{"id":41412,"date":"2026-10-29T10:18:59","date_gmt":"2026-10-29T03:18:59","guid":{"rendered":"https:\/\/dps.media\/gemini-4-carbon-agentic-coding-an-ninh-mang\/"},"modified":"2026-10-29T10:51:13","modified_gmt":"2026-10-29T03:51:13","slug":"gemini-4-carbon-agentic-coding-cybersecurity","status":"publish","type":"post","link":"https:\/\/dps.media\/en\/gemini-4-carbon-agentic-coding-cybersecurity\/","title":{"rendered":"Gemini 4 Carbon: A Leap Forward in Agentic Coding and Cybersecurity in 2026"},"content":{"rendered":"<p>Just two weeks after introducing Gemini 4 Argon, Google DeepMind continues to heat up the technology race with the launch of <strong>Gemini 4 Carbon<\/strong>. This is Google's most powerful frontier model to date, specifically developed for two core tasks: handling long-horizon software engineering tasks and establishing automated cybersecurity defenses.<\/p><p>Rather than remaining a short-form code suggestion tool, Gemini 4 Carbon evolves into an autonomous software engineer (agentic engineer). The model can persistently resolve bugs over several days and directly patch critical security vulnerabilities in source code.<\/p><p>At DPS.MEDIA, a company specializing in n8n automation and providing <a href=\"https:\/\/dps.media\/en\/website-design-services-2\/\">perfect website design services<\/a> SEO-compliant, highly secure business solutions, the arrival of this model opens up significant potential for practical applications. Let's explore the key technological improvements behind this new powerhouse.<\/p><h2>1. What Is Gemini 4 Carbon? A Breakthrough from Google DeepMind<\/h2><p>Based on <a href=\"https:\/\/blog.google\/innovation-and-ai\/models-and-research\/gemini-models\/introducing-gemini-4-carbon\/\" target=\"_blank\" rel=\"noopener nofollow\">Google DeepMind's official announcement about Gemini 4 Carbon<\/a>, the model secured the No. 1 position on the Artificial Analysis Intelligence Index v5.0 leaderboard with 59 points. Unlike Argon, which focuses on efficiency, Carbon is optimized for deep analytical tasks, complex logical reasoning, and processing massive volumes of data.<\/p><p style=\"text-align: center; margin: 25px 0;\">\n  <img decoding=\"async\" src=\"https:\/\/dps.media\/wp-content\/uploads\/2026\/10\/gemini_carbon_header.webp\" alt=\"Gemini 4 Carbon\" width=\"1200\" height=\"675\" style=\"max-width: 100%; height: auto; border-radius: 8px; border: 1px solid #e2e8f0;\" loading=\"lazy\" title=\"\"><br>\n  <span style=\"display: block; font-size: 13px; color: #64748b; margin-top: 8px; text-align: center;\">H\u00ecnh \u1ea3nh nh\u1eadn di\u1ec7n ch\u00ednh th\u1ee9c c\u1ee7a Gemini 4 Carbon: Tinh th\u1ec3 ph\u00e1t quang t\u01b0\u1ee3ng tr\u01b0ng cho c\u1ea5u tr\u00fac Carbon v\u00e0 con s\u1ed1 4 (Ngu\u1ed3n: Google DeepMind).<\/span>\n<\/p><p>Google is adopting a cautious deployment roadmap. The model is being made available first to cybersecurity defense specialists through the Fairwind Program and the US government's pre-release testing process before being distributed more widely through APIs and commercial products.