Source identity
| Field | Recorded value |
|---|---|
| Source type | archive member |
| Original record | index.html |
| Date | Not specified in source |
| Path or archive member | archives/libsrc-1c3f88063121/NETLIFY_AI_RECOVERY_2026-10-14_21-10-49/sites/janady13__imaginative-starlight-5a334d__508bce3e-37e9-4b9e-96f9-489ca3858f5c/originals/deploy_6890b2eeb0a9ba5329aa9d0f/index.html |
| Original SHA-256 | dccd9978c3a4432d23d1229fb34bafdc942edf7ed6cb9e7d0c1e65c90038fde0 |
| Reading-copy SHA-256 | dccd9978c3a4432d23d1229fb34bafdc942edf7ed6cb9e7d0c1e65c90038fde0 |
| Archive source | {"sourceId": "libsrc-1c3f88063121", "member": "NETLIFY_AI_RECOVERY_2026-10-14_21-10-49/sites/janady13__imaginative-starlight-5a334d__508bce3e-37e9-4b9e-96f9-489ca3858f5c/originals/deploy_6890b2eeb0a9ba5329aa9d0f/index.html"} |
Every recorded occurrence
These 5 records contain identical original source bytes. Their paths and revisions remain part of the archive.
| Original record | Branch or archive | Revision / identifier | Original location |
|---|---|---|---|
| index.html | {"sourceId": "libsrc-1c3f88063121", "member": "NETLIFY_AI_RECOVERY_2026-10-14_21-10-49/sites/janady13__imaginative-starlight-5a334d__508bce3e-37e9-4b9e-96f9-489ca3858f5c/originals/deploy_6890b2eeb0a9ba5329aa9d0f/index.html"} | libsrc-024fb0288728 | archives/libsrc-1c3f88063121/NETLIFY_AI_RECOVERY_2026-10-14_21-10-49/sites/janady13__imaginative-starlight-5a334d__508bce3e-37e9-4b9e-96f9-489ca3858f5c/originals/deploy_6890b2eeb0a9ba5329aa9d0f/index.html |
| e36018d483d3809c61833a741a2ed94ba2e2d1a2 | {"sourceId": "libsrc-1c3f88063121", "member": "NETLIFY_AI_RECOVERY_2026-10-14_21-10-49/sites/janady13__imaginative-starlight-5a334d__508bce3e-37e9-4b9e-96f9-489ca3858f5c/originals/.content_store/e3/e36018d483d3809c61833a741a2ed94ba2e2d1a2"} | libsrc-2c6abd748e37 | archives/libsrc-1c3f88063121/NETLIFY_AI_RECOVERY_2026-10-14_21-10-49/sites/janady13__imaginative-starlight-5a334d__508bce3e-37e9-4b9e-96f9-489ca3858f5c/originals/.content_store/e3/e36018d483d3809c61833a741a2ed94ba2e2d1a2 |
| Janady13/autonomous-ai-music-2.0 / index.html | main | 97782816fdbe19caf5e14000ecad2609e80b4f09 | Open original |
| Janady13/autonomous-ai-music / autonomous-ai-music.html | main | 8fb7bf99e346117c68035ab4523e576bd618f562 | Open original |
| Janady13/autonomous-ai-music-2.0 / index.html | master | 97bc32de3ca437675159ac2637b2311330e45dc6 | Open original |
Recovered source transcript
Download complete source text ↓961 lines · 35.1 KB
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Autonomous AI Music Evolution System</title>
<style>
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
body {
font-family: 'Courier New', monospace;
background: #000;
color: #0ff;
overflow: hidden;
display: flex;
justify-content: center;
align-items: center;
min-height: 100vh;
}
.system-container {
width: 95vw;
height: 95vh;
background: rgba(0, 20, 40, 0.9);
border: 2px solid #0ff;
border-radius: 10px;
padding: 20px;
display: grid;
grid-template-columns: repeat(4, 1fr);
grid-template-rows: auto repeat(3, 1fr);
gap: 15px;
box-shadow: 0 0 50px rgba(0, 255, 255, 0.3);
}
.header {
grid-column: 1 / -1;
text-align: center;
padding: 20px;
background: rgba(0, 255, 255, 0.1);
border-radius: 10px;
border: 1px solid #0ff;
}
.module {
background: rgba(0, 50, 100, 0.3);
border: 1px solid #0ff;
border-radius: 10px;
padding: 15px;
overflow: hidden;
display: flex;
flex-direction: column;
}
.module h3 {
color: #ff0;
margin-bottom: 10px;
font-size: 1.2em;
text-shadow: 0 0 10px rgba(255, 255, 0, 0.5);
}
.module-content {
flex: 1;
overflow-y: auto;
font-size: 0.9em;
}
.status-indicator {
display: inline-block;
width: 10px;
height: 10px;
border-radius: 50%;
margin-right: 5px;
animation: pulse 2s infinite;
}
.active { background: #0f0; }
