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🔙 Back to V1 (Exhaustive)
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The 2026 Vision
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Generative AI and Large Language Models
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Claude Code
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LLMs AI Agents MLOps
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Firebase Genkit Gemini AI Agents
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|
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Android Gemini AI
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Fundamentals
|
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|
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<a href="../../chaos-engineering/" class="md-nav__link">
|
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<span class="md-ellipsis">
|
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|
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Platform & Site Reliability
|
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</span>
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|
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|
||
<a href="../../ansible/" class="md-nav__link">
|
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|
||
|
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|
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<span class="md-ellipsis">
|
||
|
||
|
||
Hardened Infrastructure
|
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</span>
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<span class="md-nav__icon md-icon"></span>
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<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
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|
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|
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<a href="../../GoogleCloudPlatform/" class="md-nav__link">
|
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|
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|
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|
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<span class="md-ellipsis">
|
||
|
||
|
||
Cloud Providers (Hyperscalers)
|
||
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|
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|
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|
||
<a href="../../caching/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Networking & Service Mesh
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
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|
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|
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|
||
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|
||
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||
|
||
|
||
|
||
|
||
<a href="../../container-managers/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
The Container Stack
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
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|
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|
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|
||
|
||
|
||
|
||
|
||
|
||
<a href="../../crunchydata/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Data & Advanced Analytics
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
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|
||
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||
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||
|
||
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|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../../argo/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Engineering Pipeline
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
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||
|
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||
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||
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||
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||
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||
|
||
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||
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||
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||
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||
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||
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||
|
||
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||
|
||
|
||
|
||
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||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
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||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
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||
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||
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||
