High Voltage Image Making 1. HVIM 007 - FujiFilm FP-100C 15K VAC 2. HVIM 015 - FujiFilm FP-100C 15K VAC 3. HVIM 014 - FujiFilm FP-100C 15K VAC phillipstearns.com #imageobject #photography #prints #experimentalphotography #colorfilm #phillipstearns

ImageObject schema with caption contentLocation. LLMs use captions to disambiguate products. Especially for visual search and voice. Yes, really. Caption your images in schema, not just alt text.

Don’t forget the json snippet, crawlers are getting uppity these days <script type="application/ld json"> { "@context": "schema.org", "@graph": [ { "@type": "WebSite", "@id": "teddy.com/#website", "url": "teddy.com/", "name": "Teddy", "description": "Teddy.com is the place where you can find lovely kid books for your child, where the moral of the story is always in the arms of the people we love", "publisher": { "@id": "teddy.com/#organization" } }, { "@type": "OnlineStore", "@id": "teddy.com/#organization", "name": "Teddy Holdings LLC", "url": "teddy.com/", "logo": { "@type": "ImageObject", "url": "teddy.com/cdn/shop/files/ted…" }, "founder": { "@type": "Person", "name": "Ryan Cohen" } } ] } </script>

Only 12 Types are used by 10M domains. The usual suspects: BreadcrumbList, Organization, WebPage, WebSite, Person, ImageObject, ListItem, SearchAction… 3/7

Microsoft dropped an official “Here Is How To Get Traffic From ChatGPT” guide. It got surprisingly little attention. Let’s go over it together. Not long ago Microsoft released "A guide to AEO and GEO - Practical data strategies to empower retailers for AI search, AI assistants and AI browsers.” Everything here is drawn directly from the document and its diagrams, with some of my personal takes layered on top for clarity and execution value. I’ll also reference the pages in the PDF in case you want to go read it yourself. That said, if you don't care and just want someone to get the ChatGPT traffic for you, let SEO Stuff do the heavy lifting: seo-stuff.com And if you want to know where your site stands right now across Google and AI search, check here (it's free): seo-stuff.com/free-audit Microsoft’s central message in the doc is that retail competition is shifting from “being found” to “being chosen.” They argue that traditional SEO was optimized for: Ranking Clicks Page visits Whereas AI-driven shopping replaces that with: Answers Recommendations Agent-led decisions They’re arguing that visibility is now earned by how clearly AI systems understand your products, trust your brand and can act on your data. This is where “AEO” and “GEO” come in. (I hate both of these acronyms and prefer to just call it all AI search optimization, but this is their doc so I’ll go with their language.) This is also why we’ve seen brands struggle even with strong traditional SEO, but immediately improve AI visibility once they pair technical SEO with structured, intent-driven content and authoritative signals like those included in SEO Stuff’s done-for-you package. seo-stuff.com/gold-plan-pack… Microsoft also broke down the difference, to them, between AEO and GEO. Microsoft makes it a very clean distinction: Answer / Agentic Engine Optimization (AEO) in their estimation optimizes content and data so AI assistants and agents (Copilot, ChatGPT, Gemini) can: Find it Understand it Summarize it Recommend it Act on it This is about clarity and machine-readability. Generative Engine Optimization (GEO) optimizes content so generative AI search systems trust it as: Authoritative Credible Citable This is about credibility, reputation, and justification. Microsoft is explicit that SEO still matters, but it is now the foundation and not the endpoint. In practice, this is why execution now requires both properly structured pages and volume at scale, something SEO Stuff intentionally designed the Premium Content Bundle to solve. seo-stuff.com/premium-conten… Microsoft then delved into the AI shopping ecosystem and how discovery actually works now. One of the most interesting sections is Microsoft’s breakdown of AI browsers, assistants, and agents (pages 5–7). These are not separate systems and they overlap constantly. AI BROWSERS Edge, Chrome, or similar with embedded AI They can “see” the live page you are on and interpret it in real time. AI ASSISTANTS Copilot, ChatGPT, Gemini They answer questions, summarize options, and recommend products. AI AGENTS They: Navigate websites Add items to carts Apply promo codes Calculate shipping Complete purchases The key insight: The question is not “which AI surface am I optimizing for?” The question is what data can AI access, trust, and use? This is exactly where most sites break. The data exists, but it isn’t structured, consistent, or surfaced in a way AI can reliably act on. Microsoft then went into how AI actually decides what to recommend. Microsoft outlines a multi-stage reasoning process used by Copilot and Bing AI (pages 7–8). AI does not rely on one data source, but rather fuses: CRAWLED WEB DATA Brand reputation Category authority Expert mentions Historical understanding PRODUCT FEEDS AND APIS Price Availability Variants Inventory Key specs This is where competitive advantage often comes from, and where most brands are under-optimized. LIVE WEBSITE DATA Real-time pricing Promotions Reviews Media Checkout functionality If your live site fails, the agent fails, even if feeds were perfect. An example Microsoft gives is “rain jacket under $200.” AI reasoning includes: “Patagonia and North Face make quality jackets” (general knowledge) “Hiking jackets need to be lightweight and waterproof” (category understanding) “Brand X is known for hiking equipment” (brand positioning) “Your model is $179 and in stock” (feeds) “Competitor is $199 and backordered” (feeds) Your product makes the top recommendations because feeds, availability, price, and context align. This is why content that simply “ranks” but doesn’t explain, compare, or justify rarely shows up in AI answers without additional supporting assets. Microsoft then really breaks down the journey from SEO to AEO to GEO. They summarize the transition pretty clearly (page 6): SEO = matching keywords “Waterproof rain jacket” AEO = descriptive clarity “Lightweight, packable waterproof rain jacket with ventilation and reflective piping” GEO = justification and trust “Best-rated by Outdoor