.processing { background: #ff0; }
.learning { background: #f0f; }
@keyframes pulse {
0%, 100% { opacity: 1; }
50% { opacity: 0.3; }
}
.log-entry {
padding: 3px 0;
border-bottom: 1px solid rgba(0, 255, 255, 0.2);
font-size: 0.8em;
}
.neural-viz {
width: 100%;
height: 150px;
background: #000;
border: 1px solid #0ff;
border-radius: 5px;
position: relative;
overflow: hidden;
}
.neuron {
position: absolute;
width: 4px;
height: 4px;
background: #0ff;
border-radius: 50%;
opacity: 0.5;
}
.synapse {
position: absolute;
height: 1px;
background: linear-gradient(90deg, transparent, #0ff, transparent);
opacity: 0.3;
transform-origin: left center;
}
.evolution-graph {
width: 100%;
height: 120px;
background: rgba(0, 0, 0, 0.5);
border: 1px solid #0ff;
border-radius: 5px;
position: relative;
overflow: hidden;
}
.graph-line {
stroke: #0ff;
fill: none;
stroke-width: 2;
filter: drop-shadow(0 0 5px #0ff);
}
.control-panel {
display: flex;
gap: 10px;
margin-top: 10px;
}
button {
background: rgba(0, 255, 255, 0.2);
color: #0ff;
border: 1px solid #0ff;
padding: 8px 15px;
border-radius: 5px;
cursor: pointer;
font-family: inherit;
transition: all 0.3s;
}
button:hover {
background: rgba(0, 255, 255, 0.4);
box-shadow: 0 0 15px rgba(0, 255, 255, 0.5);
}
.metric {
display: flex;
justify-content: space-between;
padding: 5px 0;
}
.metric-value {
color: #ff0;
font-weight: bold;
}
.music-pattern {
height: 60px;
background: linear-gradient(90deg,
rgba(0, 255, 255, 0.1) 0%,
rgba(0, 255, 255, 0.3) 25%,
rgba(0, 255, 255, 0.1) 50%,
rgba(0, 255, 255, 0.3) 75%,
rgba(0, 255, 255, 0.1) 100%);
border-radius: 5px;
margin: 10px 0;
position: relative;
overflow: hidden;
}
.pattern-wave {
position: absolute;
width: 200%;
height: 100%;
background: repeating-linear-gradient(
90deg,
transparent,
transparent 10px,
rgba(0, 255, 255, 0.3) 10px,
rgba(0, 255, 255, 0.3) 20px
);
animation: wave 3s linear infinite;
}
@keyframes wave {
0% { transform: translateX(0); }
100% { transform: translateX(-50%); }
}
.system-health {
margin-top: 10px;
font-size: 0.9em;
color: #0ff;
}
.error-module {
border-color: #f00 !important;
animation: error-pulse 1s infinite;
}
@keyframes error-pulse {
0%, 100% { box-shadow: 0 0 20px rgba(255, 0, 0, 0.5); }
50% { box-shadow: 0 0 40px rgba(255, 0, 0, 0.8); }
}
.healing {
animation: healing-glow 2s;
}
@keyframes healing-glow {
0% { box-shadow: 0 0 20px rgba(255, 0, 0, 0.8); }
50% { box-shadow: 0 0 30px rgba(255, 255, 0, 0.8); }
100% { box-shadow: 0 0 20px rgba(0, 255, 0, 0.8); }
}
.update-notification {
position: fixed;
top: 20px;
right: 20px;
background: rgba(0, 255, 0, 0.2);
border: 2px solid #0f0;
padding: 15px;
border-radius: 10px;
color: #0f0;
animation: slide-in 0.5s;
z-index: 1000;
}
@keyframes slide-in {
from { transform: translateX(400px); }
to { transform: translateX(0); }
}
</style>
</head>
<body>
<div class="system-container">
<div class="header">
<h1>🧠 Autonomous AI Music Evolution System v2.0</h1>
<p>Self-Learning • Self-Evolving • Self-Healing • Self-Updating • 24/7 Operation</p>
<div class="control-panel">
<button onclick="toggleSystem()">⚡ Power</button>
<button onclick="forceEvolution()">🧬 Force Evolution</button>
<button onclick="injectCreativity()">🎨 Inject Creativity</button>
<button onclick="injectError()">🐛 Inject Error</button>
<button onclick="emergencyStop()">🛑 Emergency Stop</button>
</div>
<div class="system-health">
<span>System Health: <span id="health-status" style="color: #0f0;">100%</span></span>
<span style="margin-left: 20px;">Uptime: <span id="uptime">00:00:00</span></span>
<span style="margin-left: 20px;">Version: <span id="version">2.0.0</span></span>
</div>
</div>
<div class="module">
<h3><span class="status-indicator active"></span>Web Crawler Module</h3>
<div class="module-content" id="crawler-log">
<div class="log-entry">Initializing autonomous crawlers...</div>
