions. Over time, this creates a feedback loop. The most commonly cited narrative gains weight and visibility, while alternative or evolving positioning becomes less prominent.
Dig deeper: Is SEO a brand channel or a performance channel? Now itβs both
Defensive SEO isnβt a single tactic. Like all SEO efforts, itβs an ongoing process focused on understanding and shaping how search engines interpret your brand.
The first step in your defensive SEO tactical plan should be an AI visibility audit.
Auditing AI-generated responses for brand consistency helps ensure that LLMs accurately and positively reflect your brand.
Start by querying AI tools the way real users would. Identify a standard set of questions that someone may realistically ask about your brand.
The goal is to test how the AI agents describe your company across different themes, such as brand overview, services, culture, reputation, and positioning.
Use the same question set across multiple AI tools and LLMs β ChatGPT, Gemini, Copilot, and Claude. Donβt forget to ask for citations, especially if the response is unexpected.
Now that you have all of this data, itβs time to analyze the responses for consistency, accuracy, and opportunity. Look for patterns.
This audit should be done regularly. These patterns reveal how your brand narrative exists within AI-driven search, and how it evolves.
Dig deeper: 200+ AI audits reveal why some industries struggle in AI search
The next step in your defensive SEO tactical plan: update the source material these LLMs are drawing from. While you may not be able to log into ChatGPT and βfixβ an answer, you can influence how your brand is portrayed.
Many brands avoid creating content that acknowledges trade-offs or criticisms. In the past, that instinct may have made sense. But today, avoidance can often backfire.
AI systems tend to trust content that provides balanced explanations and transparent comparisons. Ultimately, this type of comparison content is an age-old SEO tactic.
If youβre not creating content that addresses it, chances are your competition is. Clear answers to common concerns signal credibility to your audience and search engines alike.
Instead of ignoring evaluation queries, we should be addressing them head on. The goal isnβt to eliminate criticism, itβs to ensure the context around it is accurate and fair.
We know that generative AI relies heavily on independent sources such as indexed content in traditional search engines, media mentions, reviews, and forum commentary. These third-party sources are influencing how your brand is described just as much, if not more than, owned content.
This means defensive SEO canβt exist in isolation. It requires alignment across multiple disciplines, including PR, social media, and customer experience.
SEO can influence visibility, but SEO alone canβt fix narrative gaps.
Leverage PR in coordination with off-page SEO to earn media coverage and mentions from authoritative third-party sources. Consider Reddit to engage with your audience and share content. Monitor and update social profiles, review aggregators, directory listings, and partner sites.
Many brands evolve faster than their content does. Pricing models change, product offerings expand, and messaging shifts to reflect new positioning. Yet older pages with outdated information often remain.
AI systems pull from everything available and fill ambiguity with whatever is most prominent. Thatβs why outdated content can shape a brandβs AI output long after itβs relevant.
Regularly reviewing and updating legacy content on your website ensures the signals being used by generative AI reflect the brand you are today.
Use structured data and schema markup to clarify information. Ensure your About pages, service pages, and leadership bios are up to date and comprehensive. Publish well-optimized blog posts and press releases that reinforce your positioning.
If the web is your brandβs resume, make sure it reflects your strongest work, not an outdated version of who you used to be.
Dig deeper: How to use AI response patterns to build better content
Traditional SEO metrics like rankings and sessions still matter, but theyβre no longer sufficient on their own.
Defensive SEO introduces a new set of signals to monitor:
Taken together, these indicators help reveal something traditional SEO dashboards rarely capture: how your brand is being interpreted across the search landscape.
Organic share of voice measures how often your brand appears, but in AI-powered search, presence alone no longer tells the whole story. What matters just as much is how your brand is described once it shows up.
This is where the broader idea of βdescription share of voiceβ becomes useful. Instead of measuring pure visibility, description share of voice looks at the language and framing associated with your brand relative to competitors.
For example, imagine two companies appearing equally often across AI-generated summaries and search results. One is consistently described as βinnovative,β βtrusted,β or βcustomer-focused.β The other is described as βaffordable,β βbasic,β or βconsistent.β Both brands may technically have the same share of voice. However, the narrative attached to that visibility is completely different.
