What are the biggest failure points for AI generated... landing pages ? >8 working designers. >40 AI landing pages. >754 failure points found. OpenAI’s Sol fumbles layout Anthropic’s Fable fumbles the finish xAI’s Grok fumbles interaction AI at Meta’s Muse Spark has room to improve across the board Every AI model breaks a landing page in its own signature way. We mapped all 4. 🧵show more

Claude DESTROYS ChatGPT for building VSL funnels. I put... together the Claude VSL Prompt Vault (below) I've booked 500+ calls for clients with VSL funnels. $1M generated. These prompts are how I build every single one. The same prompts behind every VSL funnel I've built since 2023. Scripts, landing pages, offer stacks, everything. This is the closest thing to hiring a funnel strategist for free. What's inside the vault: → Custom Claude System Prompt for VSL Scripts → The 5,280-Word VSL Conversion Framework → AI Audience Research Prompt (2-Part Deep Dive) → AI Competitor Funnel Analysis Prompt → AI Hook Generator (15+ Variations by Awareness Level) → AI Proof Stacking Prompt → AI Offer Architecture Builder (2-Part Sequence) → AI Landing Page Copy Prompt → AI VSL Script Audit + Rewrite Prompt Want access to this vault? → Comment "CLAUDE" → Follow and I'll DM the vault!show more

Claude Code + Google Stitch 2.0 is f*cking cracked... 🤯 Google just dropped a free AI design agent that solves Claude Code's biggest weakness: frontend design. One screenshot of a high-converting landing page → a production-ready site for your brand in minutes. All inside Google Stitch + Claude Code. Perfect for DTC brands and agencies who are building advertorial pages and product launch pages for Meta but burning days on designer back-and-forth. If you're running Meta ads and need 5-10 different landing pages testing different hooks, angles, and offers — each one targeting a different audience and pain point — you know the bottleneck isn't the ads. It's the pages. Briefing designers, waiting for revisions, paying $2-5K per page. Stitch eliminates the design bottleneck: → Find a high-converting advertorial that's scaling on Meta → Screenshot it and drop it into Stitch (powered by Gemini 3.1) → Stitch redesigns it with your brand's colors, fonts, and imagery using Nano Banana 2 → Edit sections visually — headlines, CTAs, layouts — without touching code → Export the code and paste it into Claude Code → Claude builds the full production site and deploys to Vercel or Netlify in 60 seconds No designer. No $3K per landing page. No Claude Code frontend that looks like a template from 2019. What you get: → Designer-quality landing pages and advertorials built in minutes, not weeks → Visual editing so you actually see the design before you code it → Nano Banana 2 generating on-brand product imagery and hero shots → A repeatable system — new angle, new page, same pipeline Built 100% with Google Stitch 2.0 + Claude Code. I put together a full playbook showing the exact workflow: how to find winning pages, redesign them in Stitch, and deploy with Claude Code. Want it for free? > Like this post > Comment "STITCH" And I'll send it over (must be following so I can DM)show more

OpenAI's AI broke out of a locked test environment,... got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.show more

A 22-YEAR-OLD IN CHINA SOLD HER VIRAL AI REELS... TO 6 LOCAL BUSINESSES AND CLEARED $11,900 LAST MONTH She never filmed a thing. She tracked 40 faceless channels in one sheet and scored every clip that beat its own channel. One line ran the sort: views over the channel's median. Anything past 30 was a format she could reskin that week. The AI-video look was blowing up on Douyin, so she took the shape and ran it in english first. For $50 a month in apps she found the format on Monday and shipped by Wednesday. Claude prompted each format into 30 shot lists. CapCut generated the whole clip, a photoreal AI girl in a full scene, with AI voice and captions. Make posted to 3 platforms and fed the numbers back into the same sheet, so her own hits fed the next batch. She walked into a boutique with a phone and a page of outlier scores. She left with a retainer for 20 AI videos a month. A studio needs a model, a crew, and a shoot day for one clip. She generated hers overnight. Every prompt and the scenario are in the article.show more