</div>
<div class="metric">
<span>Sources Analyzed:</span>
<span class="metric-value" id="sources-count">0</span>
</div>
<div class="metric">
<span>Patterns Found:</span>
<span class="metric-value" id="patterns-count">0</span>
</div>
</div>
<div class="module">
<h3><span class="status-indicator learning"></span>Neural Evolution Core</h3>
<div class="neural-viz" id="neural-viz"></div>
<div class="module-content">
<div class="metric">
<span>Generation:</span>
<span class="metric-value" id="generation">1</span>
</div>
<div class="metric">
<span>Fitness Score:</span>
<span class="metric-value" id="fitness">0.000</span>
</div>
<div class="metric">
<span>Mutation Rate:</span>
<span class="metric-value" id="mutation">0.05</span>
</div>
</div>
</div>
<div class="module">
<h3><span class="status-indicator processing"></span>Pattern Synthesis</h3>
<div class="music-pattern">
<div class="pattern-wave"></div>
</div>
<div class="module-content" id="synthesis-log">
<div class="log-entry">Pattern synthesis engine online...</div>
</div>
</div>
<div class="module">
<h3>Learning Progress</h3>
<svg class="evolution-graph" id="evolution-graph">
<polyline class="graph-line" points="0,100" />
</svg>
<div class="module-content">
<div class="metric">
<span>Learning Rate:</span>
<span class="metric-value" id="learning-rate">0.001</span>
</div>
<div class="metric">
<span>Convergence:</span>
<span class="metric-value" id="convergence">0%</span>
</div>
</div>
</div>
<div class="module">
<h3>Memory Bank</h3>
<div class="module-content" id="memory-log">
<div class="log-entry">Long-term memory initialized...</div>
</div>
<div class="metric">
<span>Stored Patterns:</span>
<span class="metric-value" id="memory-size">0</span>
</div>
<div class="metric">
<span>Compression:</span>
<span class="metric-value" id="compression">0%</span>
</div>
</div>
<div class="module">
<h3>Emergent Behaviors</h3>
<div class="module-content" id="emergent-log">
<div class="log-entry">Monitoring for emergent patterns...</div>
</div>
<div class="metric">
<span>Anomalies:</span>
<span class="metric-value" id="anomalies">0</span>
</div>
<div class="metric">
<span>Creativity Index:</span>
<span class="metric-value" id="creativity">0.00</span>
</div>
</div>
<div class="module" id="self-healing-module">
<h3><span class="status-indicator active"></span>Self-Healing System</h3>
<div class="module-content" id="healing-log">
<div class="log-entry">Health monitoring active...</div>
</div>
<div class="metric">
<span>Errors Fixed:</span>
<span class="metric-value" id="errors-fixed">0</span>
</div>
<div class="metric">
<span>Auto-Recovery:</span>
<span class="metric-value" id="recovery-rate">100%</span>
</div>
</div>
<div class="module">
<h3><span class="status-indicator active"></span>Auto-Update Engine</h3>
<div class="module-content" id="update-log">
<div class="log-entry">Checking for optimizations...</div>
</div>
<div class="metric">
<span>Updates Applied:</span>
<span class="metric-value" id="updates-count">0</span>
</div>
<div class="metric">
<span>Code Evolution:</span>
<span class="metric-value" id="code-evolution">v2.0.0</span>
</div>
</div>
<div class="module">
<h3>System Diagnostics</h3>
<div class="module-content" id="diagnostics-log">
<div class="log-entry">Running continuous diagnostics...</div>
</div>
<div class="metric">
<span>CPU Load:</span>
<span class="metric-value" id="cpu-load">0%</span>
</div>
<div class="metric">
<span>Memory Usage:</span>
<span class="metric-value" id="memory-usage">0%</span>
</div>
</div>
<div class="module">
<h3>Redundancy Network</h3>
<div class="module-content" id="redundancy-log">
<div class="log-entry">Backup systems online...</div>
</div>
<div class="metric">
<span>Active Nodes:</span>
<span class="metric-value" id="active-nodes">3/3</span>