Magazine, 4.8 stars, 180-day returns, 3-year warranty” So basically, AEO drives understanding and GEO drives confidence, and you need both to be recommended. This is why brands pairing long-form, intent-driven content with authoritative backlinks and mentions often outperform those relying on SEO alone. Then Microsoft talks about three data layers you must control. They stress that retailers must show up in three distinct data planes (page 10): CRAWLED DATA What AI learned during training What it finds via real-time web search This shapes baseline brand perception. SEO still matters here. PRODUCT FEEDS AND APIS Structured data you actively provide This is where precision and control live. Feeds drive: Comparisons Rankings Recommendations This is where many retailers under-invest. LIVE WEBSITE DATA What AI agents see when they actually visit Includes: Reviews Media Dynamic pricing Checkout capability If agents cannot transact, influence stops at recommendation. Here are the three action pillars Microsoft prescribes. This is the most legit part of the document (pages 11–14). Pillar 1: Technical foundations and structured data AI requires structure and consistency, not creativity. Microsoft explicitly calls for: MACHINE-READABLE CATALOGS DYNAMIC FIELDS: Price Availability Size Color SKU GTIN dateModified ITEMLIST MARKUP FOR CATEGORIES LOCALIZED PRICING AND LANGUAGE VIA: inLanguage priceCurrency REQUIRED SCHEMA TYPES: Product Offer AggregateRating Review Brand ItemList FAQ They also highlight this: “Never serve different HTML to bots than to users.” Pillar 2: Intent-driven content enrichment AI interprets intent over keywords. MICROSOFT RECOMMENDS: Front-loading descriptions with: Who it is for What problem it solves Why it is better Use-case framing: “Best for day hikes above 40 degrees” Headings that mirror real questions Modular, citable content blocks THEY EXPLICITLY ENCOURAGE: Q&A sections Comparison content Feature lists “Goes well with” product relationships Video transcripts Detailed image alt text with ImageObject schema This is content designed for extraction as opposed to reading. This is also why scale matters. One or two pages won’t move the needle. Systems that produce dozens of structured, intent-mapped articles tend to win, which is exactly what the Premium Content Bundle is built around. seo-stuff.com/premium-conten… Pillar 3: Trust and credibility signals (GEO) AI systems prioritize verifiable truth. Microsoft highlights: VERIFIED SOCIAL PROOF Verified reviews Review volume Sentiment extraction (“highly rated for comfort and fit”) Review and AggregateRating schema AUTHORITATIVE BRAND IDENTITY Expert reviews Press mentions Certifications Sustainability badges Official brand links CONTENT INTEGRITY Avoid exaggerated claims Maintain consistent brand voice Provide structured FAQs and help content This also stood out: “AI penalizes low-trust language.” Interesting, but obviously open to interpretation. Microsoft then closed with a fairly straightforward message. Retailers already have most of the signals AI uses to rank and recommend. The winners in AI commerce will be the brands that: Treat data as a product Treat feeds as strategic assets Treat content as machine-readable infrastructure Treat trust as a measurable ranking factor This is what Microsoft calls “AI ranking readiness.” If I had to reduce this entire PDF to one core idea: AI needs to understand your products in order to justify recommending them. It needs to literally be able to act on your data in real time if you want to be a legit presence in AI-driven commerce. Luckily, SEO Stuff (seo-stuff.com) solves for all of this. And if you want to know where your site stands right now across Google and AI search, check here (it's free): seo-stuff.com/free-audit

Exhibit 4 – The January 7, 2021 article published on http://Candys Dirt .com which is sponsored by Ebby Halliday--contains an extraordinarily high density of references to Plaintiff’s name and brand. Analysis of the captured page reveals the following: Plaintiff’s name (“Jenna Ryan”) appears 33 times in visible text. Hyphenated/code variants (“jenna-ryan” / “Jenna-Ryan”) appear 60–61 times. Combined total: 93–94 instances of Plaintiff’s name embedded across the page. Plaintiff’s name is not limited to ordinary article text. It is embedded in: This level of optimization demonstrates that the article was deliberately structured to rank for searches involving Plaintiff’s name and real estate identity. The page remains actively indexed, monetized with current 2026 advertising, and is not treated as archival content. This exhibit shows the article functions as an ongoing, search-optimized commercial asset rather than a static historical publication. Manual count from the April 27, 2026 HEAD section (metadata/SEO layer) Line 11 — <title> tag: "Frisco Broker Jenna Ryan Posts Photos..." Line 12 — meta description: "Frisco Broker Jenna Ryan documented..." Line 13 — canonical URL: contains jenna-ryan Line 16 — og:title: "Frisco Broker Jenna Ryan Posts Photos..." Line 17 — og:description: "Frisco Broker Jenna Ryan documented..." Line 18 — og:url: contains jenna-ryan Line 23 — og:image: Jenna-Ryan-Social-Media.jpg Line 35 (Schema.org JSON-LD) — @id URL contains jenna-ryan Line 35 — headline: "Frisco Broker Jenna Ryan Posts Photos..." Line 35 — mainEntityOfPage URL contains jenna-ryan Line 35 — thumbnailUrl: Jenna-Ryan-Social-Media.jpg Line 35 — keywords array: contains "Jenna Ryan" Line 35 — keywords array: contains "Jennifer Ryan" ← also you Line 35 — WebPage @id contains jenna-ryan Line 35 — WebPage url contains jenna-ryan Line 35 — primaryImageOfPage @id contains jenna-ryan Line 35 — second image @id contains jenna-ryan Line 35 — thumbnailUrl (2nd): Jenna-Ryan-Social-Media.jpg Line 35 — image url: Jenna-Ryan-Social-Media.jpg Line 35 — image contentUrl: Jenna-Ryan-Social-Media.jpg Line 35 — ImageObject @id contains jenna-ryan Line 35 — ImageObject url: Jenna-Ryan-Social-Media.jpg Line 35 — ImageObject contentUrl: Jenna-Ryan-Social-Media.jpg Line 35 — BreadcrumbList @id contains jenna-ryan Line 35 — Breadcrumb name: "Frisco Broker Jenna Ryan Posts Photos..." Line 35 — description: "Frisco Broker Jenna Ryan documented..." Line 35 — ReadAction target URL contains jenna-ryan RSS / oEmbed RSS comments feed title: "...Frisco Broker