{"id":41654,"date":"2026-01-20T13:05:01","date_gmt":"2026-01-20T13:05:01","guid":{"rendered":"https:\/\/dynaas.com\/insights\/emergent-tool-use-when-ai-teaches-itself\/"},"modified":"2026-02-03T11:38:14","modified_gmt":"2026-02-03T11:38:14","slug":"emergent-tool-use-when-ai-teaches-itself","status":"publish","type":"insights","link":"https:\/\/dynaas.com\/it\/insights\/emergent-tool-use-when-ai-teaches-itself\/","title":{"rendered":"Uso emergente di strumenti: quando l&#8217;IA si auto-insegna\u00a0"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"41654\" class=\"elementor elementor-41654 elementor-41261\" data-elementor-post-type=\"insights\">\n\t\t\t\t<div class=\"elementor-element elementor-element-146a83c5 e-con-full e-flex e-con e-parent\" data-id=\"146a83c5\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t<div class=\"elementor-element elementor-element-3c28f7a4 e-con-full e-flex 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33.941z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Emergent Tool Use: When AI Teaches Itself<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a511460 elementor-widget elementor-widget-heading\" data-id=\"5a511460\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Emergent Tool Use: When AI Teaches Itself\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5c6c939d e-con-full e-flex e-con e-parent\" data-id=\"5c6c939d\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-478ce3cd e-con-full e-flex e-con e-child\" data-id=\"478ce3cd\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-49e28148 e-con-full e-flex e-con e-child\" data-id=\"49e28148\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-670aae7 elementor-widget elementor-widget-image\" data-id=\"670aae7\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"344\" height=\"1024\" src=\"https:\/\/dynaas.com\/wp-content\/uploads\/2026\/01\/ai-drawing-concept-visual-representation-scaled-344x1024.jpg\" class=\"attachment-large size-large wp-image-41489\" alt=\"\" srcset=\"https:\/\/dynaas.com\/wp-content\/uploads\/2026\/01\/ai-drawing-concept-visual-representation-scaled-344x1024.jpg 344w, https:\/\/dynaas.com\/wp-content\/uploads\/2026\/01\/ai-drawing-concept-visual-representation-scaled-101x300.jpg 101w, https:\/\/dynaas.com\/wp-content\/uploads\/2026\/01\/ai-drawing-concept-visual-representation-scaled-768x2286.jpg 768w, https:\/\/dynaas.com\/wp-content\/uploads\/2026\/01\/ai-drawing-concept-visual-representation-scaled-516x1536.jpg 516w, https:\/\/dynaas.com\/wp-content\/uploads\/2026\/01\/ai-drawing-concept-visual-representation-scaled-688x2048.jpg 688w, https:\/\/dynaas.com\/wp-content\/uploads\/2026\/01\/ai-drawing-concept-visual-representation-scaled.jpg 860w\" sizes=\"(max-width: 344px) 100vw, 344px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-342f9f82 e-con-full e-flex e-con e-child\" data-id=\"342f9f82\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-687f231d elementor-widget elementor-widget-text-editor\" data-id=\"687f231d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Imagine setting a simple game for a child and returning to find that they\u2019ve not only mastered it but have also invented entirely new ways to play. Now you see them using objects in the room you hadn\u2019t even considered part of the game. This is something that researchers are discovering in the world of\u00a0<a href=\"https:\/\/www.devoteam.com\/uk\/services\/ai-ml\/\">Artificial Intelligence<\/a>, particularly with a concept known as\u00a0<strong>\u201cemergent tool use.\u201d<\/strong>\u00a0It\u2019s a field that\u2019s pushing the boundaries of what we thought AI could learn on its own, and it holds profound implications for the future of intelligent systems.<\/p><p>We\u2019re familiar with AI that can predict trends, generate text, or even create images. But what happens when AI starts to exhibit unscripted, creative problem-solving, particularly by learning to use tools in its environment in ways it was never explicitly programmed to? A study by\u00a0<a href=\"https:\/\/openai.com\/index\/emergent-tool-use\/\">OpenAI on multi-agent hide-and-seek<\/a>\u00a0provides a captivating window into this phenomenon, revealing how AI agents, through interaction and competition, can develop sophisticated, tool-using strategies from the ground up. In this article, I explore the concept of<strong>\u00a0emergent tool use\u00a0<\/strong>and its significance in AI and Machine Learning. Join in for a (slightly unsettling) ride<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fca49f elementor-widget elementor-widget-heading\" data-id=\"3fca49f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What is Emergent Tool Use in AI?