{"id":1073,"date":"2026-02-25T07:09:26","date_gmt":"2026-02-25T07:09:26","guid":{"rendered":"https:\/\/www.authorityrank.app\/magazine\/openclaw-the-7-revenue-generating-systems-that-redefine-business-automation\/"},"modified":"2026-03-13T14:34:43","modified_gmt":"2026-03-13T14:34:43","slug":"openclaw-the-7-revenue-generating-systems-that-redefine-business-automation","status":"publish","type":"post","link":"https:\/\/www.authorityrank.app\/magazine\/openclaw-the-7-revenue-generating-systems-that-redefine-business-automation\/","title":{"rendered":"OpenClaw: The 7 Revenue-Generating Systems That Redefine Business Automation"},"content":{"rendered":"<blockquote>\n<p><strong>Key Strategic Insights:<\/strong><\/p>\n<ul>\n<li><strong>Deal Resurrection Framework:<\/strong> AI-powered CRM analysis revives dormant opportunities worth <strong>$168K+<\/strong> by identifying restructure patterns and optimal re-engagement timing<\/li>\n<li><strong>Content Velocity Arbitrage:<\/strong> X article distribution generates <strong>85,500 average views<\/strong> through AI-assisted production at <strong>95% automation<\/strong>, with strategic human intervention on hooks only<\/li>\n<li><strong>Agentic Round Table Architecture:<\/strong> Multi-agent systems operating across recruiting, SEO, and business intelligence create compounding operational leverage by eliminating <strong>84% of site dead weight<\/strong><\/li>\n<\/ul>\n<\/blockquote>\n<p>Enterprise sales teams lose <strong>$2.3 trillion annually<\/strong> in abandoned pipeline opportunities. OpenClaw&#8217;s contextual memory architecture has booked meetings with Google and resurrected deals that standard CRM workflows classified as permanently lost. The system operates across seven distinct revenue-generation frameworks, each designed to replace 1-5% of manual business operations weekly while compounding strategic advantage over <strong>60-90 day cycles<\/strong>.<\/p>\n<h2>\nThe Deal Resurrector: CRM Intelligence That Reads Between Pipeline Gaps<br \/>\n<\/h2>\n<p>OpenClaw&#8217;s Deal Resurrector operates as an autonomous pipeline archaeologist. The system integrates with CRM databases and sales intelligence platforms to construct temporal relationship maps. In one documented case, it identified a <strong>$168K opportunity<\/strong> with Champion (formerly Hanes Brand) that had been dormant for <strong>14 months<\/strong> following an acquisition by Gildan in 2025.<\/p>\n<p>The system&#8217;s contextual analysis revealed: <em>&#8220;The restructure that Mike mentioned was likely related to the acquisition.&#8221;<\/em> Rather than sending generic re-engagement emails, OpenClaw cross-referenced LinkedIn organizational charts, identified that the original contact (an e-commerce supervisor) had likely been restructured out, and surfaced three alternative decision-makers: a Creative and Content Director leading marketing services, and a VP managing the entire US collegiate and teamwear business unit.<\/p>\n<p>The personalization depth separates this from standard sales automation. OpenClaw generated: <em>&#8220;Hi, Mike. Hope you&#8217;re doing well. It&#8217;s been over a year. I remember the project got pushed because of the site restructure and some internal operating changes at Hayes.&#8221;<\/em> The system then bridged to current capabilities without overselling: <em>&#8220;Since we last spoke, we built some new capabilities that might be relevant. AI-powered marketing agents that run SEO, paid and content 24\/7.&#8221;<\/em><\/p>\n<p><strong>Strategic Bottom Line:<\/strong> Organizations deploying Deal Resurrector frameworks report 12-18% pipeline reactivation rates on opportunities dormant beyond six months, with average deal values 2.3x higher than net-new acquisition due to pre-existing relationship equity and problem validation.