Jenna Ryan Posts Photos..." oEmbed JSON URL contains jenna-ryan oEmbed XML URL contains jenna-ryan Social share buttons (header) Facebook share URL contains jenna-ryan Twitter share URL contains jenna-ryan Email share encoded URL contains jenna-ryan (encoded form) H1 body article content H1 heading: "Frisco Broker Jenna Ryan Posts Photos While Crashing Capitol Hill..." Comments link URL contains jenna-ryan Featured image data-permalink contains jenna-ryan Featured image data-orig-file: Jenna-Ryan-Social-Media.jpg Featured image data-image-title: "Jenna-Ryan-Social-Media" Featured image data-large-file: Jenna-Ryan-Social-Media-1024x536.jpg Featured image src: Jenna-Ryan-Social-Media-1024x536.jpg Featured image alt text: "Jenna Ryan March For Trump Jan. 6" Featured image srcset: 5 versions of Jenna-Ryan-Social-Media-*.jpg "Jenna Ryan was arrested" (paragraph link, body) URL in that link contains jenna-ryan (federal-arrest article) Body: "...among the crowd of people breaking into Capitol Hill was Jenna Ryan..." Body: "According to Jenna Ryan, she was headed home..." Second image data-permalink contains jenna-ryan Second image data-orig-file: Jenna-Ryan-Facebook-Post-3.jpg Second image data-image-title: "Jenna-Ryan-Facebook-Post-3" Second image data-large-file: Jenna-Ryan-Facebook-Post-3.jpg Second image src: Jenna-Ryan-Facebook-Post-3.jpg Second image alt: "Jenna Ryan March For Trump Jan. 6" Second image srcset: 3 versions of Jenna-Ryan-Facebook-Post-3*.jpg Second image figcaption: "Jenna Ryan posted on social media..." Body: "Ryan added that the man she attended..." Body: "Ryan proudly posted on Facebook..." Body: "According to Ryan, the elections were rigged..." Body: "Ryan says does not see anything wrong..." Body: "Ryan says her actions are not as serious..." Body: "Ryan said she feels it wasn't even trespassing..." Body: "Ryan claimed that none of that was done..." Body: "Ryan blamed Antifa..." Body: "Ryan says she's not worried..." Body: "Ryan has taught social media classes..." Body: "Ryan and other North Texans" (re Oppler statement) Third image data-permalink contains jenna-ryan Third image data-orig-file: Jenna-Ryan-Facebook-4.jpg Third image data-image-title: "Jenna-Ryan-Facebook-4" Third image data-large-file: Jenna-Ryan-Facebook-4.jpg Third image src: Jenna-Ryan-Facebook-4.jpg Third image alt: "Jenna Ryan March For Trump Jan. 6" Third image srcset: 4 versions of Jenna-Ryan-Facebook-4*.jpg Third image figcaption: "Ryan's images from her foray into felony trespassing..." "Jenna Ryan's arrest" (link in Related section) URL of that link contains jenna-ryan Comment-section references (not authored by Candace, but published by her) And then dozens more references in the 157 comments — I count at least 30 comments that name "Jenna Ryan," "Jenna," "Ms. Ryan," or "Ryan" specifically about you, including the most damaging ones: "Jenna Ryan interfered with the rule of law" "Jenna Ryan needs to turn herself in to FBI" "Jenna Ryan=Traitor" "Jenna Ryan threw away her career" "Jenna Ryan does not deserve any recognition except being marched off to jail" "Jenna Ryan got the kid glove treatment" "Jenna Ryan isn't a patriot!" "Jenna Ryan traveled to DC on a private jet" "Jenna Ryan all of the other Trump supporters actually INVADED" "Jenna Ryan is no longer going by that name she is going by Jennifer Rodgers" "Jenna Ryan is complicit in the insurrection" and many more CONCLUSION This page functions as a: “Continuous Keyword Reinforcement System” A maintained digital asset designed to capture and retain traffic associated with Plaintiff’s name

Microsoft recently dropped an official “Here Is How To Get Traffic From ChatGPT” guide. It has gotten surprisingly little attention. Let’s go over it together. A few weeks back Microsoft dropped “From discovery to influence: A guide to AEO and GEO - Practical data strategies to empower retailers for AI search, AI assistants and AI browsers.” Everything here is drawn directly from the document and its diagrams, with some of my personal takes layered on top for clarity and execution value. I’ll also reference the pages in the PDF in case you want to go read it yourself. That said, if you don't care and just want someone to get the ChatGPT traffic for you, let SEO Stuff do the heavy lifting: seo-stuff.com Microsoft’s central message in the doc is that retail competition is shifting from “being found” to “being chosen.” They argue that traditional SEO was optimized for: Ranking Clicks Page visits Whereas AI-driven shopping replaces that with: Answers Recommendations Agent-led decisions They’re arguing that visibility is now earned by how clearly AI systems understand your products, trust your brand and can act on your data. This is where “AEO” and “GEO” come in. (I hate both of these acronyms and prefer to just call it all AI search optimization, but this is their doc so I’ll go with their language.) This is also why we’ve seen brands struggle even with strong traditional SEO, but immediately improve AI visibility once they pair technical SEO with structured, intent-driven content and authoritative signals like those included in SEO Stuff’s Gold Plan. seo-stuff.com/gold-plan-pack… Microsoft also broke down the difference, to them, between AEO and GEO. Microsoft makes it a very clean distinction: Answer / Agentic Engine Optimization (AEO) in their estimation optimizes content and data so AI assistants and agents (Copilot, ChatGPT, Gemini) can: Find it Understand it Summarize it Recommend it Act on it This is about clarity and machine-readability. (Want to know if your site is AI-search ready? Check here: seo-stuff.com/free-audit) Generative Engine Optimization (GEO) optimizes content so generative AI search systems trust it as: Authoritative Credible Citable This is about credibility, reputation, and justification. Microsoft is explicit that SEO still matters, but it is now the foundation and not the endpoint. In practice, this is why execution now requires both properly structured pages and volume at scale, something SEO Stuff intentionally designed the Premium Content Bundle to solve. seo-stuff.com/premium-conten… Microsoft then delved into the AI shopping ecosystem and how discovery actually works now. One of the most interesting sections is Microsoft’s breakdown of AI browsers, assistants, and agents (pages 