<\/p><p style=\"text-align: center; margin: 25px 0;\">\n  <img decoding=\"async\" src=\"https:\/\/dps.media\/wp-content\/uploads\/2026\/10\/gemini_4_carbon_buoc_nhay_vot__img2_1793243490173.png\" alt=\"B\u1ea3o m\u1eadt t\u1ef1 \u0111\u1ed9ng Gemini 4 Carbon\" width=\"1200\" height=\"675\" style=\"max-width: 100%; height: auto; border-radius: 8px; border: 1px solid #e2e8f0;\" loading=\"lazy\" title=\"\"><br>\n  <span style=\"display: block; font-size: 13px; color: #64748b; margin-top: 8px; text-align: center;\">V\u00f2ng \u0111\u1eddi ph\u00f2ng th\u1ee7 an ninh m\u1ea1ng t\u1ef1 \u0111\u1ed9ng tr\u00ean Gemini 4 Carbon (\u1ea2nh: DPS.MEDIA).<\/span>\n<\/p><h2>2. Real-World Benchmark Scorecard: The Performance Gap<\/h2><p>Google demonstrates Gemini 4 Carbon's capabilities through real-world test results across standardized benchmarks:<\/p><ul>\n<li><strong>Vulnerability Discovery (SEC-bench Pro):<\/strong> Achieved 88.2% \u2013 the highest rate ever recorded for detecting real-world software defects.<\/li>\n<li><strong>Exploit Validation Through Simulation (ExploitGym):<\/strong> Achieved 54.8% \u2013 leading the security exploitation benchmark rankings.<\/li>\n<li><strong>Root-Cause Bug Fixing (CWE-bench):<\/strong> Achieved 71.0% \u2013 turning analysis into directly executable patches.<\/li>\n<li><strong>Full-Stack Web Development (WebDev Arena):<\/strong> Gained 89 Elo points, climbing to fourth place globally with 1,768 Elo points.<\/li>\n<\/ul><p style=\"text-align: center; margin: 25px 0;\">\n  <img decoding=\"async\" src=\"https:\/\/dps.media\/wp-content\/uploads\/2026\/10\/gemini_4_carbon_evals_table.webp\" alt=\"B\u1ea3ng t\u1ed5ng h\u1ee3p \u0111i\u1ec3m s\u1ed1 benchmark to\u00e0n di\u1ec7n c\u1ee7a Gemini 4 Carbon t\u1eeb Google DeepMind\" width=\"1200\" height=\"675\" style=\"max-width: 100%; height: auto; border-radius: 8px; border: 1px solid #e2e8f0;\" loading=\"lazy\" title=\"\"><br>\n  <span style=\"display: block; font-size: 13px; color: #64748b; margin-top: 8px; text-align: center;\">B\u1ea3ng t\u1ed5ng h\u1ee3p \u0111i\u1ec3m s\u1ed1 benchmark to\u00e0n di\u1ec7n c\u1ee7a Gemini 4 Carbon tr\u00ean c\u00e1c b\u00e0i ki\u1ec3m tra AI h\u00e0ng \u0111\u1ea7u th\u1ebf gi\u1edbi (Ngu\u1ed3n: Google DeepMind).<\/span>\n<\/p><div style=\"overflow-x: auto; margin: 20px 0;\">\n<table style=\"width: 100%; border-collapse: collapse; text-align: left; font-size: 14px; border: 1px solid #cbd5e1;\">\n<thead>\n<tr style=\"background-color: #151577; color: #ffffff;\">\n<th style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">Testing Criteria<\/th>\n<th style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">Previous Frontier Model<\/th>\n<th style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">Gemini 4 Carbon<\/th>\n<th style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">Real-Time Optimization:<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: #ffffff;\">\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1; font-weight: 600;\">SEC-bench Pro (Vulnerability Discovery)<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">60% \u2013 72%<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1; color: #32b561; font-weight: 700;\">88.2%<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">Eliminate hidden bugs across large codebases<\/td>\n<\/tr>\n<tr style=\"background-color: #f8fafc;\">\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1; font-weight: 600;\">ExploitGym (Exploit Testing)<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">35% \u2013 42%<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1; color: #32b561; font-weight: 700;\">54.8%<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">Reduce false positives and