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2026-01-22 12:26:30 -03:00
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Media Analyzer - Documentation</title>
<link rel="stylesheet" href="styles.css">
</head>
<body>
<header>
<h1>Media Analyzer</h1>
<p class="subtitle">Real-Time Video Analysis Platform</p>
</header>
<main>
<section class="graph-section" id="system-architecture">
<div class="graph-header-row">
<h2>System Architecture</h2>
<a href="architecture/architecture_diagram.svg" class="view-btn" target="_blank">View Full</a>
</div>
<a href="architecture/architecture_diagram.svg" class="graph-preview" target="_blank">
<img src="architecture/architecture_diagram.svg" alt="System Architecture Diagram">
</a>
<div class="graph-details">
<p>Complete system overview showing video ingestion, AI processing pipeline, and real-time dashboard components.</p>
<h4>Components</h4>
<ul>
<li><strong>Video Ingestion</strong>: RTMP streams from OBS, FFmpeg HLS conversion</li>
<li><strong>AI Processing</strong>: CLIP/YOLO for logo detection and scene analysis</li>
<li><strong>Real-time Communication</strong>: Django Channels WebSocket for live updates</li>
<li><strong>Frontend</strong>: Angular 17+ with HLS.js video player and Canvas overlays</li>
</ul>
</div>
</section>
<section class="findings-section">
<h2>Key Features</h2>
<div class="findings-grid">
<article class="finding-card">
<h3>Video Streaming</h3>
<ul>
<li>RTMP ingestion from OBS Studio</li>
<li>FFmpeg HLS conversion</li>
<li>Event-driven segment detection</li>
<li>WebSocket-powered live updates</li>
</ul>
</article>
<article class="finding-card">
<h3>AI Analysis</h3>
<ul>
<li>Logo/brand detection (CLIP)</li>
<li>Object detection (YOLO)</li>
<li>Real-time vs batch processing modes</li>
<li>Switchable local/cloud backends</li>
</ul>
</article>
<article class="finding-card">
<h3>Infrastructure</h3>
<ul>
<li>Docker containerized services</li>
<li>Kubernetes orchestration</li>
<li>GCP integration (Storage, Vision)</li>
<li>Celery task queue with Redis</li>
</ul>
</article>
</div>
</section>
<section class="tech-section">
<h2>Technology Stack</h2>
<div class="tech-grid">
<div class="tech-column">
<h3>Backend</h3>
<ul>
<li>Django + Channels</li>
<li>Django REST Framework</li>
<li>PostgreSQL</li>
<li>Celery + Redis</li>
<li>FFmpeg</li>
</ul>
</div>
<div class="tech-column">
<h3>AI/ML</h3>
<ul>
<li>OpenCV</li>
<li>CLIP (scene analysis)</li>
<li>YOLO (object detection)</li>
<li>Hugging Face Transformers</li>
<li>GCP Vision API</li>
</ul>
</div>
<div class="tech-column">
<h3>Frontend</h3>
<ul>
<li>Angular 17+</li>
<li>HLS.js video player</li>
<li>Canvas overlays</li>
<li>WebSocket client</li>
<li>Standalone components</li>
</ul>
</div>
</div>
</section>
<section class="findings-section">
<h2>Architecture Goals</h2>
<div class="findings-grid">
<article class="finding-card">
<h3>Event-Driven Design</h3>
<p>File system watchers detect new HLS segments, triggering AI processing and real-time WebSocket notifications.</p>
</article>
<article class="finding-card">
<h3>Scalable Processing</h3>
<p>Celery workers handle AI tasks with configurable queues for real-time vs batch processing modes.</p>
</article>
<article class="finding-card">
<h3>Cloud-Native</h3>
<p>Kubernetes manifests for local (KIND) and production (GKE) deployment with easy environment switching.</p>
</article>
</div>
</section>
</main>
<footer>
<p>Media Analyzer - Real-Time Video Analysis Platform</p>
</footer>
</body>
</html>

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/* Reset and base */
*, *::before, *::after {
box-sizing: border-box;
}
:root {
--color-bg: #0f0f0f;
--color-surface: #1a1a1a;
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--color-border: #333;
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--color-text-muted: #888;
--color-accent: #4A90D9;
--color-accent-light: #6BA3E0;
--radius: 8px;
--shadow: 0 2px 8px rgba(0,0,0,0.3);
}
html {
font-size: 16px;
}
body {
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, Ubuntu, sans-serif;
background: var(--color-bg);
color: var(--color-text);
line-height: 1.6;
margin: 0;
padding: 0;
min-height: 100vh;
}
/* Header */
header {
background: var(--color-surface);
border-bottom: 1px solid var(--color-border);
padding: 2rem;
text-align: center;
}
header h1 {
margin: 0;
font-size: 2rem;
font-weight: 600;