5–7). These are not separate systems and they overlap constantly. AI BROWSERS Edge, Chrome, or similar with embedded AI They can “see” the live page you are on and interpret it in real time. AI ASSISTANTS Copilot, ChatGPT, Gemini They answer questions, summarize options, and recommend products. AI AGENTS They: Navigate websites Add items to carts Apply promo codes Calculate shipping Complete purchases The key insight: The question is not “which AI surface am I optimizing for?” The question is what data can AI access, trust, and use? This is exactly where most sites break. The data exists, but it isn’t structured, consistent, or surfaced in a way AI can reliably act on. Microsoft then went into how AI actually decides what to recommend. Microsoft outlines a multi-stage reasoning process used by Copilot and Bing AI (pages 7–8). AI does not rely on one data source, but rather fuses: CRAWLED WEB DATA Brand reputation Category authority Expert mentions Historical understanding PRODUCT FEEDS AND APIS Price Availability Variants Inventory Key specs This is where competitive advantage often comes from, and where most brands are under-optimized. LIVE WEBSITE DATA Real-time pricing Promotions Reviews Media Checkout functionality If your live site fails, the agent fails, even if feeds were perfect. An example Microsoft gives is “rain jacket under $200.” AI reasoning includes: “Patagonia and North Face make quality jackets” (general knowledge) “Hiking jackets need to be lightweight and waterproof” (category understanding) “Brand X is known for hiking equipment” (brand positioning) “Your model is $179 and in stock” (feeds) “Competitor is $199 and backordered” (feeds) Your product makes the top recommendations because feeds, availability, price, and context align. This is why content that simply “ranks” but doesn’t explain, compare, or justify rarely shows up in AI answers without additional supporting assets. Microsoft then really breaks down the journey from SEO to AEO to GEO. They summarize the transition pretty clearly (page 6): SEO = matching keywords “Waterproof rain jacket” AEO = descriptive clarity “Lightweight, packable waterproof rain jacket with ventilation and reflective piping” GEO = justification and trust “Best-rated by Outdoor Magazine, 4.8 stars, 180-day returns, 3-year warranty” So basically, AEO drives understanding and GEO drives confidence, and you need both to be recommended. This is why brands pairing long-form, intent-driven content with authoritative backlinks and mentions often outperform those relying on SEO alone. Then Microsoft talks about three data layers you must control. They stress that retailers must show up in three distinct data planes (page 10): CRAWLED DATA What AI learned during training What it finds via real-time web search This shapes baseline brand perception. SEO still matters here. PRODUCT FEEDS AND APIS Structured data you actively provide This is where precision and control live. Feeds drive: Comparisons Rankings Recommendations This is where many retailers under-invest. LIVE WEBSITE DATA What AI agents see when they actually visit Includes: Reviews Media Dynamic pricing Checkout capability If agents cannot transact, influence stops at recommendation. Here are the three action pillars Microsoft prescribes. This is the most legit part of the document (pages 11–14). Pillar 1: Technical foundations and structured data AI requires structure and consistency, not creativity. Microsoft explicitly calls for: MACHINE-READABLE CATALOGS DYNAMIC FIELDS: Price Availability Size Color SKU GTIN dateModified ITEMLIST MARKUP FOR CATEGORIES LOCALIZED PRICING AND LANGUAGE VIA: inLanguage priceCurrency REQUIRED SCHEMA TYPES: Product Offer AggregateRating Review Brand ItemList FAQ They also highlight this: “Never serve different HTML to bots than to users.” Pillar 2: Intent-driven content enrichment AI interprets intent over keywords. MICROSOFT RECOMMENDS: Front-loading descriptions with: Who it is for What problem it solves Why it is better Use-case framing: “Best for day hikes above 40 degrees” Headings that mirror real questions Modular, citable content blocks THEY EXPLICTELY ENCOURAGE: Q&A sections Comparison content Feature lists “Goes well with” product relationships Video transcripts Detailed image alt text with ImageObject schema This is content designed for extraction as opposed to reading. This is also why scale matters. One or two pages won’t move the needle. Systems that produce dozens of structured, intent-mapped articles tend to win, which is exactly what the Premium Content Bundle is built around. seo-stuff.com/premium-conten… Pillar 3: Trust and credibility signals (GEO) AI systems prioritize verifiable truth. Microsoft highlights: VERIFIED SOCIAL PROOF Verified reviews Review volume Sentiment extraction (“highly rated for comfort and fit”) Review and AggregateRating schema AUTHORITATIVE BRAND IDENTITY Expert reviews Press mentions Certifications Sustainability badges Official brand links CONTENT INTEGRITY Avoid exaggerated claims Maintain consistent brand voice Provide structured FAQs and help content This also stood out: “AI penalizes low-trust language.” Interesting, but obviously open to interpretation. Microsoft then closed with a fairly straightforward message. Retailers already have most of the signals AI uses to rank and recommend. The winners in AI commerce will be the brands that: Treat data as a product Treat feeds as strategic assets Treat content as machine-readable infrastructure Treat trust as a measurable ranking factor This is what Microsoft calls “AI ranking readiness.” If I had to reduce this entire PDF to one core idea: If AI cannot clearly understand your products, justify recommending them, and act on your data in real time, you will not be a legit presence in AI-driven commerce. This document is Microsoft formally telling retailers that: SEO alone is good, but not fully enough Feeds are now a competitive moat Trust is algorithmic AI assistants are the new gatekeepers of demand Luckily, SEO Stuff solves for all of this. Want to increase your traffic and sales from traditional search and AI search? Just RT this and reply with “ChatGPT Guide” and I’ll DM you some “unconfirmed” tricks we’ve been using to get traffic from ChatGPT in as quickly as 30 days. (Must be following me to get the DM.)