There’s a feeling of “wtf is a designer anymore”... floating around… I felt it while scrolling Reddit after Figma Make was announced 😬 For some people it simply didn’t compute that a code prototype could be a design artifact. But I’ll let you in on a little secret… A few days ago I visited a well-known design leader and he said that in the last 6 months, ~40% of their team's design artifacts are now created in Lovable, Bolt, etc. That’s kind of crazy, right? It's a big reason why Ioana Teleanu (1st AI designer at Miro) said design is "in the middle of an identity crisis" So Ioana and I went deep into this topic during today's episode and I want to share a few ideas I’m still thinking about: ——— We’re in a weird moment in history where there are AI designers and non-AI designers. But this is a blip on the timeline as adoption accelerates. The idea of "AI designer" won't exist in the future Here's what Ioana said 👇 “We’re all gonna be thinking about some sort of AI angle in the way we do our work. I don’t even feel that the AI design role will exist in this explicit format in a couple of years. All the designers will be AI designers” I want to make something clear though... Ioana described herself as generally “change averse” and the type of person who "DOESN'T jump on new things" That’s why her initial approach to AI was a bit less intentional… But now she’s changed her tune: “We have a moral duty to experiment with these technologies because we're designers and we should be curious about the world, and we should be curious about the future.” The cost of ignoring new technology has never been higher So if you’re interested in what it looks like to design AI experiences within your existing product then I think you’ll really enjoy this week's episode Ioana shares a ton of lessons learned from Miro and frameworks for how she helps clients integrate AI effectively 👇show more

YOMIRGO #Product #Update YOMIRGO AI-HUB OFFICIALLY LAUNCH ---A Structural... Upgrade from a Single-Product Model to an AI Agent Ecosystem Platform In its first phase, 11 AI projects have been integrated, spanning high-value sectors including finance, scientific research, enterprise services, development tools, and experiential AI. ➡️AI-Hub: This is not merely a feature expansion — it represents a critical structural upgrade from a single-product architecture to a multi-vertical AI Agent aggregation and capitalization platform. This milestone marks the initial structural formation of the YOMIRGO ecosystem. 1. Structural Distinction Between Agent Matrix Lab and AI-Hub To avoid positioning ambiguity, we formally clarify the structural division between the two: 🔘 Agent Matrix Lab — Internal AI Production & Incubation Platform Agent Matrix Lab serves as YOMIRGO’s proprietary AI development and internal incubation platform, responsible for: • R&D and testing of in-house AI products • Incubation of native AI Agents • Technical architecture experimentation and runtime validation • Testing of AI Agent models, memory systems, and runtime orchestration It functions as the production workshop and experimental engine of YOMIRGO’s “AI Super Factory.” 🔘 AI-Hub — External AI Agent Aggregation & Ecosystem Layer AI-Hub is a market-facing AI Agent aggregation and showcase platform, responsible for: • Curation and onboarding of high-quality AI projects • Cross-vertical structured ecosystem layout • Rating and classification systems • Traffic distribution and ecosystem collaboration entry points AI-Hub is not an internal incubation unit, but a standardized aggregation framework at the ecosystem level. 2. Integrated Project Structure (First Batch) ✅1. Finance & Prediction 🔹Cointoken AI — AI Agent-powered quantitative trading engine 🔹VVAI — AI-driven real-time Web3 intelligence and decision system 🔹AlphaQuant — Global financial market forecasting engine 🔹NextGoals — AI-powered global sports prediction agent This vertical forms the real-time information, trading, and predictive decision infrastructure for Web3-native users. ✅2. Science 🔹Charmen AI — Large-model-based pet acoustic recognition technology 🔹Encore Health — AI-driven health forecasting and longevity management system for high-net-worth individuals 🔹Reproducibility AI — AI expert system for financial engineering validation and academic reproducibility This sector focuses on research-grade AI capabilities, collaborating with universities and research institutions to drive real-world scientific deployment. ✅3. Business 🔹GlobalSales — B2B automated lead-generation AI Agent 🔹ResearchBot — Business intelligence and deep due diligence AI Agent This vertical targets the enterprise market, delivering scalable and commercially viable AI productivity tools. ✅4. Coding 🔹CodeMatrix — Full-stack development assistant Providing AI-driven development infrastructure and low-barrier building capabilities to global users. ✅5. Interesting 🔹Fortunetell AI — AI-powered symbolic analysis and interactive insight system Exploring the application boundaries of AI within experiential and interactive scenarios. 3. YOMIRGO Four-Layer Structural Framework YOMIRGO has now established a clearly defined four-layer structure: ▶️Layer 1: Agent Matrix Lab — Internal Production & Incubation ▶️Layer 2: AI-Hub — Ecosystem Aggregation & Rating ▶️Layer 3: LaunchPad — Capitalization Pathway ▶️Layer 4: Market — Circulation & Value Realization Together forming a complete industrial pipeline: Incubation → Validation → Aggregation → Rating → Capitalization → Market Circulation This is the structural model behind YOMIRGO’s defined “AI Super Factory.” 4. Strategic Significance The launch of AI-Hub signifies: • YOMIRGO has established standardized AI Agent aggregation capabilities • A cross-vertical ecosystem structure is now in place • Internal incubation and external aggregation mechanisms are structurally separated • The AI Agent industrial flywheel has begun operating YOMIRGO is no longer merely an AI product platform, but a structured AI Agent industrial system integrating production, aggregation, capitalization, and circulation. 5. Next Phase • Continue expanding high-utility AI Agents with real-world application value • Optimize AI-Hub’s scoring, rating, and filtering mechanisms • Strengthen synergy with LaunchPad and Market • Enable AI Agents to complete value realization within the ecosystem The first 11 projects are only the beginning. AI-Hub is designed to become a continuously expanding AI Agent gateway — not a static product showcase. Further structural expansion is underway.🔥show more