\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3b47fc4 elementor-widget elementor-widget-text-editor\" data-id=\"3b47fc4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>At its core,\u00a0emergent tool use refers to AI systems, typically\u00a0<a href=\"https:\/\/www.devoteam.com\/uk\/expert-view\/frequently-asked-questions-on-ai-agents\/\">AI agents<\/a>, developing the ability to utilise objects or functionalities in their environment as tools to achieve their goals. The systems are doing that\u00a0without being directly instructed on how to use these tools.\u00a0Instead,\u00a0these behaviours arise spontaneously or \u2019emerge\u2019 from the learning process, driven by the agent\u2019s objectives and its interactions within a given environment.<\/p><p>Imagine this scenario: you tell an AI its goal (e.g., \u201cstay hidden\u201d or \u201cfind the other agent\u201d), give it some basic abilities (e.g., \u201cmove,\u201d \u201cgrab\u201d), and place it in an environment with various objects. Through countless trials and errors, often in competition with other AI agents, it may eventually figure out that a box can be used for cover or a ramp can be used to scale a wall, demonstrating effective tool use that no human has explicitly coded. This is a significant step beyond simply following programmed instructions; it\u2019s about discovery and adaptation.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fffa1f8 elementor-widget elementor-widget-heading\" data-id=\"fffa1f8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The OpenAI Hide-and-Seek Experiment: A Masterclass in Emergence\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8a29ffa elementor-widget elementor-widget-text-editor\" data-id=\"8a29ffa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The\u00a0<a href=\"https:\/\/openai.com\/index\/emergent-tool-use\/\">OpenAI experiment<\/a>\u00a0beautifully illustrates this. Researchers created a simulated 3D environment where AI agents played a simple game of hide-and-seek. There were two teams: hiders and seekers. The environment contained various objects like boxes, ramps, and walls that agents could interact with.<\/p><p>Initially, the agents\u2019 behaviours were random and unsophisticated. However, through millions of rounds of gameplay, driven by reinforcement learning (where agents are rewarded for achieving their goals), fascinating strategies began to emerge:<\/p><ul class=\"wp-block-list\"><li><strong>Basic Hiding and Seeking:<\/strong>\u00a0Agents learned the fundamental mechanics of the game.<\/li><li><strong>Exploiting the Environment:\u00a0<\/strong>Hiders started using boxes to build shelters, barricading themselves in. Seekers, in turn, learned to move or use these boxes.<\/li><\/ul><ul class=\"wp-block-list\"><li><strong>Tool Use \u2013 Phase 1 (Ramps):\u00a0<\/strong>When hiders got too good at building shelters, seekers discovered they could use ramps to jump over walls and into the hiders\u2019 forts. This was a clear instance of\u00a0<strong>emergent tool use<\/strong>; no one told them a ramp could be used this way.<\/li><li><strong>Counter-Strategies with Tools:<\/strong>\u00a0Hiders adapted by learning to drag the ramps into their shelters and lock them away before the game started, preventing seekers from using them.<\/li><li><strong>Further Escalation:<\/strong>\u00a0At various points, agents learned to \u201csurf\u201d on top of boxes (by standing on a box and moving it) or even work collaboratively to overcome obstacles.<\/li><\/ul><p>Throughout this process, the agents developed a sort of \u201cbehavioural autocurriculum,\u201d where each new strategy by one team spurred the development of a counter-strategy by the other, leading to increasingly complex and intelligent tool use. OpenAI noted that six distinct strategies emerged, each a direct result of the multi-agent learning dynamics.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-408e8e6 elementor-widget elementor-widget-heading\" data-id=\"408e8e6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Why is This Discovery So Significant?\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7d3ffb0 elementor-widget elementor-widget-text-editor\" data-id=\"7d3ffb0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Les implications de l\u2019apprentissage par IA \u00e0 l\u2019utilisation des outils sont vastes :<\/p><ul class=\"wp-block-list\"><li><strong>R\u00e9solution de probl\u00e8mes novatrice :<\/strong> Elle d\u00e9montre que l\u2019IA peut trouver des solutions \u00e0 des probl\u00e8mes que les programmeurs humains n\u2019auraient peut-\u00eatre pas anticip\u00e9s. Cela ouvre la porte aux IA pour relever les d\u00e9fis de mani\u00e8re plus cr\u00e9ative et potentiellement plus efficace.