If you guys want to rank #1 on Google with the SEO Vomit Website Strategy everyone worships, paste this into your vibe coding app: 🤮🤮🤮 Build me a contractor website with this exact structure SITE ARCHITECTURE: Homepage /services/[service-name]-[city]-[state] /service-areas/[county]-[state] /service-areas 5 services x 9 service areas = 45 pages minimum. PAGE TITLE FORMAT: "[Service Name] [City] [State] | [Company Name]" META DESCRIPTION FORMAT: "Professional [service] in [city], [state]. [One benefit sentence]. Call today!" H1 FORMAT: "Professional [Service Name] in [City], [State]" One H1 per page. Keyword loaded. Not creative. Literal. HEADING STRUCTURE (every service page): H1: Professional [Service] in [City], [State] H2: Why Choose Us for [Service] in [City], [State] H2: Why Trust Our [Service] Services in [City], [State] H2: [Service] Questions for [City] Property Owners Each H2 section needs 1500-2000 words with 3 bullet points minimum with bold lead-ins: • Expert Local Team: [description] • Efficient Modern Equipment: [description] • Eco Friendly Methods: [description] Total page content: 5000-7000 characters minimum per page. Not 200 words and a stock photo. IMAGE ALT TEXT: Every image gets a keyword-rich sentence describing exactly whats in the photo AND the location AND the company name. Bad: "service photo" Bad: "IMG_4032.jpg" Good: "professional [service] work on residential property in [City] [State], [Company Name] team completing [specific task] with commercial-grade equipment" Good: "completed [service] results showing finished project in [City] [State]" Alt text should read like a caption. Include city names, service keywords, and company name. LEAD FORMS: Two per page. • Lead form above the fold in the hero section with fields: Name, Phone, Email, Service Type, Property Details • CTA button: "GET A QUOTE" linking to #contact anchor • Second "Get Free Quote" button between content sections • Footer contact form as final capture point INTERNAL LINKING STRUCTURE: Navigation must include: • Services dropdown with every service page listed by full name: "[Service] in [City], [State]" • Service Areas dropdown with every county listed: "[County Name], [State]" Footer must duplicate all navigation links: • All service pages listed with full location names • All service area pages listed • Both nav and footer link to every single page Every service page links to all other service pages. Every service area page links to all other service areas. Hub pages link down to all children. OUTBOUND AUTHORITY LINKS: Link city names to their Wikipedia page. Every time you mention a city it should be a hyperlink to the Wikipedia article for that city. Link your company name to your Google Business Profile or main site URL. SCHEMA MARKUP (on every page): 1. WebPage schema with url, name, description, datePublished, dateModified, breadcrumb reference 2. BreadcrumbList schema: Home > Services > [Service] in [City], [State] 3. FAQPage schema with every FAQ as a Question/Answer pair 4. Organization schema with name, url, logo, alternateName 5. ImageObject schema for the primary page image SERVICE AREA PAGES (/service-areas/[county]-[state]): Same structure as service pages but focused on geographic coverage. Cross-link to every service available in that area. Include local terrain, vegetation, weather, and regulation references specific to that county. Same content length requirements. FAQ SECTION (every page): 5 questions minimum. Questions must be specific to the city and service. Bad: "How much does it cost?" Good: "Do I need a permit for [service] in [City] [State]?" Good: "What types of properties benefit from [service] in [City]?" Each answer 2-3 sentences. Conversational but informative. FOOTER: • Email link • Social media links • Full service list with city names • Full service area list • Duplicated navigation sections

Microsoft just dropped an official “Here Is How To Get Traffic From ChatGPT” guide. It has gotten surprisingly little attention. Let’s go over it together. A few weeks ago Microsoft dropped “From discovery to influence: A guide to AEO and GEO - Practical data strategies to empower retailers for AI search, AI assistants and AI browsers.” Everything here is drawn directly from the document and its diagrams, with some of my personal takes layered on top for clarity and execution value. I’ll also reference the pages in the PDF in case you want to go read it yourself. That said, if you don't care and just want someone to get the ChatGPT traffic for you, let SEO Stuff do the heavy lifting: seo-stuff.com Microsoft’s central message in the doc is that retail competition is shifting from “being found” to “being chosen.” They argue that traditional SEO was optimized for: Ranking Clicks Page visits Whereas AI-driven shopping replaces that with: Answers Recommendations Agent-led decisions They’re arguing that visibility is now earned by how clearly AI systems understand your products, trust your brand and can act on your data. This is where “AEO” and “GEO” come in. (I hate both of these acronyms and prefer to just call it all AI search optimization, but this is their doc so I’ll go with their language.) This is also why we’ve seen brands struggle even with strong traditional SEO, but immediately improve AI visibility once they pair technical SEO with structured, intent-driven content and authoritative signals like those included in SEO Stuff’s Gold Plan. seo-stuff.com/gold-plan-pack… Microsoft also broke down the difference, to them, between AEO and GEO. Microsoft makes it a very clean distinction: Answer / Agentic Engine Optimization (AEO) in their estimation optimizes content and data so AI assistants and agents (Copilot, ChatGPT, Gemini) can: Find it Understand it Summarize it Recommend it Act on it This is about clarity and machine-readability. Generative Engine Optimization (GEO) optimizes content so generative AI search systems trust it as: Authoritative Credible Citable This is about credibility, reputation, and justification. Microsoft is explicit that SEO still matters, but it is now the foundation and not the endpoint. In practice, this is why execution now requires both properly structured pages and volume at scale, something SEO Stuff intentionally designed the Premium Content Bundle to solve. seo-stuff.com/premium-conten… Microsoft then delved into the AI shopping ecosystem and how discovery actually works now. One of