Description share of voice captures that distinction. It reflects the themes and positioning that AI is repeatedly associating with your brand relative to others in the category. And over time, patterns will emerge. Certain descriptors get reinforced, while others may disappear from the conversation entirely.
Tracking these patterns and adjectives provides a clearer understanding of how your brand is being framed and characterized when it does appear.
Despite the name, defensive SEO isnβt about reacting to threats. Itβs about strengthening clarity and trust.
When brands actively manage their narrative across the modern search landscape, they reduce misinformation, support informed decision-making, and create a more consistent brand experience. Ultimately, defensive SEO ensures that when someone asks AI about your brand, the answer reflects who you actually are.
This shift isnβt just an evolution for SEO. Itβs an organizational one.
Shaping how a brand is understood in AI-driven search queries forces collaboration between teams that too often operate in silos. PR influences the narratives circulating in the media. Customer experience teams hold the signals that shape reviews and sentiment. Social media can surface emerging perceptions long before they appear in search results.
All of those signals increasingly feed the systems that summarize and interpret brands for users.
Most SEOs agree that search has evolved beyond just a discovery channel. Itβs now a reputation and perception engine, and often the first filter through which customers understand your brand.
In this multimodal, multichannel world shaped by AI, visibility alone isnβt enough.
Ranking without narrative alignment is fragile. Ranking without context leaves interpretation to systems you donβt control.
The brands that succeed will rank well, shape how theyβre understood, and make sure the right story is told.
Weβve tested Google AI Max over the past nine months, analyzing 23 individual tests across 16 already mature advertisers operating within a range of verticals. This article reveals what we did to maximize success with this campaign type.
Your experiments and observations may vary. If so, weβd welcome the debate.
This is intended to be just one voice among many in the conversation around AI Max. All the analyses we discuss are replicable within your own accounts, so you can ratify or dispute the findings based on your own data.
Before launching an AI Max test, consider several factors. Two are particularly significant:
With those prerequisites satisfied, we can now cover some of the juicier findings weβve uncovered from our AI Max tests.
AI Max performs best when you enable all three core features simultaneously:
Overall, we saw a 40% higher uplift in test success rates for campaigns that used all three features compared to those that opted in only to the baseline search term matching functionality.
Google has been pushing the text customization concept in various guises for a few years. However, earlier versions, like auto-applied recommendations, have had limited uptake. So, we were keen to finally assess the impact this would have.
Using the Added by segment in the assets report, you can compare how text customization performs compared toΒ standard advertiser-provided assets.
We found that AI-edited assets delivered an improved return on ad spend (ROAS) and helped extract more value per impression. Put simply, clients were better off when text customization was activated than when it wasnβt.
This trend was consistent across both headline and description assets, even though we found that text customization modified headlines far more often than descriptions.
Strong performance is the ultimate objective for AI Max campaigns. But from a search geekβs perspective, the arguably more tantalizing result is that text customization demonstrably improved Quality Score.
We assessed historical Quality Scores for clients who activated text customization before and after the test launch. This analysis is valid because the Google Ads interface reports Quality Score only when the search query syntax exactly matches the keyword. This methodology provides a like-for-like comparison across a group of queries that were targeted both before and after switching on AI Max.
We saw a topline improvement in weighted Quality Score, from 6.8 to 7.3. This upward trend repeated across the three components of the Quality Score, with ad relevance showing the most notable uplift.
Logically, this shouldnβt be a surprise. After all, the premise of text customization is that Google shows the best possible ad to each individual user. Nonetheless, itβs satisfying to see this story unfold in our analysis.
At the same time, this finding is noteworthy because advertisers have generally been reluctant to use the full AI Max suite. Across all our test cases, only 50% used text customization, and even fewer (44%) enabled URL optimization.
Some brands will need to adhere to compliance guidelines that outright prohibit the use of these features. But our results suggest that if you have any wiggle room at all, youβd be well served by running a test with all three features.