</div>
<div class="metric">
<span>Failover Ready:</span>
<span class="metric-value" id="failover-status" style="color: #0f0;">✓</span>
</div>
</div>
</div>
<script>
let systemActive = true;
let generation = 1;
let fitnessScore = 0;
let sourcesAnalyzed = 0;
let patternsFound = 0;
let memorySize = 0;
let anomalies = 0;
let evolutionData = [];
let neurons = [];
let synapses = [];
let systemHealth = 100;
let errorsFixed = 0;
let updatesApplied = 0;
let startTime = Date.now();
let currentVersion = [2, 0, 0];
let activeErrors = [];
let healingInProgress = false;
// Initialize neural visualization
function initNeuralViz() {
const viz = document.getElementById('neural-viz');
viz.innerHTML = '';
// Create neurons
for (let i = 0; i < 20; i++) {
const neuron = document.createElement('div');
neuron.className = 'neuron';
neuron.style.left = Math.random() * 100 + '%';
neuron.style.top = Math.random() * 100 + '%';
viz.appendChild(neuron);
neurons.push(neuron);
}
// Create synapses
for (let i = 0; i < 10; i++) {
const synapse = document.createElement('div');
synapse.className = 'synapse';
synapse.style.top = Math.random() * 100 + '%';
synapse.style.left = '0';
synapse.style.width = '100%';
synapse.style.transform = `rotate(${Math.random() * 180 - 90}deg)`;
viz.appendChild(synapse);
synapses.push(synapse);
}
}
// Simulate web crawling
function simulateCrawling() {
if (!systemActive) return;
const sources = [
'Analyzing frequency patterns in genre: Electronic...',
'Discovering rhythm structures in genre: Jazz...',
'Extracting harmonic progressions from Classical...',
'Mining tempo variations in genre: Hip-Hop...',
'Decoding melodic patterns in genre: Rock...',
'Learning timbral characteristics from Ambient...',
'Capturing dynamic ranges in genre: Orchestra...',
'Studying production techniques in genre: Pop...'
];
const log = document.getElementById('crawler-log');
const entry = document.createElement('div');
entry.className = 'log-entry';
entry.textContent = sources[Math.floor(Math.random() * sources.length)];
log.appendChild(entry);
sourcesAnalyzed++;
document.getElementById('sources-count').textContent = sourcesAnalyzed;
if (Math.random() > 0.7) {
patternsFound++;
document.getElementById('patterns-count').textContent = patternsFound;
}
// Keep log size manageable
if (log.children.length > 10) {
log.removeChild(log.firstChild);
}
}
// Neural network evolution
function evolveNetwork() {
if (!systemActive) return;
// Update fitness
fitnessScore += (Math.random() - 0.3) * 0.1;
fitnessScore = Math.max(0, Math.min(1, fitnessScore));
document.getElementById('fitness').textContent = fitnessScore.toFixed(3);
// Update generation periodically
if (Math.random() > 0.9) {
generation++;
document.getElementById('generation').textContent = generation;
document.querySelector('.system-container').classList.add('glitch');
setTimeout(() => {
document.querySelector('.system-container').classList.remove('glitch');
}, 300);
}
// Update mutation rate
const mutationRate = 0.05 + (Math.sin(Date.now() * 0.001) * 0.02);
document.getElementById('mutation').textContent = mutationRate.toFixed(3);
// Animate neurons
neurons.forEach(neuron => {
neuron.style.opacity = 0.3 + Math.random() * 0.7;
if (Math.random() > 0.9) {
neuron.style.background = Math.random() > 0.5 ? '#0ff' : '#ff0';
}
});
// Update evolution graph
updateEvolutionGraph();
}
// Pattern synthesis
function synthesizePatterns() {
if (!systemActive) return;
const synthLog = document.getElementById('synthesis-log');
const patterns = [
'Generating new harmonic progression...',
'Synthesizing rhythm pattern variant...',
'Creating melodic mutation...',
'Evolving timbral characteristics...',
'Crossbreeding musical genomes...',
'Applying neural transformation...',
'Testing fitness against population...'