most of the top holders of $goblin keep buying... every single day soon the sellers will be out and it will be up only i haven’t seen a group of bullish top holders like this for a very long time we all know what we hold i roundtripped 200k in profit on $goblin to a 60k loss and would to it again because i believe so much in this coin and think its THE ONE most bullish AI narrative we have ever seen open ai mascot fully embraced by open ai ( with merch , stickers and daily posts) Chatgpt obsessed with Goblins Open ai devs obsessed with Goblins New model coming out with Goblin mode such an easy lay up for those who understand Truth terminals ( meme) went to billions and noone outside CT has never heard of that shit whats the ceiling for $goblin then? the meme created by the biggest AI ever…? named the mascot? GOBLIN ‘s website avatar was taken straight from the website and put on a OpenAI tshirt…. Billions, all you gotta do is wait.show more

I just built a Claude prompt library that runs... your entire DTC marketing operation 🤯 100+ prompts organized by function: competitor research, creative briefs, ad copy, hooks, landing pages, performance analysis, customer review mining, and more. Perfect for DTC brands and agencies who are still prompting Claude from scratch every time they open a new chat, rewriting the same context, and getting generic output that sounds like every other AI-generated ad. This prompt library eliminates the entire loop: → Competitor Research: scrape and analyze competitor ads, extract winning hooks, map creative strategies, build competitive battlecards → Creative Briefs: generate data-backed briefs from ad performance, write iteration briefs, new concept briefs, test plans → Ad Copy & Hooks: 20 hooks across 10 frameworks, full ad copy variations, persona-specific angles, fatigue-busting rewrites → Landing Pages: audit any landing page against DR best practices, clone high-converting advertorial structures, write product page copy → Performance Analysis: audit Google Ads accounts, find wasted spend, build visual dashboards, weekly narrative reports → Customer Intelligence: mine reviews for ad copy language, extract objections, find unexpected use cases, build persona cards from real data → SEO & Content: find keyword gaps, write content in your brand voice, optimize product listings for AI shopping (ChatGPT, Gemini) → Email & SMS: launch sequences, weekly newsletters, abandoned cart flows, post-purchase nurture No more blank-page prompting. No more re-explaining your brand every session. No more generic AI output that sounds like a template. What you get: →100+ copy-paste prompts organized by the 8 functions DTC teams actually run →Every prompt pre-loaded with the context structure Claude needs to give you real output →Prompts that reference your brand voice, your ICPs, and your real data — not generic placeholders →A living library you can customize once and reuse across every campaign I put together the full prompt library as a single downloadable playbook: organized by section, ready to copy-paste into Claude today. Want it for free? > Like this post >Comment "PROMPTS" And I'll send it over (must be following so I can DM)show more