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||
|
||
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||
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||
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||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../../ChromeDevTools/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Developer Ecosystem
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../../appointment-scheduling/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Career & Industry
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
|
||
<div class="md-content" data-md-component="content">
|
||
|
||
<article class="md-content__inner md-typeset">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="https://github.com/nubenetes/awesome-kubernetes/edit/master/v2-docs/videos/ai-agents.md" title="Edit this page" class="md-content__button md-icon" rel="edit">
|
||
|
||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4zm10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1z"/></svg>
|
||
</a>
|
||
|
||
|
||
|
||
|
||
|
||
<a href="https://github.com/nubenetes/awesome-kubernetes/raw/master/v2-docs/videos/ai-agents.md" title="View source of this page" class="md-content__button md-icon">
|
||
|
||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M17 18c.56 0 1 .44 1 1s-.44 1-1 1-1-.44-1-1 .44-1 1-1m0-3c-2.73 0-5.06 1.66-6 4 .94 2.34 3.27 4 6 4s5.06-1.66 6-4c-.94-2.34-3.27-4-6-4m0 6.5a2.5 2.5 0 0 1-2.5-2.5 2.5 2.5 0 0 1 2.5-2.5 2.5 2.5 0 0 1 2.5 2.5 2.5 2.5 0 0 1-2.5 2.5M9.27 20H6V4h7v5h5v4.07c.7.08 1.36.25 2 .49V8l-6-6H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h4.5a8.2 8.2 0 0 1-1.23-2"/></svg>
|
||
</a>
|
||
|
||
|
||
|
||
<h1 id="ai-agents-and-mcp">🎥 AI Agents and MCP<a class="headerlink" href="#ai-agents-and-mcp" title="Permanent link">¶</a></h1>
|
||
<div class="admonition tip">
|
||
<p class="admonition-title">Nubenetes V2 Elite Portal</p>
|
||
<p>You are browsing the AI-Curated V2 Elite Edition. Looking for the exhaustive list of references? Check out the <a href="/v1/"><strong>V1 Historical Archive</strong></a>.</p>
|
||
</div>
|
||
<p>Welcome to the <strong>AI Agents and MCP</strong> section of the V2 Video Hub. Explore curated high-density videos with architectural summaries.</p>
|
||
<h2 id="table-of-contents">Table of Contents<a class="headerlink" href="#table-of-contents" title="Permanent link">¶</a></h2>
|
||
<ol>
|
||
<li><a href="#red-hat-openshift">Red Hat OpenShift</a></li>
|
||
<li><a href="#grafana-assistant-claude-code-grafana-mcp-server">Grafana Assistant Claude Code Grafana MCP Server</a></li>
|
||
<li><a href="#generative-ai-and-large-language-models">Generative AI and Large Language Models</a></li>
|
||
<li><a href="#red-hat-openshift-ai">Red Hat OpenShift AI</a></li>
|
||
<li><a href="#claude-code">Claude Code</a></li>
|
||
<li><a href="#neural-networks">Neural Networks</a></li>
|
||
<li><a href="#llm-architecture-and-post-training">LLM Architecture and Post-Training</a></li>
|
||
<li><a href="#agentic-devops">Agentic DevOps</a></li>
|
||
<li><a href="#ai-agents-and-observability">AI Agents and Observability</a></li>
|
||
<li><a href="#devops-ai-agents">DevOps AI Agents</a></li>
|
||
<li><a href="#sre-ai-agents">SRE AI Agents</a></li>
|
||
<li><a href="#ai-agents-distributed-ml-operations">AI Agents Distributed ML Operations</a></li>
|
||
<li><a href="#iam-ai-agents">IAM AI Agents</a></li>
|
||
<li><a href="#llms-ai-agents-mlops">LLMs AI Agents MLOps</a></li>
|
||
<li><a href="#mlops-ai-agents">MLOps AI Agents</a></li>
|
||
<li><a href="#firebase-genkit-gemini-ai-agents">Firebase Genkit Gemini AI Agents</a></li>
|
||
<li><a href="#android-gemini-ai">Android Gemini AI</a></li>
|
||
<li><a href="#mobile-cloud-integration-ai-agents">Mobile Cloud Integration AI Agents</a></li>
|
||
</ol>
|
||
<h2 id="red-hat-openshift">Red Hat OpenShift<a class="headerlink" href="#red-hat-openshift" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 Thursday morning general session - May 9 - Red Hat Summit 2019</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This session outlines the architectural enablement of cloud-native AI/ML workloads by integrating Red Hat OpenShift with NVIDIA GPU acceleration and automated MLOps platforms like H2O.ai and ProphetStor. It demonstrates how standardizing on a Kubernetes-based hybrid cloud substrate abstracts heterogeneous hardware environments, facilitating deterministic scaling, resource orchestration, and cognitive monitoring for high-performance AI pipelines. This unified operational model serves as a foundational blueprint for modern enterprise AI-platform engineering and edge computing architectures.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="FUu4kMc0PL8" data-video-url="https://www.youtube.com/embed/FUu4kMc0PL8?clip=UgkxLbUzIJtyeKPi66qAvxxRlGbofYp_Gr8B&clipt=EIDy0gIY4MbWAg" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/FUu4kMc0PL8/hqdefault.jpg" alt="Thursday morning general session - May 9 - Red Hat Summit 2019" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="grafana-assistant-claude-code-grafana-mcp-server">Grafana Assistant Claude Code Grafana MCP Server<a class="headerlink" href="#grafana-assistant-claude-code-grafana-mcp-server" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 I Stopped Staring at Dashboards. AI Reads My Grafana Metrics Now.