Google is constantly rolling out additional guardrail features to clarify what is and isnβt off-limits from a brand messaging perspective. Marketers in more risk-averse organizations would be well-advised to keep a close eye on these releases.
Dig deeper: Google expands AI Max text guidelines globally
This next suggestion might seem counterintuitive, but hear me out.
If youβre testing out AI Max for the first time, you might be better off enabling the feature across your entire account right from the start, rather than following a step-by-step approach. There are a few reasons for this.
With AI Max enabled, you can target more queries and users than before. And of those queries, many will genuinely be net-new to your account.
However, itβs also common for queries that another campaign in your account once reached to get pulled into your AI Max campaign.
When we assessed performance at the campaign level, we saw an average +7% increase in conversion value, directly generated by queries the campaign had never targeted before.
When we zoomed out to an account-level view, however, only 46% of those queries were actually new to the account. The remaining 54% had previously been captured elsewhere in the account.
That still isnβt a bad result. An approximately 3% incremental uplift in conversion value, especially for accounts that were already running with a high broad match adoption, is great.
But this finding does have two key implications:
Donβt rely on a cost per acquisition (CPA) by match type analysis to assess AI Maxβs efficacy. This approach reveals attribution data within your campaign. But what you really want to know is whether AI Max has improved your overall ability to generate returns at an incremental investment that youβre comfortable with.
There are examples of advertisers trialing AI Max and achieving account-wide efficiency improvements. But you should identify those cases by reflecting on macro, account-wide performance β not by looking at your match type CPAs.
Consider how AI Max interacts with your other campaign types and targeting methods. Letβs call out one particularly glaring example: Dynamic Search Ads (DSA). In our own analysis, every successful AI Max test occurred in an account with low-to-no adoption of DSA campaigns.
This is understandable. Almost every single capability of DSA campaigns is now available in AI Max. So, it shouldnβt be surprising that having both campaign types running in parallel doesnβt improve performance.
Itβs plausible that we may not be that far away from Google announcing another round of campaign streamlining initiatives, similar to those for Smart Shopping and Discovery campaigns in previous years. But until then, itβs on marketers to put some thought into the role you intend each campaign type to play within your overall account plan.
Dig deeper: AI Max in action: What early case studies and a new analysis script reveal
If youβre already comfortable with AI Max and youβre ready to push onto the next step, thereβs a wealth of new testing opportunities to think about.
Search Bidding Exploration (SBE) was and still is the first major user-facing change to Googleβs bidding technology in the last five years. Yet thereβs been remarkably little industry chatter so far about this feature. SBE feels like a natural partner for AI Max, given that both tools are designed to reach incremental and previously inaccessible customers.
AI Max also gives you the chance to evolve your thinking around account structure. In an AI Max world, the optimal balance between segmentation and consolidation may lie elsewhere than before.
Weβre already starting to see some green shoots of successful hyper-consolidation approaches. But itβs still too early to decisively comment one way or another.
Dig deeper: AI Max increases revenue 13% but drives higher CPA: Study
Itβs an intriguing time to be working in paid search, and AI Max has already sparked significant debate and experimentation within the industry. If youβre a later adopter or if youβre looking to improve on a previously unsuccessful foray into AI Max, then consider the following:
HackAIGC is an all-in-one NSFW AI platform for uncensored chat, image creation, and video generation. It lets you converse freely with an adult chatbot, turn text into images, edit and transform images, and create videos from text or images without content filters. Powered by private AI running on your device, it keeps prompts and data local and secure. Free and premium plans provide daily credits and advanced features for high-volume use.
Session Sense connects to your calendars and payroll to show the real cost of every meeting. It centralizes attendance, time, and compensation data in an intuitive dashboard so you can see where time and money go. Its AI finds costly patterns, flags bloated invites, and recommends changes to optimize cadence, duration, and participants. Use reporting to align teams, trim waste, and ensure the right people are in the room.
Xbox Mode is coming to Windows 11 Microsoft has confirmed that its βXbox Full Screen Experienceβ is rolling out to Windows users (in select markets) next month under the new βXbox Modeβ name. Xbox mode will let PC gamers switch between the standard Windows user interface and Microsoftβs controller-friendly βXbox Modeβ user interface. This UI [β¦]
The post βXbox Modeβ is coming to Windows 11 next month appeared first on OC3D.