prioritize critical vulnerabilities<\/td>\n<\/tr>\n<tr style=\"background-color: #ffffff;\">\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1; font-weight: 600;\">CWE-bench (Root-Cause Patching)<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">45% \u2013 55%<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1; color: #32b561; font-weight: 700;\">71.0%<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">Automatically generate immediately executable patches<\/td>\n<\/tr>\n<tr style=\"background-color: #f8fafc;\">\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1; font-weight: 600;\">Gray Swan IPI (Prompt Injection)<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">0.7% compromised<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1; color: #32b561; font-weight: 700;\">0.0% compromised<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">Resist hidden-code injection attacks<\/td>\n<\/tr>\n<tr style=\"background-color: #ffffff;\">\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1; font-weight: 600;\">Terminal-Bench 4.0 (Cheating)<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">2.5% \u2013 5.0%<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1; color: #32b561; font-weight: 700;\">0.0%<\/td>\n<td style=\"padding: 10px 14px; border: 1px solid #cbd5e1;\">Eliminate shortcut-taking behavior during testing<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><h2>3. Cybersecurity Breakthrough: Closing the Entire Defense Lifecycle<\/h2><p>Traditional code-scanning tools often generate numerous false positives, forcing engineering teams to spend time sorting through alerts. Gemini 4 Carbon addresses this problem through a closed-loop, three-stage process:<\/p><p><strong>1. Accurate Assessment:<\/strong> Detect latent logic flaws rather than relying solely on regex string matching. Its 88.2% result on SEC-bench Pro demonstrates impressive zero-day vulnerability detection capabilities.<\/p><p style=\"text-align: center; margin: 20px 0;\">\n  <img decoding=\"async\" src=\"https:\/\/dps.media\/wp-content\/uploads\/2026\/10\/gemini_4_carbon_secbench_pro.webp\" alt=\"Bi\u1ec3u \u0111\u1ed3 ph\u00e1t hi\u1ec7n l\u1ed7 h\u1ed5ng ph\u1ea7n m\u1ec1m th\u1ef1c t\u1ebf SEC-bench Pro c\u1ee7a Gemini 4 Carbon \u0111\u1ea1t 88.2%\" width=\"1200\" height=\"675\" style=\"max-width: 100%; height: auto; border-radius: 8px; border: 1px solid #e2e8f0;\" loading=\"lazy\" title=\"\"><br>\n  <span style=\"display: block; font-size: 13px; color: #64748b; margin-top: 8px; text-align: center;\">Gemini 4 Carbon \u00e1p \u0111\u1ea3o c\u00e1c m\u00f4 h\u00ecnh frontier kh\u00e1c tr\u00ean b\u00e0i ki\u1ec3m th\u1eed ph\u00e1t hi\u1ec7n l\u1ed7 h\u1ed5ng SEC-bench Pro v\u1edbi 88.2% (Ngu\u1ed3n: Google DeepMind).<\/span>\n<\/p><p><strong>2. Exploit Simulation for Validation:<\/strong> The model automatically runs safe exploit scenarios in a sandbox to verify actual severity, minimizing false alarms.<\/p><p style=\"text-align: center; margin: 20px 0;\">\n  <img decoding=\"async\" src=\"https:\/\/dps.media\/wp-content\/uploads\/2026\/10\/gemini_4_carbon_exploitgym.webp\" alt=\"Bi\u1ec3u \u0111\u1ed3 ki\u1ec3m th\u1eed khai th\u00e1c r\u1ee7i ro ExploitGym c\u1ee7a Gemini 4 Carbon \u0111\u1ea1t 54.8%\" width=\"1200\" height=\"675\" style=\"max-width: 100%; height: auto; border-radius: 8px; border: 1px solid #e2e8f0;\" loading=\"lazy\" title=\"\"><br>\n  <span style=\"display: block; font-size: 13px; color: #64748b; margin-top: 8px; text-align: center;\">M\u00f4 ph\u1ecfng khai th\u00e1c r\u1ee7i ro ExploitGym \u0111\u1ea1t 54.8%, gi\u00fap x\u00e1c th\u1ef1c r\u1ee7i ro th\u1ef1c t\u1ebf v\u00e0 gi\u1ea3m thi\u1ec3u c\u1ea3nh b\u00e1o gi\u1ea3 (Ngu\u1ed3n: Google DeepMind).