</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This video explores how AI agents can read Grafana metrics, logs, and traces directly using Grafana Assistant and Claude Code wired up to the Grafana MCP server. It demonstrates querying Prometheus, Loki, and Tempo, generating custom dashboards from natural language, and analyzing runtime data from the command line. This workflow bridges the gap between analysis and remediation, allowing agents to verify fixes and make data-grounded decisions.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="HI6KleJAZPY" data-video-url="https://www.youtube.com/embed/HI6KleJAZPY" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/HI6KleJAZPY/hqdefault.jpg" alt="I Stopped Staring at Dashboards. AI Reads My Grafana Metrics Now." class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="generative-ai-and-large-language-models">Generative AI and Large Language Models<a class="headerlink" href="#generative-ai-and-large-language-models" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 Artificial Intelligence | 60 Minutes Full Episodes</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This documentary anthology traces the rapid evolution of artificial intelligence from early deep learning implementations to advanced generative AI systems like Google's Bard and OpenAI's ChatGPT. For 2026 cloud-native architectures, these developments highlight the critical need for integrating scalable AI model orchestration, strict ethical guardrails, and secure data pipelines directly into enterprise platform engineering. Understanding these socio-technical shifts assists cloud architects in designing resilient, compliant AI-integrated infrastructures that balance massive computational demands with robust operational governance.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="aZ5EsdnpLMI" data-video-url="https://www.youtube.com/embed/aZ5EsdnpLMI?si=ESsNnVwE8IdWSiWZ" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/aZ5EsdnpLMI/hqdefault.jpg" alt="Artificial Intelligence | 60 Minutes Full Episodes" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="red-hat-openshift-ai">Red Hat OpenShift AI<a class="headerlink" href="#red-hat-openshift-ai" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 Red Hat OpenShift AI overview</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>Red Hat OpenShift AI provides an enterprise-grade MLOps platform built on Kubernetes that standardizes the training, tuning, serving, and monitoring of foundation and predictive AI models across hybrid and multi-cloud environments. By integrating open-source frameworks like Jupyter, PyTorch, and KServe with certified hardware accelerators, it delivers a secure, consistent, and self-service environment for platform and data science teams. This architecture ensures robust AI governance, operational scalability, and accelerated time-to-market for intelligent cloud-native applications.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="Hc8emNr2igU" data-video-url="https://www.youtube.com/embed/Hc8emNr2igU?si=kehLRUpOAvyK_Bku" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/Hc8emNr2igU/hqdefault.jpg" alt="Red Hat OpenShift AI overview" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="claude-code">Claude Code<a class="headerlink" href="#claude-code" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 Mastering Claude Code in 30 minutes</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This technical session explores the architecture and implementation of Claude Code, Anthropic's agentic CLI designed for autonomous, multi-step engineering tasks. It details how the tool leverages the Model Context Protocol (MCP) to integrate with external data sources and documentation, enabling a self-healing development cycle where the agent autonomously reads code, executes shell commands, and iterates through test-driven development (TDD) loops. For 2026 platform engineers, mastering these agentic workflows is critical for scaling complex refactoring, managing cognitive load in large-scale repositories, and establishing robust human-in-the-loop (HITL) governance for AI-driven infrastructure operations.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="6eBSHbLKuN0" data-video-url="https://www.youtube.com/embed/6eBSHbLKuN0" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/6eBSHbLKuN0/hqdefault.jpg" alt="Mastering Claude Code in 30 minutes" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<details class="note">
|
||