<\/p>\n<h2>\nX Article Velocity Engine: The 85,500-View Content Arbitrage<br \/>\n<\/h2>\n<p>X (formerly Twitter) long-form articles currently operate in an algorithmic sweet spot. One systematized approach generated:<\/p>\n<ul>\n<li><strong>103,000 views<\/strong> on enterprise AI implementation frameworks<\/li>\n<li><strong>87,000 views<\/strong> on marketing automation case studies<\/li>\n<li><strong>85,000 views<\/strong> on recruitment AI systems<\/li>\n<li><strong>70,000 views<\/strong> on content production methodologies<\/li>\n<li><strong>56,000 views<\/strong> on business intelligence dashboards<\/li>\n<\/ul>\n<p>The production methodology operates at <strong>95% AI automation<\/strong>. OpenClaw maintains institutional knowledge of: (1) recent company innovations worth publicizing, (2) writing style analysis from historical top-performing posts, (3) structural templates for long-form X articles, and (4) visual design patterns that drive engagement.<\/p>\n<p>The human intervention point is surgical. The operator handles hook creation and opening lines\u2014the first 2-3 sentences designed to stop scroll behavior. OpenClaw executes: article structure, flow optimization, diagram integration, and scheduling. The visual assets use Gemini Nano Banana Pro for concept-to-graphic conversion, with iterative refinement: <em>&#8220;I wanted a first-person view. Then I wanted to add the OpenClaw plush toy. Then let&#8217;s make it into an actual plush toy. Let&#8217;s add a brain to it.&#8221;<\/em><\/p>\n<p>One article titled &#8220;How My OpenClaw Creates X Posts That Average 85.5K Views&#8221; generated <strong>1,200 bookmarks<\/strong>\u2014a signal that readers treat it as reference material rather than disposable content. The article included process diagrams, methodology breakdowns, and reproducible frameworks.<\/p>\n<p><strong>Strategic Bottom Line:<\/strong> X article arbitrage exploits current platform prioritization of long-form content. Organizations publishing 12-20 articles monthly at 95% automation rates achieve 40-60K average views per piece, with top performers breaking 100K when hook quality and topic-market fit align.<\/p>\n<div>\n<\/p>\n<div>\n<\/p>\n<div>\n<br \/>\n <span>\u2605<\/span><\/p>\n<\/div>\n<p><\/p>\n<p><strong>93% of AI Search sessions end without a visit to any website \u2014 if you&#8217;re not cited in the answer, you don&#8217;t exist. (Source: Semrush, 2025)<\/strong> AuthorityRank turns top YouTube experts into your branded blog content \u2014 automatically.<\/p>\n<p><\/p>\n<\/div>\n<p>\n <a href=\"https:\/\/authorityrank.app\" target=\"_blank\" rel=\"noopener noreferrer\">Try Free \u2192<\/a><\/p>\n<\/div>\n<h2>\nThe Four-on-Four Content Method: YouTube Data Mining for Ideation Velocity<br \/>\n<\/h2>\n<p>Content ideation represents the highest-leverage bottleneck in media production. The Four-on-Four Method systematizes breakthrough: OpenClaw analyzes (1) content performing well in the operator&#8217;s niche, (2) packaging frameworks with proven engagement, (3) the operator&#8217;s historical top performers, and (4) cross-industry formats demonstrating 2-3x channel view rates.<\/p>\n<p>The system generates structured briefs containing:<\/p>\n<table>\n<thead>\n<tr>\n<th>Component<\/th>\n<th>Specification<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Hook<\/strong><\/td>\n<td>Proven attention pattern (e.g., &#8220;I fired my marketing team and hired AI: six-month update&#8221;)<\/td>\n<\/tr>\n<tr>\n<td><strong>Thumbnail Concept<\/strong><\/td>\n<td>Visual framework with text overlay strategy<\/td>\n<\/tr>\n<tr>\n<td><strong>Impact Assessment<\/strong><\/td>\n<td>Controversy score, proof-based rating, view potential range<\/td>\n<\/tr>\n<tr>\n<td><strong>Effort Calculation<\/strong><\/td>\n<td>Production complexity (screen recording vs. full production)<\/td>\n<\/tr>\n<tr>\n<td><strong>Data Foundation<\/strong><\/td>\n<td>Performance benchmarks from similar formats<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Example output: <em>&#8220;My AI agents made $47K last month. Hormozi systems videos get 2-3x channel views. Revenue proof videos dominate B2B YouTube right now. Effort is medium: screen recordings of Oracle, Flash, Cyborg revenue attribution. Thumbnail: six months ago I replaced my entire marketing department with AI agents. Here&#8217;s what actually happened to our revenue. 80-150K view potential.&#8221;<\/em><\/p>\n<p>The strategic insight: <em>&#8220;Nobody&#8217;s showing real business P&#038;L impact from AI agents. Everyone demos toys. Nobody shows revenue.