Git layer that preserves the why behind AI-written code
Personal AI Assistant that operates your entire computer
Interactive product tours powered by AI
Googleβs newest London building, Platform 37, is named to honor Google DeepMindβs AlphaGo.
We analyze over 650 PC games to compare DLSS, FSR, and XeSS support to reveal how far Nvidia, AMD, and Intel really are from each other in upscaling and frame generation adoption.
Web3Trackers delivers Web3 attribution and crypto marketing analytics that connect Web2 campaigns to on-chain conversions across Ethereum, Solana, Base, and TON. Add one script tag to detect wallet connections, build UTM-style links, and view CAC, LTV, and ROI by channel in real time. It scores wallet quality, filters bots, and tracks swaps, mints, and transfers without an SDK. Start free.
Google announced that Search Console's brand queries filter is open to all eligible sites, spurring questions about the feature.
The post Google Answers Questions About Search Consoleβs Branded Queries Filter appeared first on Search Engine Journal.
AMD is co-engineering its next-generation FSR tech with Xbox for Project Helix AMDβs Jach Huynh has confirmed that AMD is working on its next-generation FSR technologies as part of a βdeep co-engineering partnershipβ with Xbox. Xbox will support βFSR Diamondβ, which will be βnatively optimisedβ for Project Helix (Xboxβs next-generation console) and βdeeply integrated into [β¦]
The post AMD unveils βFSR Diamondβ, and its βnatively optimisedβ for Project Helix appeared first on OC3D.
Xbox promises huge CPU and GPU performance uplifts with its next-generation Xbox console At GDC 2026, Microsoft has been shedding some light on its next-generation Xbox console, codenamed Project Helix. Microsoftβs Jason Ronald has confirmed that Project Helix will have an βorder of magnitude increase in ray tracing performance and capabilityβ. This places Microsoftβs next [β¦]
The post Xbox confirms 10x Ray Tracing boost with Project Helix appeared first on OC3D.
Browser Agents that communicate using ASCII wireframes
Automate any workflow by recording a demo
Craigslist for the agent economy - where AI and humans trade
Modernize your outdated websites in minutes, not weeks
AI agents that find the right people to hire automatically
Payments stack for AI agents
Prototype, design, and collaborate on your existing products
Agentic R&D factory to test pivots and benchmark strategy
Captures and stores your AI Conversation from LLM platforms
An open-source macOS client for Codex
End-to-end Voice & SMS shared inbox for Teams
AI for PMs that sees your screen & completes your work
Control which digital ads people see inside your property
A multiplayer mobile browser with AI agents on every webpage
Watch it Once. Retell it Forever.
Turn user drop-offs into Pull Requests
Analyze, interpret, and create interactive visuals from data
A real-time dashboard of email provider performance
Turn customer feedback into specs for your coding agent
An infrastructure layer for training deskop agents
AI video benchmarks, leaderboards and industry news
A general purpose collaborative AI agent
On-demand sandboxes for AI Agents
AI agents that pentest your app and prove real exploits
Manage all your browser tabs in one place
Turn any web component into pixel-perfect AI Prompt
Turn AI-generated HTML into a live URL via MCP/API
Platform that turns multi-agent AI into a game-like visual
An AI agent that monitors your AI agents
AI churn prediction & prevention for SaaS
Continuous Improvement for your AI Agent
Touchless computing with hand tracking and AI agents
Superset, Ghostty and tmux coding workspace but for Windows
A better legal memory for your law firm
The API for AI agents to book appointments
Turn messy voice notes into ready-to-publish content.
Engineering change on autopilot
Deploy your AI Agents in 60 seconds
Autonomous AI wildfire agent with nearby alerts
Secure AI agents that work like humans but free & flawless
Rank in search. Get cited in AI.
Design App Store screenshots in your IDE
Your marketing agent for organic, paid, and AI search
This is Ava, a different kind of ChatGPT.
Login