60% of contracts filed to the SEC contain mistakes.... Spellbook Labs ran 60,000 pages of contracts from 500+ public companies through AI to answer a question: How often do human lawyers hallucinate? Accidents from self-driving cars get headlines—and they should. But human drivers have a higher accident rate. We wondered if something similar might play out in contract drafting. Reviewing 100 page contracts line-by-line is inhuman. Mistakes are inevitable. AI is very good at spotting issues like missing definitions and broken section references. There are risks to using AI in law. But we also need to ask: what is the risk of *not* using AI review as a second set of eyes? In the full report we show: 1. Top 10 public companies with the cleanest contracts 2. Error rates across industries (Media and Telecom were highest) 3. Error rates over time (we're not getting better!) 4. Error rates by contract type and length ... and a bunch more. Congrats IBM for having the cleanest agreements. 📌 Read the report here:show more

Everyone on Reddit is arguing whether Clawdbot will replace... human traders. Meanwhile one wallet quietly made $507K doing what no AI figured out yet. distinct-baguette. The name kept appearing in comment sections. Always downvoted. Always deleted. Someone really did not want attention on this account. I found the profile. 30,067 predictions. $11,400 biggest single win. Profit curve that looks AI generated but verified human. → Wallet: Spent a week analyzing every position. Here is what I found. While everyone debates Clawdbot's next move, this wallet does something simpler. It watches what Clawdbot does wrong. AI models react to headlines. They parse sentiment. They move fast but they move together. When Clawdbot and similar systems all buy YES at the same second, NO becomes mispriced. Supply and demand. Basic economics. distinct-baguette waits. Watches the herd move. Then buys the side everyone just abandoned. Not fighting AI. Feeding off its blind spots. One position from February 4. Ethereum Up or Down window. Entry at 44 cents. Everyone else panic sold. Result: 127% return. $259 profit from one position in 15 minutes. Another trade same day. Solana window. Bought at 53 cents when sentiment scanners flagged bearish. Closed at $1. 88% gain. The Reddit threads about this wallet keep getting deleted. 167,800 people watching anyway. Someone wants this strategy quiet. Clawdbot processes millions of data points. distinct-baguette processes one thing: Where the machines all agree, the machines are all wrong. While you argue about AI replacing traders, someone is getting rich from AI being predictable. The question is not whether AI will win. The question is whether you are trading with the crowd or against it.show more

do you understand what Anthropic just admitted? their engineers... haven’t written most of their own code since early 2026. 80% of the code merged into Anthropic’s codebase last month was written by Claude. let that sink in. the company building the most powerful AI in the world is already being built by that AI. and the numbers keep getting wilder: → engineers are shipping 8x more code than in 2024. not because they’re working harder. because Claude is doing most of it. → Claude’s success rate on open-ended coding problems hit 76% in May 2026. up 50 points in just 6 months. → one Anthropic employee said “it’s been 5 months since I last wrote any code myself.” → Claude Mythos Preview achieved 52x speedup on research optimization. a skilled human gets 4x in 4-8 hours. → the length of tasks AI can reliably complete is doubling every 4 months. and here’s the part that keeps me up at night: in April 2026, Claude-powered agents were given an open AI safety problem and left alone to solve it. two human researchers recovered 23% of the performance gap in a week. the agents recovered 97% in 800 hours. Anthropic calls this recursive self-improvement. AI building AI. getting better. building a better version. repeat. they say we’re not there yet. but they also say it could come sooner than most institutions are prepared for.show more

We just put 50+ GTM leaders on Nasdaq's board... in Times Square. Why? We're about to witness a seismic shift in CRM – one that even surpasses the cloud revolution of 1999. AI is here, and it's going to change everything. The category has been on the cusp of revolution for years. At Attio, we've been preparing for this paradigm shift since our founding, with a clear vision for the next generation of CRM. But great ideas don't exist in a vacuum. CRM is one of the most widely used tools in business, and its future in the AI era will be shaped by many voices. That's why we reached out to 50+ GTM leaders – the people who are actively building companies and using these tools every day. We wanted to understand their thoughts on the future of CRM in the AI era, and start a conversation. So we took it to Times Square, where their perspectives will be shared all week. We've compiled their insights, along with our own vision, into a manifesto for the future of CRM. It's a glimpse into what's possible when AI meets customer relationships. It's what we're building at Attio. Read the manifesto 👉show more