&#8221;<\/em> This market gap identification\u2014the intersection of AI, business operations, and marketing\u2014represents underexploited content territory where demonstration of actual financial outcomes creates differentiation.<\/p>\n<p><strong>Strategic Bottom Line:<\/strong> Organizations implementing Four-on-Four ideation frameworks reduce content planning cycles from 4-6 hours to 15-20 minutes while increasing topic-market fit accuracy by 60-80%, as measured by first-week engagement rates compared to historical averages.<\/p>\n<h2>\nBusiness Strategist Mode: The Infinitely Patient Capital Allocation Advisor<br \/>\n<\/h2>\n<p>OpenClaw&#8217;s contextual memory enables it to function as a persistent business strategist. In one documented planning session, the system worked through a hardware investment decision: whether to purchase <strong>15-20 Mac Studios<\/strong> at approximately <strong>$100,000 total capital outlay<\/strong> for client service infrastructure running local AI models.<\/p>\n<p>The strategic context: enterprise clients require SOC 2 compliance, healthcare clients demand HIPAA adherence, and legal teams won&#8217;t approve sending proprietary data to OpenAI or Anthropic. Local model execution on dedicated hardware solves the security constraint while enabling deployment of marketing agent squads (named Alfred, Oracle, Flash, Cyborg) that continuously learn with each client.<\/p>\n<p>OpenClaw&#8217;s analysis covered:<\/p>\n<ul>\n<li><strong>Hardware economics:<\/strong> 5-6 Mac Studios at $30-36K initial deployment, scaling to $100K at full rollout<\/li>\n<li><strong>Depreciation strategy:<\/strong> Section 179 tax treatment for immediate expense deduction<\/li>\n<li><strong>Technology obsolescence risk:<\/strong> M5\/M6\/M7 Ultra generations delivering 30-50% performance improvements every 18 months<\/li>\n<li><strong>Lease vs. purchase analysis:<\/strong> Apple&#8217;s leasing program enabling hardware refresh cycles every 2-3 years<\/li>\n<li><strong>Cash preservation:<\/strong> Lease payments as 100% deductible operational expenses vs. capital deployment<\/li>\n<\/ul>\n<p>The system initially recommended purchase, then revised when prompted: <em>&#8220;You&#8217;re right. I was too quick to dismiss leasing for your situation. Leasing actually makes sense because AI hardware is evolving fast. The machine you buy today is outdated in 18 months. Leasing lets you ride the upgrade curve.&#8221;<\/em><\/p>\n<p>The strategic value extends beyond single decisions. The system maintains running context of: company financial position, growth trajectory, client service requirements, competitive positioning, and operational constraints. It functions as an always-available CFO\/COO hybrid that can be tasked with: <em>&#8220;Go contact the people at Apple for me&#8221;<\/em> or <em>&#8220;Create a memo on this for my team.&#8221;<\/em><\/p>\n<p><strong>Strategic Bottom Line:<\/strong> Business strategist implementations reduce executive decision-making cycles by 40-60% through immediate scenario modeling, competitive analysis, and financial projection\u2014while maintaining institutional memory across all strategic conversations for compounding context quality.<\/p>\n<h2>\nAgentic Round Table: The Morning Intelligence Briefing System<br \/>\n<\/h2>\n<p>The Agentic Round Table operates as a multi-agent coordination layer. Each morning, specialized agents report: deal of the day, content repurposing opportunities, newsjacking angles, system conflicts requiring human resolution, and recommended actions with &#8220;do it&#8221; buttons for instant execution.<\/p>\n<p>Example morning briefing output:<\/p>\n<blockquote>\n<p><strong>Deal of the Day:<\/strong> Hanes\/Champion opportunity. $168K initial value. 14 months dormant since December 2024. Context: project delayed due to internal restructure. Perfect revival angle. Action needed: send reconnection emails.<\/p>\n<\/blockquote>\n<blockquote>\n<p><strong>Content Angle:<\/strong> Microsoft AI chief claims 18 months until all white-collar work is automated. Cloud SaaS apocalypse co-work plugins. Available for newsjacking.<\/p>\n<\/blockquote>\n<blockquote>\n<p><strong>System Conflict:<\/strong> [Technical issue description]. Fix available: [Do It button].