<\/li><li><strong>Le pouvoir de l\u2019interaction :<\/strong> La configuration <a href=\"https:\/\/www.devoteam.com\/uk\/expert-view\/multi-agent-ai\/\">multi-agents<\/a> \u00e9tait cruciale. La concurrence et la coop\u00e9ration ont pouss\u00e9 les agents \u00e0 explorer plus en profondeur leur environnement et ses objets, acc\u00e9l\u00e9rant ainsi le processus d\u2019apprentissage. Cela a des implications pour la conception de syst\u00e8mes d\u2019IA capables d\u2019apprendre et de s\u2019adapter dans des environnements complexes et dynamiques, un aspect cl\u00e9 de la \u00ab <a href=\"https:\/\/www.devoteam.com\/uk\/expert-view\/ai-webinar-making-sense-of-ai-in-2025\/\">Mont\u00e9e de l\u2019IA Agentique<\/a> \u00bb<\/li><li><strong>Vers une IA plus g\u00e9n\u00e9rale : <\/strong>Bien que ces agents aient \u00e9t\u00e9 limit\u00e9s \u00e0 un jeu sp\u00e9cifique, les principes fondamentaux d\u2019apprentissage par l\u2019interaction et d\u2019atteinte d\u2019objectifs via l\u2019utilisation des outils sont des \u00e9tapes vers une intelligence artificielle plus g\u00e9n\u00e9rale pouvant op\u00e9rer sur un \u00e9ventail plus large de t\u00e2ches et d\u2019environnements.<\/li><li><strong>Comprendre les syst\u00e8mes complexes :<\/strong> Observer l\u2019\u00e9mergence de ces strat\u00e9gies \u00e0 partir de r\u00e8gles simples nous donne un aper\u00e7u de la fa\u00e7on dont la complexit\u00e9 peut surgir dans les syst\u00e8mes intelligents, et m\u00eame dans l\u2019\u00e9volution naturelle.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-728ac36 elementor-widget elementor-widget-heading\" data-id=\"728ac36\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Challenges and the Future\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1718ceb elementor-widget elementor-widget-text-editor\" data-id=\"1718ceb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p _msttexthash=\"23972611\" _msthash=\"12732\">The emergence of tool use in AI is undoubtedly exciting, but it also brings forth important considerations and challenges. Here are some potential issues that come to mind when thinking about emergent tool use in AI<\/p>\n<ul class=\"wp-block-list\" _msthidden=\"1\">\n<li _msttexthash=\"150498920\" _msthash=\"12727\"><strong _istranslated=\"1\">Impr\u00e9visibilit\u00e9 et contr\u00f4le : <\/strong>Les comportements \u00e9mergents ne sont, par nature, pas explicitement programm\u00e9s. Cela signifie qu\u2019ils peuvent parfois \u00eatre impr\u00e9visibles. S\u2019assurer que les syst\u00e8mes d\u2019IA restent align\u00e9s sur les intentions humaines et op\u00e8rent dans des limites s\u00fbres devient encore plus crucial \u00e0 mesure qu\u2019ils d\u00e9veloppent des capacit\u00e9s plus autonomes.<\/li>\n<li _msttexthash=\"92591369\" _msthash=\"12728\"><strong _istranslated=\"1\">\u00c9volutivit\u00e9 vers la complexit\u00e9 r\u00e9elle :<\/strong> Les outils de l\u2019exp\u00e9rience OpenAI \u00e9taient relativement simples. Adapter ces r\u00e9sultats \u00e0 des agents IA capables d\u2019utiliser efficacement et en toute s\u00e9curit\u00e9 des outils complexes du monde r\u00e9el (applications logicielles, interfaces de machines physiques, syst\u00e8mes financiers) est un bond en avant et un domaine de recherche actif.<\/li>\n<li _msttexthash=\"171068066\" _msthash=\"12729\"><strong _istranslated=\"1\">Le probl\u00e8me du \u00ab piratage par r\u00e9compense \u00bb :<\/strong> Les agents IA sont optimis\u00e9s pour atteindre leur signal de r\u00e9compense. Parfois, ils peuvent trouver des moyens involontaires voire ind\u00e9sirables de maximiser cette r\u00e9compense. Cela pourrait ne pas correspondre au r\u00e9sultat r\u00e9el souhait\u00e9. L\u2019\u00e9tude OpenAI elle-m\u00eame a not\u00e9 des cas o\u00f9 des agents ont trouv\u00e9 des failles ou adopt\u00e9 des comportements techniquement r\u00e9ussis mais pas dans l\u2019esprit de la t\u00e2che.<\/li>\n<li _msttexthash=\"324011064\" _msthash=\"12730\"><strong _istranslated=\"1\">S\u00e9curit\u00e9 et implications soci\u00e9tales :<\/strong> Comme l\u2019a reconnu OpenAI, les agents utilisant des outils pourraient avoir des implications soci\u00e9tales impr\u00e9vues. Si une IA peut apprendre \u00e0 utiliser un outil \u00e0 des fins b\u00e9n\u00e9fiques, il existe \u00e9galement un risque d\u2019abus si les objectifs sont mal d\u00e9finis ou si l\u2019IA est compromise. Cela s\u2019inscrit dans des pr\u00e9occupations plus larges concernant les menaces de l\u2019IA agentique, telles que l\u2019utilisation abusive des outils et la rupture d\u2019intention.