color: var(--color-text);
}
header .subtitle {
margin: 0.5rem 0 0;
color: var(--color-text-muted);
font-size: 1rem;
}
/* Main content */
main {
max-width: 1400px;
margin: 0 auto;
padding: 2rem;
}
section {
margin-bottom: 3rem;
}
section h2 {
font-size: 1.5rem;
font-weight: 600;
margin: 0 0 1.5rem;
padding-bottom: 0.5rem;
border-bottom: 2px solid var(--color-border);
}
/* Card Grid */
.card-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
gap: 1.5rem;
}
@media (min-width: 1000px) {
.card-grid {
grid-template-columns: repeat(3, 1fr);
}
}
@media (min-width: 700px) and (max-width: 999px) {
.card-grid {
grid-template-columns: repeat(2, 1fr);
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/* Graph Cards */
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background: var(--color-surface);
border: 1px solid var(--color-border);
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overflow: hidden;
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color: inherit;
transition: transform 0.2s, box-shadow 0.2s, border-color 0.2s;
}
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transform: translateY(-2px);
box-shadow: var(--shadow);
border-color: var(--color-accent);
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.card-preview {
aspect-ratio: 4/3;
background: #fff;
display: flex;
align-items: center;
justify-content: center;
overflow: hidden;
padding: 1rem;
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/* Findings Grid */
.findings-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
gap: 1.5rem;
}
@media (min-width: 1000px) {
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.finding-card {
background: var(--color-surface);
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background: var(--color-bg);
padding: 1rem;
border-radius: 4px;
overflow-x: auto;
margin-top: 0.5rem;
font-size: 0.85rem;
}
/* Tech Stack Grid */
.tech-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 1.5rem;
}
@media (min-width: 700px) {
.tech-grid {
grid-template-columns: repeat(3, 1fr);
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background: var(--color-surface);
border: 1px solid var(--color-border);
border-radius: var(--radius);
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font-weight: 600;
color: var(--color-accent-light);
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margin: 0;
padding-left: 1.25rem;
font-size: 0.9rem;
}
.tech-column li {
margin-bottom: 0.25rem;
}
/* Graph Section */
.graph-section {
background: var(--color-surface);
border: 1px solid var(--color-border);
border-radius: var(--radius);
margin-bottom: 2rem;
overflow: hidden;
}
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justify-content: space-between;
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padding: 1rem 1.5rem;
border-bottom: 1px solid var(--color-border);
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.view-btn {
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text-decoration: none;
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border-radius: 4px;
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font-size: 0.85em;
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/* Footer */
footer {
background: var(--color-surface);
border-top: 1px solid var(--color-border);
padding: 1.5rem 2rem;
text-align: center;
color: var(--color-text-muted);
font-size: 0.9rem;
}
footer p {
margin: 0.25rem 0;
}
/* Mobile adjustments */
@media (max-width: 600px) {
main {
padding: 1rem;
}
header {
padding: 1.5rem 1rem;
}
header h1 {
font-size: 1.5rem;
}
.card-grid,
.findings-grid {
grid-template-columns: 1fr;
}
}

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goals of the video
show what I know of the stack thoroughly
show how how I deal with stuff I don't know
show my thought process and development workflow
show the current state of my use of AI tools
managing context
how I "code-review" it as it goes (def/whyclaudewhy, def/improvements, relevant discussions)
video structure
introduction
what you are about see
-
-
-
where I come from
keyboards (early days)
music vs coding (the gap gets wider)
recurrent back to basics
behind the scenes
the setup
deskmeter
timelapses/ffmpeg
demo
phase 1
phase 2
phase 3
extras
opinions
bootcamps
pimp-up-your-profile new trend
show the current state of my use of AI tools
motivations
im not in it (just) for the money
video processing is my passion (?