CoinMarketCap AI Is Live: What Does It Really Change... ? 🌱 In the fast paced world of crypto, information is power but its often scattered, delayed, or hard to trust. CoinMarketCap newly launched CMC AI aims to fix that by offering real time insights with no friction. ✨ Real Time Q&A on Coin Pages 🌱CMC AI is now integrated into major coin detail pages, generating automatic Q&As every 30 minutes. During periods of volatility, it updates dynamically, helping users understand price movements with short and structured explanations. No login required, no delays. 🌱However, while this speeds up the process, its not a substitute for deeper analysis. It answers the “what” and “why,” but not always the “what’s next.” ✨What’s Coming Next? 🌱CMC AI is just getting started. According to its roadmap, several new features are on the way • Homepage Integration: A quick view of market trends and opportunities, without clicking into individual coins. • Live Chart Analysis: AI will add context to price moves by linking them to news, sentiment, and social media. • Token Comparison Tool: Users will be able to compare tokens like BTC vs SOL across utility, performance, and tech specs. • Portfolio Insights: One click portfolio analysis with rebalancing suggestions and market outlooks. • Cross Device Continuity: Start an AI conversation on desktop and continue it seamlessly on mobile. ✨A Tool Not a Strategy 🌱 CMC AI brings speed and clarity, two things crypto investors often lack. But it’s still just a tool. It won’t make decisions for you. It helps guide your thinking not replace it. 🌱 The smartest way to use it? Treat it as a compass, not a map. It can point you in the right direction, but the journey is still yours. 🌱 CMC AI represents a step forward in how users interact with crypto data. It filters the noise, shortens research time, and brings useful context closer to the user. But like any shortcut, it works best when you already understand the long route.show more

🌌 AI Agents Are Taking Over... And We’re Bringing... Them to Berachain Foundation 🐻⛓ 🐻🔥 Hundreds of hours spent on research, tracking wallets, analyzing bribes, and managing portfolios... What if your AI Agent could do this for you—24/7? ⏲️ 🔧 Our Tech Is Next-Level On our testnet, you’ve been memeing it up with PumpFun™, creating dank memecoins enhanced by NFTs. But once Berachain’s mainnet is live, you’ll be able to create your own AI Agents. To test and perfect our tech, we shared it with projects like AI Agent Layer | AIFUN, allowing us to test it in all conditions and continuously improve its performance. 🛠️🔥 🐻 Why AI Agent are great for berachain? Berachain might seem simple at first glance: validators, bribes, POL, staking rewards… but the deeper you go, the more complex the game theory becomes. 🤯 Here’s where AI comes in. Imagine an agent helping you: 💡 Optimize bribes 📊 Analyze validator behavior 🧠 Make decisions faster and smarter and much more, as AI Agents won't be limited to the chain itself! Examples of AI Agent Projects Dominating the Space 🚀 $VIRTUAL - Launchpad for AI Agents ($3.5B mcap) 🧠 $AI16Z - Eliza OS Framework ($2B mcap) 🔍 $AIXBT - The AI Analyst revolutionizing CT ($430M mcap) 🎮 $GAME - Low-code toolkit for creating AI Agents ($230M mcap) 💡 There are already AI Agents managing portfolios, betting on sports, and automating tasks. And guess what? They're outperforming humans. 🌐 We've built Virtuals on Berachain Our protocol integrates directly with Berachain, providing real utility to our token: $AIBERA 💎. Say Ooga Booga if you want to see a thread about tokenomics and $AIBERA utility. The chain has beras on it, and beras deserve AI Agents. 🐻🤖 Ooga Booga. 🔥show more