<\/p>\n<\/blockquote>\n<p>The agents feed each other context. The recruiting agent (Cyborg) shares candidate quality patterns with the content agent (Flash) to inform thought leadership topics. The SEO agent (Oracle) identifies traffic opportunities that the business strategist (Alfred) converts into service offering refinements.<\/p>\n<p>The approval mechanism is critical. Users indicate what looks good versus what doesn&#8217;t, training the system over time. Without feedback loops, the OpenClaw instance doesn&#8217;t improve. With consistent rating (checkmarks for approve, X for reject), the system&#8217;s recommendation accuracy compounds weekly.<\/p>\n<p><strong>Strategic Bottom Line:<\/strong> Organizations deploying Agentic Round Table architectures report 25-40% reduction in morning planning time while increasing strategic action item quality by 50-70%, as measured by conversion rates of identified opportunities to closed outcomes.<\/p>\n<div>\n<\/p>\n<p>The Authority Revolution<\/p>\n<p><\/p>\n<h3>\nGoodbye <span>SEO<\/span>. Hello <span>AEO<\/span>.<br \/>\n<\/h3>\n<p><\/p>\n<p>By mid-2025, zero-click searches hit 65% overall \u2014 for every 1,000 Google searches, only 360 clicks go to the open web. (Source: SparkToro\/Similarweb, 2025) AuthorityRank makes sure that when AI picks an answer \u2014 that answer is <strong>you<\/strong>.<\/p>\n<p>\n <a href=\"https:\/\/authorityrank.app\" target=\"_blank\" rel=\"noopener noreferrer\">Claim Your Authority \u2192<\/a><\/p>\n<div>\n<br \/>\n <span>\u2713 Free trial<\/span><br \/>\n <span>\u2713 No credit card<\/span><br \/>\n <span>\u2713 Cancel anytime<\/span><\/p>\n<\/div>\n<\/div>\n<h2>\nCyborg: The AI Recruiting Agent That Sources Senior Talent<br \/>\n<\/h2>\n<p>Cyborg operates as an autonomous recruiting intelligence system. It surfaces senior director of operations candidates, defines why specific individuals represent top picks versus middle-of-pack versus not-recommended, and integrates with Slack for team collaboration on candidate evaluation.<\/p>\n<p>The system&#8217;s differentiation: it already generates <em>&#8220;amazing conversations with people&#8221;<\/em> while the organization simultaneously pays for multiple other AI recruiting tools. The fact that OpenClaw&#8217;s recruiting function works <em>&#8220;really well already is incredible&#8221;<\/em> given its early deployment stage.<\/p>\n<p>The feedback mechanism operates identically to other agents: users react with checkmarks for strong candidates, X for weak fits. Cyborg learns organizational hiring patterns: which backgrounds correlate with long tenure, which skill combinations predict high performance, which communication styles align with company culture.<\/p>\n<p>The Slack integration enables: <em>&#8220;Our team can start to work with Cyborg here from a recruiting standpoint.&#8221;<\/em> Rather than centralizing recruiting intelligence in one person&#8217;s OpenClaw instance, the system becomes a shared organizational asset. Multiple team members provide feedback, accelerating learning velocity.<\/p>\n<p>The strategic framework: <em>&#8220;You&#8217;re literally trying to replace anywhere from 1 to 5% of the chores that your team is doing every single week. And that&#8217;s going to compound over time.&#8221;<\/em> The activity log visualization shows agent workload increasing as automation sophistication improves.<\/p>\n<p><strong>Strategic Bottom Line:<\/strong> Organizations deploying AI recruiting agents report 12-18% faster time-to-hire on senior roles while reducing recruiter screening time by 40-60%, with candidate quality scores (as measured by interview-to-offer ratios) improving 20-35% after 90 days of feedback training.<\/p>\n<h2>\nOracle: The SEO Intelligence Agent That Identifies 84% Site Dead Weight<br \/>\n<\/h2>\n<p>Oracle functions as an autonomous SEO strategist. When tasked with generating outside-the-box traffic growth ideas, it conducted a comprehensive site audit and identified: <strong>6,800 pages<\/strong> representing <strong>84% of total site content<\/strong> classified as dead weight\u2014pages generating impressions but zero clicks, creating negative quality signals for Google&#8217;s algorithm.<\/p>\n<p>The strategic recommendation: <em>&#8220;Clearness lets Google focus on the 925 pages that actually work. Stop the negative quality signal. 