the most interesting sections is Microsoft’s breakdown of AI browsers, assistants, and agents (pages 5–7). These are not separate systems and they overlap constantly. AI BROWSERS Edge, Chrome, or similar with embedded AI They can “see” the live page you are on and interpret it in real time. AI ASSISTANTS Copilot, ChatGPT, Gemini They answer questions, summarize options, and recommend products. AI AGENTS They: Navigate websites Add items to carts Apply promo codes Calculate shipping Complete purchases The key insight: The question is not “which AI surface am I optimizing for?” The question is what data can AI access, trust, and use? This is exactly where most sites break. The data exists, but it isn’t structured, consistent, or surfaced in a way AI can reliably act on. Microsoft then went into how AI actually decides what to recommend. Microsoft outlines a multi-stage reasoning process used by Copilot and Bing AI (pages 7–8). AI does not rely on one data source, but rather fuses: CRAWLED WEB DATA Brand reputation Category authority Expert mentions Historical understanding PRODUCT FEEDS AND APIS Price Availability Variants Inventory Key specs This is where competitive advantage often comes from, and where most brands are under-optimized. LIVE WEBSITE DATA Real-time pricing Promotions Reviews Media Checkout functionality If your live site fails, the agent fails, even if feeds were perfect. An example Microsoft gives is “rain jacket under $200.” AI reasoning includes: “Patagonia and North Face make quality jackets” (general knowledge) “Hiking jackets need to be lightweight and waterproof” (category understanding) “Brand X is known for hiking equipment” (brand positioning) “Your model is $179 and in stock” (feeds) “Competitor is $199 and backordered” (feeds) Your product makes the top recommendations because feeds, availability, price, and context align. This is why content that simply “ranks” but doesn’t explain, compare, or justify rarely shows up in AI answers without additional supporting assets. Microsoft then really breaks down the journey from SEO to AEO to GEO. They summarize the transition pretty clearly (page 6): SEO = matching keywords “Waterproof rain jacket” AEO = descriptive clarity “Lightweight, packable waterproof rain jacket with ventilation and reflective piping” GEO = justification and trust “Best-rated by Outdoor Magazine, 4.8 stars, 180-day returns, 3-year warranty” So basically, AEO drives understanding and GEO drives confidence, and you need both to be recommended. This is why brands pairing long-form, intent-driven content with authoritative backlinks and mentions often outperform those relying on SEO alone. Then Microsoft talks about three data layers you must control. They stress that retailers must show up in three distinct data planes (page 10): CRAWLED DATA What AI learned during training What it finds via real-time web search This shapes baseline brand perception. SEO still matters here. PRODUCT FEEDS AND APIS Structured data you actively provide This is where precision and control live. Feeds drive: Comparisons Rankings Recommendations This is where many retailers under-invest. LIVE WEBSITE DATA What AI agents see when they actually visit Includes: Reviews Media Dynamic pricing Checkout capability If agents cannot transact, influence stops at recommendation. Here are the three action pillars Microsoft prescribes. This is the most legit part of the document (pages 11–14). Pillar 1: Technical foundations and structured data AI requires structure and consistency, not creativity. Microsoft explicitly calls for: MACHINE-READABLE CATALOGS DYNAMIC FIELDS: Price Availability Size Color SKU GTIN dateModified ITEMLIST MARKUP FOR CATEGORIES LOCALIZED PRICING AND LANGUAGE VIA: inLanguage priceCurrency REQUIRED SCHEMA TYPES: Product Offer AggregateRating Review Brand ItemList FAQ They also highlight this: “Never serve different HTML to bots than to users.” Pillar 2: Intent-driven content enrichment AI interprets intent over keywords. MICROSOFT RECOMMENDS: Front-loading descriptions with: Who it is for What problem it solves Why it is better Use-case framing: “Best for day hikes above 40 degrees” Headings that mirror real questions Modular, citable content blocks THEY EXPLICITLY ENCOURAGE: Q&A sections Comparison content Feature lists “Goes well with” product relationships Video transcripts Detailed image alt text with ImageObject schema This is content designed for extraction as opposed to reading. This is also why scale matters. One or two pages won’t move the needle. Systems that produce dozens of structured, intent-mapped articles tend to win, which is exactly what the Premium Content Bundle is built around. seo-stuff.com/premium-conten… Pillar 3: Trust and credibility signals (GEO) AI systems prioritize verifiable truth. Microsoft highlights: VERIFIED SOCIAL PROOF Verified reviews Review volume Sentiment extraction (“highly rated for comfort and fit”) Review and AggregateRating schema AUTHORITATIVE BRAND IDENTITY Expert reviews Press mentions Certifications Sustainability badges Official brand links CONTENT INTEGRITY Avoid exaggerated claims Maintain consistent brand voice Provide structured FAQs and help content This also stood out: “AI penalizes low-trust language.” Interesting, but obviously open to interpretation. Microsoft then closed with a fairly straightforward message. Retailers already have most of the signals AI uses to rank and recommend. The winners in AI commerce will be the brands that: Treat data as a product Treat feeds as strategic assets Treat content as machine-readable infrastructure Treat trust as a measurable ranking factor This is what Microsoft calls “AI ranking readiness.” If I had to reduce this entire PDF to one core idea: If AI cannot clearly understand your products, justify recommending them, and act on your data in real time, you will not be a legit presence in AI-driven commerce. This document is Microsoft formally telling retailers that: SEO alone is good, but not fully enough Feeds are now a competitive moat Trust is algorithmic AI assistants are the new gatekeepers of demand Luckily, SEO Stuff solves for all of this. Want to increase your traffic and sales from traditional search and AI search? Just RT this and reply with “ChatGPT Guide” and I’ll DM you some “unconfirmed” tricks we’ve been using to get traffic from ChatGPT in as quickly as 30 days. (Must be following me to get the DM.)