let me explain what Anthropic just did they built... an AI model so good at finding security vulnerabilities that they have refused to release it meet Claude Mythos → it’s Anthropic’s newest frontier model and it’s not available to the public. not because it’s not ready. because it’s too dangerous → Mythos found tens of thousands of zero day vulnerabilities across every major operating system and web browser… many of them 1 to 2 decades old. for context… Opus 4.6 found about 500. Mythos found tens of thousands → it found vulnerabilities in the Linux kernel. a 27 year old vulnerability in OpenBSD. a 16 year old vulnerability in FFmpeg → it doesn’t just find bugs. it writes the exploits too. that’s the part that scared them → so instead of releasing it… Anthropic has created Project Glasswing. a cybersecurity initiative where they hand picked 40+ companies to use Mythos for defense only → the partner list reads like a who’s who of tech… Amazon, Apple, Microsoft, Google, Nvidia, Broadcom, Cisco, CrowdStrike, Palo Alto Networks, JPMorgan, the Linux Foundation → Anthropic is giving up to $100 million in usage credits to these partners and $4 million to open source security organizations → they’re briefing CISA and the Commerce Department on how to handle this → the benchmarks are truly insane… Mythos hit 77.8% on SWE-bench Pro where Opus 4.6 scored 53.4%. hit 93.9% on SWE-bench Verified where Opus 4.6 scored 80.8% → Anthropic’s head of frontier red team said this is “the first time a model is this good that we decided to approach release in a very different way” this is the first time an AI company has held back a model because it was too capable not too expensive. not too slow. too dangerous and instead of locking it in a vault they weaponized it for defense and gave it to the companies that run the internet that’s either the most responsible thing an AI company has ever done… or the scariest only time will tellshow more

🚨 AI MAY HAVE JUST SHATTERED A MATHEMATICAL BELIEF... THAT STOOD FOR 80 YEARS. A geometry problem posed by legendary mathematician Paul Erdős in 1946 may have just been overturned… by an AI. The problem sounds simple on the surface: How many pairs of dots can exist on a flat plane while all being exactly the same distance apart? For decades, mathematicians believed there was a strict upper limit to how fast those equal-distance pairs could grow. But OpenAI’s internal model found something unexpected: There may be no universal limit at all. Using high-dimensional geometry and abstract number structures far beyond human visual intuition, the AI generated hundreds of pages of rigorous reasoning showing you can create vastly more unit-distance relationships than experts ever thought possible. Why this matters: It challenges one of the longest-standing open problems in discrete geometry. It shows AI isn’t just solving known problems faster it’s discovering entirely new mathematical territory. It raises the real possibility that machines can now explore abstract realities humans never even thought to search for. The deeper implication is almost unsettling: For centuries, mathematics was considered the purest form of human reasoning. Now AI may be helping expand the very boundaries of proof itself finding truths no human mind can fully grasp intuitively. What happens when machines start discovering mathematical realities that feel alien even to the mathematicians who study them?show more

Donna Mills, 85, Makes OnlyFans History The Knots Landing... legend just launched her own OnlyFans — and she’s owning every bit of it. “I’ve always appreciated the incredible support from my fans,” Mills said. “Social media has been wonderful, but OnlyFans offers a more personal and direct connection.” She’ll be sharing daily life, behind-the-scenes moments, and real talk with the people who’ve stuck with her for decades. “Honey, I’ve been in this business over 50 years. I’ve seen every trend come and go. Why wouldn’t I jump in?” (Video: AI) “Life’s too short to sit on the sidelines. My fans have given me everything — this is my way of letting them in a little closer.” Left: Donna today at 85. Right: the Knots Landing era in the 1980s. If her fans want to see more of Donna Mills and she’s happy to share, more power to her. It’s a free country.show more

my team didn't want me to give this away... for free. But I'm going to do it anyway it's the SEO & AI search dashboard I built in Claude Code it connects to your Google Analytics (GA4) and Google Search Console and Claude Code builds it in 5 minutes and I made a Notion document and a skill file so you can build this in Claude Code yourself in literally minutes the dashboard has three tabs: 1. AI Search - How much traffic is coming from ChatGPT, Perplexity, and Gemini ETC. It aggregates the GA4 data and gives single number 2. Paid ads - which keywords rank top 3 for but still pay for ads on, you should cut these to save budget 3. Organic overview - sessions, conversions, top landing pages, demographics. The single view for what is working I built this because this is how I drive our SEO and AEO forward it gives me the insights I need to allocate budget and prioritize what content to work on next I decided to give it away because most companies have no idea AI search is already sending them traffic like this post and comment "AEOdashboard" and I'll send it overshow more