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4913787 elementor-widget elementor-widget-heading\" data-id=\"4913787\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What Does Emergent Tool Use in AI mean for businesses?\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f539fb elementor-widget elementor-widget-text-editor\" data-id=\"3f539fb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The insights from experiments like OpenAI\u2019s hide-and-seek are invaluable as we design the next generation of AI agents. The key is to create environments and incentive structures that guide AI towards discovering useful and safe tool-using behaviours.<\/p><p><strong>For businesses, this research underscores the growing potential of AI to go beyond data analysis and content generation to become active participants in operational workflows.\u00a0<\/strong>As AI agents become more adept at interacting with their digital environments and using tools, we can expect to see them applied to:<\/p><ul class=\"wp-block-list\"><li>Automating complex IT processes.<\/li><li>Managing intricate logistics and supply chains.<\/li><li>Conducting sophisticated scientific research by interacting with lab equipment or data sources.<\/li><li>Providing highly adaptive and interactive customer support.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ac28aa elementor-widget elementor-widget-heading\" data-id=\"6ac28aa\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Conclusion\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4daebda elementor-widget elementor-widget-text-editor\" data-id=\"4daebda\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The OpenAI hide-and-seek experiment and the\u00a0<strong>emergent tool use<\/strong>\u00a0it revealed are more than just a fascinating academic exercise. They offer a glimpse into a future where AI systems learn, adapt, and discover in ways that can significantly augment human capabilities.<\/p><p>While we are still in the relatively early stages of understanding and utilising these emergent properties, the trajectory is clear. AI is becoming increasingly capable of sophisticated, autonomous action. For organisations looking to stay at the forefront of technological innovation, understanding these developments is key. From my perspective, the ability of AI to independently discover how to use tools is a genuine game-changer. It\u2019s like giving an apprentice a workshop and seeing them figure out how to use the lathe and the chisel, not just competently, but innovatively. The real challenge is to ensure that my \u201capprentice\u201d uses the tools to create something valuable and not to cause any harm. Are we ready to be the bosses of AI agents?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-df05808 e-flex e-con-boxed e-con e-parent\" data-id=\"df05808\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Dynaas Insights Emergent Tool Use: When AI Teaches Itself Emergent Tool Use: When AI Teaches Itself Imagine setting a simple game for a child and returning to find that they\u2019ve not only mastered it but have also invented entirely new ways to play. Now you see them using objects in the room you hadn\u2019t even [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":41656,"menu_order":0,"template":"elementor_header_footer","meta":{"_acf_changed":false},"industries":[259],"resource_types":[283],"topics":[261],"class_list":["post-41654","insights","type-insights","status-publish","has-post-thumbnail","hentry","industries-tech","resource_types-expert-view","topics-ai-business-solutions"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/dynaas.com\/it\/wp-json\/wp\/v2\/insights\/41654","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dynaas.com\/it\/wp-json\/wp\/v2\/insights"}],"about":[{"href":"https:\/\/dynaas.com\/it\/wp-json\/wp\/v2\/types\/insights"}],"author":[{"embeddable":true,"href":"https:\/\/dynaas.com\/it\/wp-json\/wp\/v2\/users\/3"}],"version-history":[{"count":26,"href":"https:\/\/dynaas.com\/it\/wp-json\/wp\/v2\/insights\/41654\/revisions"}],"predecessor-version":[{"id":43152,"href":"https:\/\/dynaas.com\/it\/wp-json\/wp\/v2\/insights\/41654\/revisions\/43152"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dynaas.com\/it\/wp-json\/wp\/v2\/media\/41656"}],"wp:attachment":[{"href":"https:\/\/dynaas.com\/it\/wp-json\/wp\/v2\/media?parent=41654"}],"wp:term":[{"taxonomy":"industries","embeddable":true,"href":"https:\/\/dynaas.com\/it\/wp-json\/wp\/v2\/industries?post=41654"},{"taxonomy":"resource_types","embeddable":true,"href":"https:\/\/dynaas.com\/it\/wp-json\/wp\/v2\/resource_types?post=41654"},{"taxonomy":"topics","embeddable":true,"href":"https:\/\/dynaas.com\/it\/wp-json\/wp\/v2\/topics?post=41654"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}