<summary>🎬 The 6 Levels of Claude Code Explained</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This video provides a structured architectural roadmap for mastering Claude Code, Anthropic's agentic CLI tool for software engineering. It details the progression from basic code generation to complex, multi-step agentic workflows, emphasizing the tool's relevance to the 2026 Kubernetes ecosystem through automated manifest refactoring, contextual codebase analysis, and direct CLI-driven infrastructure orchestration. By exploring agentic "vibe coding" levels, it helps platform engineers integrate AI-augmented automation into mission-critical CI/CD pipelines and platform engineering workflows.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="TUKYbUIXLOE" data-video-url="https://www.youtube.com/embed/TUKYbUIXLOE" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/TUKYbUIXLOE/hqdefault.jpg" alt="The 6 Levels of Claude Code Explained" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="neural-networks">Neural Networks<a class="headerlink" href="#neural-networks" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 ¿Qué es una Red Neuronal? Parte 1 : La Neurona | DotCSV [SPANISH CONTENT]</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This video deconstructs the foundational mathematical and algorithmic mechanics of a single artificial neuron, illustrating its direct relationship with linear regression, weights, biases, and activation functions. In a 2026 cloud-native landscape, mastering these core neural principles is critical for platform architects optimizing distributed micro-models and real-time AI inference engines deployed on Kubernetes-driven edge and cloud infrastructure. This granular understanding enables more efficient hardware acceleration profiling (GPUs/vGPUs/TPUs) and smarter resource allocation for decentralized machine learning pipelines.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="MRIv2IwFTPg" data-video-url="https://www.youtube.com/embed/MRIv2IwFTPg?si=F07g869i6yIfqRdg" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/MRIv2IwFTPg/hqdefault.jpg" alt="¿Qué es una Red Neuronal? Parte 1 : La Neurona | DotCSV" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="llm-architecture-and-post-training">LLM Architecture and Post-Training<a class="headerlink" href="#llm-architecture-and-post-training" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 Stanford CS229: Building Large Language Models (LLMs)</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This Stanford CS229 technical deep-dive deconstructs the transition from raw autoregressive language models to instruction-tuned assistants, focusing on the systems orchestration required for 2026 AI infrastructure. It explores critical patterns in tokenization (BPE/Sub-word), parameter-efficient fine-tuning (PEFT/LoRA), and the shift from RLHF to Direct Preference Optimization (DPO) to simplify model alignment pipelines. For cloud architects, the lecture provides a foundational framework for optimizing the "Compute-to-Token" ratio and managing memory constraints (KV Cache) in distributed distributed inference environments, while advocating for LLM-as-a-Judge automated evaluation loops for scalable model governance.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="9vM4p9NN0Ts" data-video-url="https://www.youtube.com/embed/9vM4p9NN0Ts" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/9vM4p9NN0Ts/hqdefault.jpg" alt="Stanford CS229: Building Large Language Models (LLMs)" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="agentic-devops">Agentic DevOps<a class="headerlink" href="#agentic-devops" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 Agentic DevOps Live</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This official live series explores the paradigm shift from traditional CI/CD pipelines to autonomous, agent-driven operations (Agentic DevOps). It covers technical deep-dives into Azure SRE Agents for automated root cause analysis and proactive reliability, GitHub Copilot App Mod Agents for modernizing legacy systems at scale, and AI-powered workflows across GitHub and Azure DevOps. Essential for platform engineers designing self-healing environments and scalable human-in-the-loop AI governance in 2026.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><iframe width="720" height="405" src="https://www.youtube.com/embed/videoseries?list=PLmsFUfdnGr3w9BdWjQGAwV7UPW0kspwOp" title="Agentic DevOps Live" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="ai-agents-and-observability">AI Agents and Observability<a class="headerlink" href="#ai-agents-and-observability" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 AI Observability Deep Dive Demo | Grafana Cloud</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This deep-dive demonstration presents Grafana AI Observability, a specialized platform and database designed for monitoring agentic workflows, LLM usage, and AI Agent ecosystems. It outlines the instrumentation of AI agents using OpenTelemetry to capture vital performance metrics including execution costs, error rates, time-to-first-token, and tool latency. The session shows how online evaluations score agent outputs for helpfulness, safety, and hallucinations, enabling developers to conduct granular troubleshooting of failed agent runs. For 2026 platform engineers, these capabilities provide the necessary governance, cost tracking, and behavioral observability needed to operate production-grade agentic architectures.