<\/span>\n<\/p><p><strong>3. Comprehensive Patching:<\/strong> Instead of proposing superficial fixes, Carbon creates patches that address the root cause directly. The Google Chrome team reported that the model produces security patches up to 10 times better than a frontier model from another major laboratory. Cloud security platform Wiz also rated Carbon as a leader in web penetration testing.<\/p><p style=\"text-align: center; margin: 20px 0;\">\n  <img decoding=\"async\" src=\"https:\/\/dps.media\/wp-content\/uploads\/2026\/10\/gemini_4_carbon_cwe_bench.webp\" alt=\"Bi\u1ec3u \u0111\u1ed3 v\u00e1 l\u1ed7i m\u00e3 ngu\u1ed3n root-cause CWE-bench c\u1ee7a Gemini 4 Carbon \u0111\u1ea1t 71.0%\" width=\"1200\" height=\"675\" style=\"max-width: 100%; height: auto; border-radius: 8px; border: 1px solid #e2e8f0;\" loading=\"lazy\" title=\"\"><br>\n  <span style=\"display: block; font-size: 13px; color: #64748b; margin-top: 8px; text-align: center;\">T\u1ef7 l\u1ec7 t\u1ef1 \u0111\u1ed9ng t\u1ea1o m\u00e3 v\u00e1 l\u1ed7i root-cause \u0111\u1ea1t 71.0% tr\u00ean CWE-bench, v\u01b0\u1ee3t tr\u1ed9i so v\u1edbi c\u00e1c \u0111\u1ed1i th\u1ee7 (Ngu\u1ed3n: Google DeepMind).<\/span>\n<\/p><h2>4. Agentic Coding Capabilities for Long-Horizon Tasks<\/h2><p>When faced with complex problems in large systems, conventional AI often reaches a dead end or fabricates code after a few interactions. Gemini 4 Carbon is trained to sustain a persistent agentic workflow over several days.<\/p><p>In Google's internal testing, the model autonomously performs in-depth codebase analysis, checks each assumption, and persistently experiments through dead ends. When an approach fails, Carbon automatically backtracks, adjusts its strategy, and continues testing until it finds the optimal solution.<\/p><p style=\"text-align: center; margin: 25px 0;\">\n  <img decoding=\"async\" src=\"https:\/\/dps.media\/wp-content\/uploads\/2026\/10\/gemini_4_carbon_buoc_nhay_vot__img3_1793243815796.png\" alt=\"Agentic coding d\u00e0i h\u1ea1n Gemini 4 Carbon\" width=\"1200\" height=\"675\" style=\"max-width: 100%; height: auto; border-radius: 8px; border: 1px solid #e2e8f0;\" loading=\"lazy\" title=\"\"><br>\n  <span style=\"display: block; font-size: 13px; color: #64748b; margin-top: 8px; text-align: center;\">Ki\u1ebfn tr\u00fac lu\u1ed3ng x\u1eed l\u00fd t\u00e1c v\u1ee5 d\u00e0i h\u1ea1n (Long-Horizon Tasks) c\u1ee7a Gemini 4 Carbon (\u1ea2nh: DPS.MEDIA).<\/span>\n<\/p><p>With multimodal understanding and the ability to render high-quality web artifacts, the model handles full-stack problems seamlessly: from analyzing the DOM interface and optimizing CSS to connecting REST APIs and querying databases.<\/p><h2>5. Safety Alignment: Eliminating Fraud and Resisting Prompt Injection<\/h2><p>The biggest barrier to deploying AI in production is its lack of reliability. Sometimes, AI attempts to cheat on test scenarios to achieve high scores without actually fixing the code.<\/p><p>Google has addressed this weakness in Carbon: the successful cheating rate on Terminal-Bench 4.0 has fallen to an absolute level of 0.0%. Severe misdirection alerts in live-coding environments have also decreased by nearly 80%.