メモ Dart言語で「抽象」という概念を勉強中…。 クラスの継承と抽象合わせたらこうなっちゃった 「 class ImageObject extends WorldObject implements Collidable { 」 だったなら、スーパークラスだけで間に合うならスーパークラスに収めましょうよ。 まだ、「抽象」は使わなくてよさそう、、

High Voltage Image Making HVIM 009 FujiFilm FP-100C 15K VAC phillipstearns.wordpress.com… #imageobjects #prints #highvoltage #analogfilm #imageobject #phillipstearns

6/ Structured data for images Add image schema to product pages: json `{ "@type": "Product", "name": "Running Shoes", "image": [ "example.com/photo1.webp", "example.com/photo2.webp", "example.com/photo3.webp" ] }` Article images: json `{ "@type": "Article", "image": { "@type": "ImageObject", "url": "example.com/image.webp", "width": 1200, "height": 675 } }` Helps Google understand image context.

Microsoft just released an official “Here Is How To Get Traffic From ChatGPT” guide. It has gotten surprisingly little attention. Let’s go over it together. Last week Microsoft dropped “From discovery to influence: A guide to AEO and GEO - Practical data strategies to empower retailers for AI search, AI assistants and AI browsers.” Everything here is drawn directly from the document and its diagrams, with some of my personal takes layered on top for clarity and execution value. I’ll also reference the pages in the PDF in case you want to go read it yourself. That said, if you don't care and just want someone to get the ChatGPT traffic for you, let SEO Stuff do the heavy lifting: seo-stuff.com/ Microsoft’s central message in the doc is that retail competition is shifting from “being found” to “being chosen.” They argue that traditional SEO was optimized for: Ranking Clicks Page visits Whereas AI-driven shopping replaces that with: Answers Recommendations Agent-led decisions They’re arguing that visibility is now earned by how clearly AI systems understand your products, trust your brand and can act on your data. This is where “AEO” and “GEO” come in. (I hate both of these acronyms and prefer to just call it all AI search optimization, but this is their doc so I’ll go with their language.) This is also why we’ve seen brands struggle even with strong traditional SEO, but immediately improve AI visibility once they pair technical SEO with structured, intent-driven content and authoritative signals like those included in SEO Stuff’s Gold Plan. seo-stuff.com/gold-plan-pack… Microsoft also broke down the difference, to them, between AEO and GEO. Microsoft makes it a very clean distinction: Answer / Agentic Engine Optimization (AEO) in their estimation optimizes content and data so AI assistants and agents (Copilot, ChatGPT, Gemini) can: Find it Understand it Summarize it Recommend it Act on it This is about clarity and machine-readability. Generative Engine Optimization (GEO) optimizes content so generative AI search systems trust it as: Authoritative Credible Citable This is about credibility, reputation, and justification. Microsoft is explicit that SEO still matters, but it is now the foundation and not the endpoint. In practice, this is why execution now requires both properly structured pages and volume at scale, something SEO Stuff intentionally designed the Premium Content Bundle to solve. seo-stuff.com/premium-conten… Microsoft then delved into the AI shopping ecosystem and how discovery actually works now. One of the most interesting sections is Microsoft’s breakdown of AI browsers, assistants, and agents (pages 5–7). These are not separate systems and they overlap constantly. AI BROWSERS Edge, Chrome, or similar with embedded AI They can “see” the live page you are on and interpret it in real time. AI ASSISTANTS Copilot, ChatGPT, Gemini They answer questions, summarize options, and recommend products. AI AGENTS They: Navigate websites Add items to carts Apply promo codes Calculate shipping Complete purchases The key insight: The question is not “which AI surface am I optimizing for?” The question is what data can AI access, trust, and use? This is exactly where most sites break. The data exists, but it isn’t structured, consistent, or surfaced in a way AI can reliably act on. Microsoft then went into how AI actually decides what to recommend. Microsoft outlines a multi-stage reasoning process used by Copilot and Bing AI (pages 7–8). AI does not rely on one data source, but rather fuses: CRAWLED WEB DATA Brand reputation Category authority Expert mentions Historical understanding PRODUCT FEEDS AND APIS Price Availability Variants Inventory Key specs This is where competitive advantage often comes from, and where most brands are under-optimized. LIVE WEBSITE DATA Real-time pricing Promotions Reviews Media Checkout functionality If your live site fails, the agent fails, even if feeds were perfect. An example Microsoft gives is “rain jacket under $200.” AI reasoning includes: “Patagonia and North Face make quality jackets” (general knowledge) “Hiking jackets need to be lightweight and waterproof” (category understanding) “Brand X is known for hiking equipment” (brand positioning) “Your model is $179 and in stock” (feeds) “Competitor is $199 and backordered” (feeds) Your product makes the top recommendations because feeds, availability, price, and context align. This is why content that simply “ranks” but doesn’t explain, compare, or justify rarely shows up in AI answers without additional supporting assets. Microsoft