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="MFai008Ze84" data-video-url="https://www.youtube.com/embed/MFai008Ze84" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/MFai008Ze84/hqdefault.jpg" alt="AI Observability Deep Dive Demo | Grafana Cloud" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="devops-ai-agents">DevOps AI Agents<a class="headerlink" href="#devops-ai-agents" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 Model Context Protocol (MCP) and AI Agents in DevOps</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This architectural playlist details how Model Context Protocol (MCP) standardizes context and tool-calling interfaces for AI agents within modern cloud-native systems. It explores the practical integration of autonomous agents into DevOps, SRE, and API automation pipelines, replacing complex, manual scripts with intent-driven workflows. By utilizing secure and scalable MCP servers, platform teams can safely delegate infrastructure, database, and testing orchestration to context-aware AI systems.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><iframe width="720" height="405" src="https://www.youtube.com/embed/videoseries?list=PLA6Ht2dJt3SId7vE9P5mppWdj65_l6hl7" title="Model Context Protocol (MCP) and AI Agents in DevOps" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe></p>
|
||
</center>
|
||
</details>
|
||
<details class="note">
|
||
<summary>🎬 Cloud at Google I/O 2026</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This playlist presents the Cloud Native and GenAI architectural frameworks showcased at Google I/O 2026, focusing on the transition from traditional DevOps to 'agent-first' autonomous workflows using Google Cloud’s serverless stack (Cloud Run, Eventarc, Cloud Build, BigQuery) and the Agent Development Kit (ADK). By utilizing Model Context Protocol (MCP) and event-driven architectures, developers can orchestrate specialized multi-agent swarms to handle complex Day-2 operations, such as self-healing remediation and intelligent CI/CD pipelines. This paradigm shift provides a highly secure, automated foundation for scaling and governing AI-native applications directly from code to production.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><iframe width="720" height="405" src="https://www.youtube.com/embed/videoseries?list=PLOU2XLYxmsIJp39MsvkeWYYNwMso-HeNT" title="Cloud at Google I/O 2026" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe></p>
|
||
</center>
|
||
</details>
|
||
<details class="note">
|
||
<summary>🎬 Agent-first workflows from prompt to production</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This session outlines an end-to-end architectural workflow for transitioning from AI-agentic code generation directly to secure, production-grade deployments on Google Cloud. It demonstrates how developers can utilize IDE-integrated Gemini Code Assist agents, Cloud Run, and Cloud Build to build, containerize, and deploy AI-native applications without leaving their development environments. The presentation emphasizes securing the agentic software development lifecycle (SDLC) using enterprise-grade IAM, automated CI/CD, and policy-driven compliance.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><iframe width="720" height="405" src="https://www.youtube.com/embed/QtXBAz6TU2g?list=PLOU2XLYxmsIKL_eEgkKJWDRhYUEvS9eYz" title="Agent-first workflows from prompt to production" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="sre-ai-agents">SRE AI Agents<a class="headerlink" href="#sre-ai-agents" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 The Shift Podcast by Microsoft Azure—Agentic Edition</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This technical podcast series details the evolution of cloud-native infrastructure required to support autonomous, context-aware AI agents in enterprise environments. It explores critical engineering paradigms such as multi-agent orchestration, context engineering over traditional RAG, unified data fabrics for governed access, and Postgres-centric vector storage. Additionally, it addresses the SRE and operations dimension by analyzing how agentic AI is reforming IT operations, cloud management, and security governance through defined agentic borders.