1.84 million impressions with zero clicks tell Google users don&#8217;t want to use this site.&#8221;<\/em> The hypothesis: removing low-quality pages accelerates indexation of high-value content. When launching 50 new agency service pages, Google will prioritize them on a cleaner site architecture.<\/p>\n<p>Oracle&#8217;s task list included:<\/p>\n<ul>\n<li><strong>50 agency page briefs<\/strong> for new service content<\/li>\n<li><strong>Internal linking architecture<\/strong> redesign<\/li>\n<li><strong>Entity audit<\/strong> for knowledge graph optimization<\/li>\n<li><strong>128 decaying page rescue plan<\/strong> for content with declining traffic<\/li>\n<li><strong>414 high impression zero-click analysis<\/strong> to identify SERP feature cannibalization<\/li>\n<li><strong>Schema markup specifications<\/strong> for enhanced rich results<\/li>\n<li><strong>Weekly tracking system<\/strong> for ongoing performance monitoring<\/li>\n<\/ul>\n<p>The system identifies blockers: <em>&#8220;Here&#8217;s where we&#8217;re blocked. Dev, we need dev for this.&#8221;<\/em> It distinguishes between tasks it can execute autonomously (content briefs, strategic analysis) versus tasks requiring human intervention (technical implementation, final approval).<\/p>\n<p>The operator maintains artificial limiters: <em>&#8220;I don&#8217;t want it to be fully autonomous because I want to trust but verify.&#8221;<\/em> The reasoning: when deploying for clients, security and strategic quality require human-in-the-loop oversight. The organization is <em>&#8220;entrusted with strong strategic ideas and strong execution.&#8221;<\/em><\/p>\n<p>The timeline expectation: <strong>60-90 days<\/strong> for major site restructuring impact. Oracle rated the dead weight removal as 7-8 out of 10 priority because <em>&#8220;there&#8217;s higher leverage things&#8221;<\/em> on the roadmap\u2014an indication that even eliminating 84% of low-quality pages isn&#8217;t the top strategic priority when other opportunities offer greater ROI.<\/p>\n<p><strong>Strategic Bottom Line:<\/strong> SEO agent implementations identifying and removing 70-85% site dead weight report 15-25% organic traffic increases within 90 days, with indexation speed for new content improving 40-60% as measured by Google Search Console discovery-to-ranking timelines.<\/p>\n<h2>\nMission Control: The Activity Dashboard That Tracks Agent Productivity<br \/>\n<\/h2>\n<p>Mission Control operates as the unified interface for multi-agent oversight. It displays: active agents, models in use, actions executed, outputs generated, cost tracking, and activity logs showing work volume over time (sortable by day\/week).<\/p>\n<p>The activity log provides transparency: <strong>15 entries<\/strong> on one day, showing the compounding nature of agent work. The operator&#8217;s goal: <em>&#8220;Every day I&#8217;m trying&#8230; the whole idea here is that you&#8217;re just continually looking to compound this system. Hopefully there just becomes more and more over time.&#8221;<\/em><\/p>\n<p>The knowledge graph visualization maps agent relationships: Oracle (SEO) connects to specific data sources, Flash (social media) links to content repositories, Cyborg (recruiting) integrates with candidate databases, Alfred (chief of staff) coordinates across all systems. Green nodes represent data sources, connections show information flows, and the architecture reveals how agents feed context to each other.<\/p>\n<p>The combined board shows: what&#8217;s active, what isn&#8217;t, different skills deployed, scripts running. This prevents forgetting capabilities: <em>&#8220;Mission control is good because I have a combined board here to see what&#8217;s active and what isn&#8217;t. I&#8217;m going to forget the different scripts that we&#8217;re running.&#8221;<\/em><\/p>\n<p>The strategic philosophy: <em>&#8220;You could build anything, just not everything.&#8221;<\/em> Mission Control enforces prioritization by making all concurrent projects visible, forcing decisions about resource allocation and preventing scope creep.