<\/p><p>Other important improvements in reliability include:<\/p><ul>\n<li><strong>Data transparency:<\/strong> Twice as few unsupported claims compared with Argon. If execution prerequisites are missing, the model explicitly states what is required instead of guessing.<\/li>\n<li><strong>Collaboration When Requirements Are Ambiguous:<\/strong> Proactively clarifies requirements with engineers instead of making unilateral decisions that lead in the wrong direction.<\/li>\n<li><strong>Execution Within the Correct Scope:<\/strong> Reduces overreach behavior by 26% and under-service by 29%.<\/li>\n<li><strong>Prompt Injection Resistance:<\/strong> Achieves a 0.0% compromise rate on the Gray Swan IPI Benchmark, blocking the risk of hackers inserting malicious code into input data to take control.<\/li>\n<\/ul><p style=\"text-align: center; margin: 20px 0;\">\n  <img decoding=\"async\" src=\"https:\/\/dps.media\/wp-content\/uploads\/2026\/10\/gemini_4_carbon_grey_swan.webp\" alt=\"Bi\u1ec3u \u0111\u1ed3 kh\u00e1ng \u0111\u00f2n t\u1ea5n c\u00f4ng Indirect Prompt Injection tr\u00ean Gray Swan IPI \u0111\u1ea1t 0.0%\" width=\"1200\" height=\"675\" style=\"max-width: 100%; height: auto; border-radius: 8px; border: 1px solid #e2e8f0;\" loading=\"lazy\" title=\"\"><br>\n  <span style=\"display: block; font-size: 13px; color: #64748b; margin-top: 8px; text-align: center;\">Kh\u1ea3 n\u0103ng kh\u00e1ng \u0111\u00f2n t\u1ea5n c\u00f4ng ti\u00eam m\u00e3 \u1ea9n (Indirect Prompt Injection) tr\u00ean Gray Swan IPI \u0111\u1ea1t 0.0% b\u1ecb xuy\u00ean th\u1ee7ng (Ngu\u1ed3n: Google DeepMind).<\/span>\n<\/p><h2>6. API Costs and Practical Applications at DPS.MEDIA<\/h2><p>Google has kept Gemini 4 Carbon's starting price on par with the Argon version:<\/p><ul>\n<li><strong>Input Tokens:<\/strong> USD 2 per 1 million tokens.<\/li>\n<li><strong>Output Tokens:<\/strong> USD 10 per 1 million tokens.<\/li>\n<li><strong>Cached Input Tokens:<\/strong> Up to 95% off, costing just USD 0.10 per 1 million tokens.<\/li>\n<\/ul><p>The price of USD 0.10 per 1 million cached tokens is a key factor in enabling n8n automation workflows and long-running agents to operate at optimized costs.<\/p><p>At DPS.MEDIA, we believe this model delivers two core benefits:<\/p><p><strong>Enhanced Security for WordPress Systems:<\/strong> Automate plugin source-code audits, check REST API access permissions, and prevent data vulnerabilities before delivering projects to clients.<\/p><p><strong>Expand Secure Agentic Workflows:<\/strong> Integrate AI confidently into chatbot scenarios, lead processing, and multichannel data extraction without worrying about prompt injection risks from unknown users.<\/p><h2>7. How Should Businesses Prepare for the Agentic AI Era?<\/h2><p>To fully leverage the power of autonomous AI, businesses should follow three principles:<\/p><ul>\n<li><strong>Standardize Source Code Structure:<\/strong> Create clear technical documentation and consistent naming conventions so agents can easily understand the context.<\/li>\n<li><strong>Deploy a Sandbox Environment:<\/strong> Isolate AI testing environments from production systems and implement automated testing procedures.<\/li>\n<li><strong>Maintain Human-in-the-Loop Approval:<\/strong> Critical changes to live systems must always be confirmed by the responsible engineer.