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><iframe width="720" height="405" src="https://www.youtube.com/embed/videoseries?list=PLLasX02E8BPBCP7KdYsjKKFFQUmNEUmE9" title="The Shift Podcast by Microsoft Azure—Agentic Edition" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="ai-agents-distributed-ml-operations">AI Agents Distributed ML Operations<a class="headerlink" href="#ai-agents-distributed-ml-operations" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 Dialogues at Google I/O 2026</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This playlist explores the shift toward proactive, agentic AI, physical robotics, and the convergence of quantum computing with distributed machine learning models. For a 2026 Cloud Native context, it highlights how these autonomous agentic workflows and advanced model capabilities transition enterprise operations from reactive automation to proactive, self-healing system management. This shift fundamentally redefines how distributed computing infrastructures orchestrate, monitor, and scale complex, multi-agent AI workloads.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><iframe width="720" height="405" src="https://www.youtube.com/embed/videoseries?list=PLOU2XLYxmsIJuxYe1znksQlhLJ4w8GKCu" title="Dialogues at Google I/O 2026" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="iam-ai-agents">IAM AI Agents<a class="headerlink" href="#iam-ai-agents" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 Your AI Agent Has No Identity, Here's Why That's Dangerous</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This architectural discussion addresses the critical vulnerability in modern IAM frameworks where autonomous AI agents lack stable, verifiable identity constructs, leading to dangerous dependencies on long-lived API tokens. It explores how the Model Context Protocol (MCP) is redefining agent-to-agent (A2A) and human-to-agent authentication, highlighting strategies to mitigate prompt injection and secure ephemeral credentials in decentralized agentic workflows. Ultimately, it details how modern enterprise security architectures must adapt to protect billions of credentials as multi-agent orchestration becomes standard in cloud-native environments.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="BtgZLw8hYiU" data-video-url="https://www.youtube.com/embed/BtgZLw8hYiU" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/BtgZLw8hYiU/hqdefault.jpg" alt="Your AI Agent Has No Identity, Here's Why That's Dangerous" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="llms-ai-agents-mlops">LLMs AI Agents MLOps<a class="headerlink" href="#llms-ai-agents-mlops" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 A Hacker's Guide to Language Models</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This foundational guide demystifies Large Language Models (LLMs) for system engineers, demonstrating how to pragmatically integrate, prompt, and fine-tune models using direct APIs and local deployments rather than heavy frameworks. For cloud-native architects, it provides the essential blueprint for building resource-efficient, low-latency AI agents and MLOps pipelines that run reliably within containerized microservice architectures.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="DjBBV7oZxm8" data-video-url="https://www.youtube.com/embed/DjBBV7oZxm8" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/DjBBV7oZxm8/hqdefault.jpg" alt="A Hacker's Guide to Language Models" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="mlops-ai-agents">MLOps AI Agents<a class="headerlink" href="#mlops-ai-agents" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 What's new in Google AI</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This presentation outlines Google's latest end-to-end AI stack from Google I/O 2026, focusing on deployment strategies for multimodal models, media generation, and intelligent agents. It details how to leverage Google's managed infrastructure and MLOps tools to tune, serve, and scale open-source models in cloud-native environments. Additionally, it introduces new 'vibe-coding' workflows and agentic capabilities designed to streamline next-generation AI application development.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="SSe1VmVrtw0" data-video-url="https://www.youtube.com/embed/SSe1VmVrtw0" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/SSe1VmVrtw0/hqdefault.jpg" alt="What's new in Google AI" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<details class="note">
|
||
<summary>🎬 Inside image generation’s Renaissance moment — the OpenAI Podcast Ep. 19</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This episode details the architectural evolution of OpenAI's image generation capabilities to Images 2.0, focusing on the shift from single-turn prompting to multi-modal 'creative agents' integrated with code execution engines like Codex. It addresses the MLOps and infrastructure challenges of scaling real-time generative pipelines to 1.5 billion weekly requests while maintaining strict character consistency and prompt alignment. For 2026 Cloud Native topologies, this highlights the necessity of building low-latency, event-driven agent orchestration layers that seamlessly bind generative media models with stateful transactional systems.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="bH2nP-aCFjk" data-video-url="https://www.youtube.com/embed/bH2nP-aCFjk" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/bH2nP-aCFjk/hqdefault.jpg" alt="Inside image generation’s Renaissance moment — the OpenAI Podcast Ep. 19" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<details class="note">