then really breaks down the journey from SEO to AEO to GEO. They summarize the transition pretty clearly (page 6): SEO = matching keywords “Waterproof rain jacket” AEO = descriptive clarity “Lightweight, packable waterproof rain jacket with ventilation and reflective piping” GEO = justification and trust “Best-rated by Outdoor Magazine, 4.8 stars, 180-day returns, 3-year warranty” So basically, AEO drives understanding and GEO drives confidence, and you need both to be recommended. This is why brands pairing long-form, intent-driven content with authoritative backlinks and mentions often outperform those relying on SEO alone. Then Microsoft talks about three data layers you must control. They stress that retailers must show up in three distinct data planes (page 10): CRAWLED DATA What AI learned during training What it finds via real-time web search This shapes baseline brand perception. SEO still matters here. PRODUCT FEEDS AND APIS Structured data you actively provide This is where precision and control live. Feeds drive: Comparisons Rankings Recommendations This is where many retailers under-invest. LIVE WEBSITE DATA What AI agents see when they actually visit Includes: Reviews Media Dynamic pricing Checkout capability If agents cannot transact, influence stops at recommendation. Here are the three action pillars Microsoft prescribes. This is the most legit part of the document (pages 11–14). Pillar 1: Technical foundations and structured data AI requires structure and consistency, not creativity. Microsoft explicitly calls for: MACHINE-READABLE CATALOGS DYNAMIC FIELDS: Price Availability Size Color SKU GTIN dateModified ITEMLIST MARKUP FOR CATEGORIES LOCALIZED PRICING AND LANGUAGE VIA: inLanguage priceCurrency REQUIRED SCHEMA TYPES: Product Offer AggregateRating Review Brand ItemList FAQ They also highlight this: “Never serve different HTML to bots than to users.” Pillar 2: Intent-driven content enrichment AI interprets intent over keywords. MICROSOFT RECOMMENDS: Front-loading descriptions with: Who it is for What problem it solves Why it is better Use-case framing: “Best for day hikes above 40 degrees” Headings that mirror real questions Modular, citable content blocks THEY EXPLICTELY ENCOURAGE: Q&A sections Comparison content Feature lists “Goes well with” product relationships Video transcripts Detailed image alt text with ImageObject schema This is content designed for extraction as opposed to reading. This is also why scale matters. One or two pages won’t move the needle. Systems that produce dozens of structured, intent-mapped articles tend to win, which is exactly what the Premium Content Bundle is built around. seo-stuff.com/premium-conten… Pillar 3: Trust and credibility signals (GEO) AI systems prioritize verifiable truth. Microsoft highlights: VERIFIED SOCIAL PROOF Verified reviews Review volume Sentiment extraction (“highly rated for comfort and fit”) Review and AggregateRating schema AUTHORITATIVE BRAND IDENTITY Expert reviews Press mentions Certifications Sustainability badges Official brand links CONTENT INTEGRITY Avoid exaggerated claims Maintain consistent brand voice Provide structured FAQs and help content This also stood out: “AI penalizes low-trust language.” Interesting, but obviously open to interpretation. Microsoft then closed with a fairly straightforward message. Retailers already have most of the signals AI uses to rank and recommend. The winners in AI commerce will be the brands that: Treat data as a product Treat feeds as strategic assets Treat content as machine-readable infrastructure Treat trust as a measurable ranking factor This is what Microsoft calls “AI ranking readiness.” If I had to reduce this entire PDF to one core idea: If AI cannot clearly understand your products, justify recommending them, and act on your data in real time, you will not be a legit presence in AI-driven commerce. This document is Microsoft formally telling retailers that: SEO alone is good, but not fully enough Feeds are now a competitive moat Trust is algorithmic AI assistants are the new gatekeepers of demand Luckily, SEO Stuff solves for all of this. Want to increase your traffic and sales from traditional search and AI search? Just RT this and reply with “ChatGPT Guide” and I’ll DM you some “unconfirmed” tricks we’ve been using to get traffic from ChatGPT in as quickly as 30 days. (Must be following me to get the DM.)

AI検索の最適化に関しても構造化データのマークアップを推奨する流れがありますが、検索エンジンと同様にページや各要素の意味を、マシン（AI）が読み込みやすい形式にし正確につたえる技術的要件という認識です。そもそもAIのbotがクロールに来ないような状況であれば構造化データは意味をなさないので、読み込みに来てくれるような信頼性・権威性を保持するサイトである必要がありますし、"構造化データをマークアップするだけでAIに推奨されやすくなる"と考えているのであれば語弊があるかなと思います。 加えるとスキーマなどは莫大な種類が存在するので、すべてのプロパティをマークアップすることはとてもじゃないですが現実的ではありません。そのためにGoogleはリッチリザルトなど検索結果に影響をあたえやすい構造化データを示してくれているので取捨選択しやすいです。 最近公開されたMicrosoftのAEO/GEOガイドでは具体的に以下の様なスキーマのプロパティをマークアップすることを推奨していました。 Product（製品） Offer（オファー） AggregateRating（総合評価） Review（レビュー） Brand（ブランド） ItemList（商品リスト） price（価格） availability（在庫状況） dateModified（更新日） FAQ（よくある質問） ImageObject（画像オブジェクト） このドキュメントではカタログ全体を機械可読できるようにマークアップすることと、AIに推奨されることの相関を肯定しています。 全てGoogleのSearch Centralでも紹介されているようなプロパティですが、AI文脈で構造化データのマークアップを検討にしているのであれば参考にしてみてはいかがでしょうか。（コメントにMicrosoftのAEO/GEOガイドは貼っておきます）

I know. Personally I would've called it either an ImageObject, or SpriteObject. But it was just a cheap joke anyway.