{"id":8016,"date":"2026-04-08T08:59:54","date_gmt":"2026-04-08T06:59:54","guid":{"rendered":"https:\/\/www.rawk.at\/?p=6172"},"modified":"2026-05-31T07:50:48","modified_gmt":"2026-05-31T07:50:48","slug":"soma-somatic-operating-model-architecture","status":"publish","type":"post","link":"https:\/\/new.rawk.at\/?p=8016","title":{"rendered":"SOMA &#8211; Somatic Operating Model Architecture"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"8016\" class=\"elementor elementor-8016\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-793a568 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"793a568\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3d56cdf\" data-id=\"3d56cdf\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5476c1d elementor-widget elementor-widget-text-editor\" data-id=\"5476c1d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong>Type:<\/strong> Software\u00a0 <br \/><strong>Status:<\/strong> POC<\/p><p><strong>Tech Stack: <\/strong>Python, FastAPI, Uvicorn, psutil, httpx, Pydantic, Ollama, InfluxDB 2.7, React 18, Recharts, Tailwind CSS, Vite, Docker<\/p><p><strong>Problem Statement<\/strong><\/p><p>Current AI systems operate completely detached from their physical infrastructure. Even when a server is under extreme load, overheating, or running low on memory, the AI responds identically as when idle. There is no feedback channel between hardware state and AI behavior \u2014 no equivalent to fatigue, stress, or exhaustion. This leads to suboptimal resource utilization and prevents emergent self-regulation.<\/p><p><strong>Description<\/strong><\/p><p>An experimental Embodied AI framework that creates a feedback loop between hardware metrics, a simulated hormonal system, and LLM behavior modulation. Six simulated hormones (Cortisol, Dopamine, Serotonin, Adrenaline, Melatonin, Oxytocin) are calculated from CPU, RAM, temperature, and network telemetry and dynamically modulate LLM inference parameters such as Temperature, Top-P, and token limits. Detects burnout states, flow, and recovery as emergent behavior. Fully configurable via YAML.<\/p><p><strong>Use Case<\/strong><\/p><p><em>What if an AI could sense stress and exhaustion and adapt its behavior accordingly?<\/em><\/p><p><strong>Link: <\/strong><a href=\"https:\/\/github.com\/rawk7000\/Soma\" target=\"_blank\" rel=\"noopener\">https:\/\/github.com\/rawk7000\/Soma<\/a> (private repo)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b9da404 elementor-widget elementor-widget-gallery\" data-id=\"b9da404\" data-element_type=\"widget\" data-e-type=\"widget\" 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httpx, Pydantic, Ollama, InfluxDB 2.7, React 18, Recharts, Tailwind CSS, Vite, Docker Problem Statement Current AI systems operate completely detached from their physical infrastructure. Even when a server is under extreme load, overheating, or running low on memory, the AI responds identically as when idle. [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":8393,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-8016","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-projects"],"_links":{"self":[{"href":"https:\/\/new.rawk.at\/index.php?rest_route=\/wp\/v2\/posts\/8016","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/new.rawk.at\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/new.rawk.at\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/new.rawk.at\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/new.rawk.at\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=8016"}],"version-history":[{"count":4,"href":"https:\/\/new.rawk.at\/index.php?rest_route=\/wp\/v2\/posts\/8016\/revisions"}],"predecessor-version":[{"id":8413,"href":"https:\/\/new.rawk.at\/index.php?rest_route=\/wp\/v2\/posts\/8016\/revisions\/8413"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/new.rawk.at\/index.php?rest_route=\/wp\/v2\/media\/8393"}],"wp:attachment":[{"href":"https:\/\/new.rawk.at\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8016"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/new.rawk.at\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8016"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/new.rawk.at\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8016"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}