|
||
<summary>🎬 ComfyUI + Google Models: A Creator-Friendly Agent Workflow</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This architectural overview highlights Google Cloud's integration of Gemini, Veo, and Imagen models with Avid and ComfyUI to deliver secure, agentic workflows for media production. The framework details how multimodal search and generative AI pipelines are orchestrated on Google Cloud compute infrastructure, streamlining video rendering, editing, and creative asset management.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><div class="video-lazy-container" data-video-id="ppMWxjsmk2k" data-video-url="https://www.youtube.com/embed/ppMWxjsmk2k" style="position: relative; width: 720px; max-width: 100%; aspect-ratio: 16/9; background: #000; border-radius: 8px; overflow: hidden; cursor: pointer; border: 1px solid var(--md-typeset-table-color);">
|
||
<img src="https://img.youtube.com/vi/ppMWxjsmk2k/hqdefault.jpg" alt="ComfyUI + Google Models: A Creator-Friendly Agent Workflow" class="video-lazy-thumbnail" style="width: 100%; height: 100%; object-fit: cover; opacity: 0.8; transition: opacity 0.3s, transform 0.3s;">
|
||
<div class="video-lazy-play-btn" style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 68px; height: 48px; background: rgba(0, 0, 0, 0.7); border-radius: 12px; display: flex; align-items: center; justify-content: center; transition: all 0.3s ease;">
|
||
<svg viewBox="0 0 24 24" style="width: 30px; height: 30px; fill: #fff; transition: fill 0.3s;"><path d="M8 5v14l11-7z"/></svg>
|
||
</div>
|
||
</div></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="firebase-genkit-gemini-ai-agents">Firebase Genkit Gemini AI Agents<a class="headerlink" href="#firebase-genkit-gemini-ai-agents" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 Developer Keynote (Google I/O '24)</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>The Google I/O 2024 Developer Keynote details key advancements in building agentic workflows using Firebase Genkit and Gemini 1.5 Pro, establishing modern paradigms for orchestrating AI agents in cloud-native architectures. It also introduces client-side rendering breakthroughs with Flutter's Impeller engine ('Antigravity') and highlights decentralized edge computing via built-in Gemini Nano integration across Chrome and Android platforms.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><iframe width="720" height="405" src="https://www.youtube.com/embed/aqmpZocmR8o?list=PLOU2XLYxmsIKL_eEgkKJWDRhYUEvS9eYz" title="Developer Keynote (Google I/O '24)" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="android-gemini-ai">Android Gemini AI<a class="headerlink" href="#android-gemini-ai" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 What's new in Android development tools</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This session outlines the latest developer tool advancements in Android Studio, highlighting Gemini AI integrations that accelerate mobile development workflows across modern Android APIs. It showcases how cloud-connected AI capabilities and intelligent tooling enhance developer velocity, which is critical for scaling cloud-native mobile applications and modern CI/CD pipelines.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><iframe width="720" height="405" src="https://www.youtube.com/embed/iyc69njNYOw?list=PLOU2XLYxmsIKL_eEgkKJWDRhYUEvS9eYz" title="What's new in Android development tools" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe></p>
|
||
</center>
|
||
</details>
|
||
<h2 id="mobile-cloud-integration-ai-agents">Mobile Cloud Integration AI Agents<a class="headerlink" href="#mobile-cloud-integration-ai-agents" title="Permanent link">¶</a></h2>
|
||
<details class="note">
|
||
<summary>🎬 What's new in Android</summary>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Summary</p>
|
||
<p>This session outlines the architectural evolution in Android 17, highlighting advancements in Jetpack Compose, performance optimizations, and the integration of client-side agentic automation. For cloud-native environments, this shifts the paradigm toward edge-to-cloud coordination, requiring backend architectures to seamlessly ingest, secure, and support high-frequency orchestrations driven by autonomous mobile AI agents.</p>
|
||
</div>
|
||
<center markdown="1">
|
||
<p><iframe width="720" height="405" src="https://www.youtube.com/embed/sig3n7XyaaA?list=PLOU2XLYxmsIKL_eEgkKJWDRhYUEvS9eYz" title="What's new in Android" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe></p>
|
||
</center>
|
||
</details>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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