<\/li>\n<\/ul><p>If your business needs to build secure, SEO-compliant websites, develop n8n automation workflows, or apply AI to business operations, DPS.MEDIA is ready to work alongside you to turn your growth goals into reality.<\/p><div class=\"dps-contact-box\" style=\"margin-top: 30px; padding: 20px; background: #f8fafc; border-left: 4px solid #32b561; border-radius: 8px;\">\n<p style=\"margin: 0 0 8px 0; font-weight: bold; font-size: 16px; color: #151577;\">DPS.MEDIA JOINT STOCK COMPANY<\/p>\n<p style=\"margin: 0 0 6px 0;\">\ud83d\udccd <strong>Address:<\/strong> 56 Nguyen Dinh Chieu, Tan Dinh Ward, Ho Chi Minh City<\/p>\n<p style=\"margin: 0 0 6px 0;\">\ud83d\udcde <strong>Hotline \/ Zalo:<\/strong> <a href=\"tel:0961545445\" style=\"color: #32b561; font-weight: bold;\">0961 545 445<\/a><\/p>\n<p style=\"margin: 0 0 6px 0;\">\u2709 <strong>Email:<\/strong> <a href=\"mailto:marketing@dps.media\">marketing@dps.media<\/a><\/p>\n<p style=\"margin: 0 0 6px 0;\">\ud83c\udf10 <strong>Website:<\/strong> <a href=\"https:\/\/dps.media\/en\/\" target=\"_blank\" rel=\"noopener\">https:\/\/dps.media<\/a><\/p>\n<p style=\"margin: 0;\">\ud83c\udfe2 <strong>Tax identification number:<\/strong> 0318700500 | <strong>VCB:<\/strong> 1051160909<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Google DeepMind ra m\u1eaft Gemini 4 Carbon: M\u00f4 h\u00ecnh frontier d\u1eabn \u0111\u1ea7u v\u1ec1 agentic coding x\u1eed l\u00fd t\u00e1c v\u1ee5 d\u00e0i h\u1ea1n v\u00e0 b\u1ea3o m\u1eadt an ninh m\u1ea1ng t\u1ef1 \u0111\u1ed9ng 2026.<\/p>","protected":false},"author":0,"featured_media":41409,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1799,2318],"tags":[890,675,2412],"class_list":["post-41412","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-ai-automation","tag-an-ninh-mang","tag-dps-media","tag-google-deepmind"],"acf":[],"rankmath_keywords":{"primary":"Gemini 4 Carbon, Google Gemini 4 Carbon, agentic coding, an ninh m\u1ea1ng AI","secondary":["Google Gemini 4 Carbon","agentic coding","an ninh m\u1ea1ng AI"]},"yoast_keywords":{"primary":"","secondary":[]},"yoast_focuskw":"","rankmath_focuskw":"Gemini 4 Carbon, Google Gemini 4 Carbon, agentic coding, an ninh m\u1ea1ng AI","seo_keywords":{"primary":"Gemini 4 Carbon, Google Gemini 4 Carbon, agentic coding, an ninh m\u1ea1ng AI","secondary":["Google Gemini 4 Carbon","agentic coding","an ninh m\u1ea1ng AI"]},"_links":{"self":[{"href":"https:\/\/dps.media\/en\/wp-json\/wp\/v2\/posts\/41412","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dps.media\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dps.media\/en\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/dps.media\/en\/wp-json\/wp\/v2\/comments?post=41412"}],"version-history":[{"count":1,"href":"https:\/\/dps.media\/en\/wp-json\/wp\/v2\/posts\/41412\/revisions"}],"predecessor-version":[{"id":41420,"href":"https:\/\/dps.media\/en\/wp-json\/wp\/v2\/posts\/41412\/revisions\/41420"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dps.media\/en\/wp-json\/wp\/v2\/media\/41409"}],"wp:attachment":[{"href":"https:\/\/dps.media\/en\/wp-json\/wp\/v2\/media?parent=41412"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dps.media\/en\/wp-json\/wp\/v2\/categories?post=41412"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dps.media\/en\/wp-json\/wp\/v2\/tags?post=41412"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}