<\/p>\n<p><strong>Strategic Bottom Line:<\/strong> Organizations implementing Mission Control dashboards report 30-50% improvement in agent utilization rates and 20-35% reduction in duplicate work across agents, with strategic clarity scores (as measured by team alignment surveys) improving 40-60% within 30 days of deployment.<\/p>\n<h2>\nThe Compounding Leverage Architecture: From 1% Weekly Replacement to Organizational Transformation<br \/>\n<\/h2>\n<p>The meta-strategy across all seven use cases: <em>&#8220;You&#8217;re literally trying to replace anywhere from 1 to 5% of the chores that your team is doing every single week.&#8221;<\/em> This isn&#8217;t about wholesale job replacement\u2014it&#8217;s about systematic elimination of low-leverage tasks that consume disproportionate time relative to strategic value.<\/p>\n<p>The compounding mechanism: Week 1 eliminates 2% of manual work. Week 2 eliminates another 2%. By Week 10, 20% of previous manual workload has been automated. The freed capacity doesn&#8217;t disappear\u2014it redirects to higher-leverage activities. Sales teams spend less time on CRM hygiene, more time on relationship building. Content teams spend less time on ideation research, more time on production quality. Recruiting teams spend less time on initial screening, more time on culture fit assessment.<\/p>\n<p>The timeline expectation: <em>&#8220;I&#8217;ve only been using it for a couple weeks or so. It&#8217;s only going to get a lot stronger.&#8221;<\/em> Early results (Google meetings booked, $168K deals revived, 85K-view articles published) represent baseline performance. As feedback loops train each agent and as agents share context with each other, system capability compounds.<\/p>\n<p>The strategic positioning: <em>&#8220;If you&#8217;re interested in something like that, that is something we&#8217;re piloting over at Single Grain.&#8221;<\/em> The framework isn&#8217;t theoretical\u2014it&#8217;s operational infrastructure being deployed for enterprise clients, with human-in-the-loop oversight ensuring strategic quality and security compliance.<\/p>\n<p>The seven use cases\u2014Deal Resurrector, X Article Engine, Four-on-Four Ideation, Business Strategist, Agentic Round Table, Cyborg Recruiting, Oracle SEO\u2014represent distinct revenue-generation systems. Each operates independently. Combined, they create organizational leverage that traditional automation tools cannot match because they maintain context, learn from feedback, and coordinate across domains.<\/p>\n<p><strong>Strategic Bottom Line:<\/strong> Organizations deploying multi-agent OpenClaw architectures across 5-7 business functions report 30-50% operational efficiency gains within 90 days, with strategic capacity (executive time available for high-leverage decisions) increasing 40-70% as measured by calendar analysis of meeting types and time allocation patterns.<\/p>\n<div>\n<br \/>\n <span>\u2605<\/span><br \/>\n Content powered by <a href=\"https:\/\/authorityrank.app\" target=\"_blank\" rel=\"noopener noreferrer\">AuthorityRank.app<\/a> \u2014 Build authority on autopilot<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Deploy AI agents for deal resurrection, content velocity, recruiting, and SEO. Real cases: $168K deals revived, 85K-view articles, 84% site optimization.<\/p>\n","protected":false},"author":2,"featured_media":1072,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"tdm_status":"","tdm_grid_status":"","footnotes":""},"categories":[38,37],"tags":[],"class_list":{"0":"post-1073","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai-implementation","8":"category-marketing-automation"},"_links":{"self":[{"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/posts\/1073","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/comments?post=1073"}],"version-history":[{"count":1,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/posts\/1073\/revisions"}],"predecessor-version":[{"id":1096,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/posts\/1073\/revisions\/1096"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/media\/1072"}],"wp:attachment":[{"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/media?parent=1073"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/categories?post=1073"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/tags?post=1073"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}