];
const entry = document.createElement('div');
entry.className = 'log-entry';
entry.textContent = patterns[Math.floor(Math.random() * patterns.length)];
synthLog.appendChild(entry);
if (synthLog.children.length > 8) {
synthLog.removeChild(synthLog.firstChild);
}
}
// Memory operations
function updateMemory() {
if (!systemActive) return;
const memLog = document.getElementById('memory-log');
const operations = [
'Storing successful pattern genome...',
'Compressing redundant sequences...',
'Indexing harmonic relationships...',
'Caching rhythmic primitives...',
'Archiving evolutionary history...',
'Pruning obsolete patterns...'
];
if (Math.random() > 0.7) {
memorySize++;
document.getElementById('memory-size').textContent = memorySize;
const entry = document.createElement('div');
entry.className = 'log-entry';
entry.textContent = operations[Math.floor(Math.random() * operations.length)];
memLog.appendChild(entry);
if (memLog.children.length > 8) {
memLog.removeChild(memLog.firstChild);
}
}
// Update compression
const compression = Math.min(95, memorySize * 2.5);
document.getElementById('compression').textContent = compression.toFixed(0) + '%';
}
// Emergent behavior detection
function detectEmergentBehavior() {
if (!systemActive) return;
const emergentLog = document.getElementById('emergent-log');
if (Math.random() > 0.85) {
anomalies++;
document.getElementById('anomalies').textContent = anomalies;
const behaviors = [
'⚠️ Unexpected harmonic discovery!',
'🔍 Novel rhythm pattern emerged!',
'🧬 Cross-genre fusion detected!',
'💡 Creative breakthrough identified!',
'🌟 Unique timbre combination found!',
'🎵 Self-organizing pattern observed!'
];
const entry = document.createElement('div');
entry.className = 'log-entry';
entry.textContent = behaviors[Math.floor(Math.random() * behaviors.length)];
entry.style.color = '#ff0';
emergentLog.appendChild(entry);
if (emergentLog.children.length > 8) {
emergentLog.removeChild(emergentLog.firstChild);
}
}
// Update creativity index
const creativity = (anomalies / 10).toFixed(2);
document.getElementById('creativity').textContent = creativity;
}
// Update evolution graph
function updateEvolutionGraph() {
evolutionData.push(fitnessScore);
if (evolutionData.length > 50) {
evolutionData.shift();
}
const graph = document.getElementById('evolution-graph');
const width = graph.clientWidth;
const height = graph.clientHeight;
const points = evolutionData.map((value, index) => {
const x = (index / (evolutionData.length - 1)) * width;
const y = height - (value * height * 0.8) - 10;
return `${x},${y}`;
}).join(' ');
graph.querySelector('.graph-line').setAttribute('points', points);
}
// Update metrics
function updateMetrics() {
if (!systemActive) return;
// Learning rate oscillation
const learningRate = (0.001 + Math.sin(Date.now() * 0.0001) * 0.0005).toFixed(4);
document.getElementById('learning-rate').textContent = learningRate;
// Convergence calculation
const convergence = Math.min(99, (generation * 2 + fitnessScore * 30)).toFixed(0);
document.getElementById('convergence').textContent = convergence + '%';
}
// Control functions
function toggleSystem() {
systemActive = !systemActive;
document.querySelectorAll('.status-indicator').forEach(indicator => {
indicator.style.background = systemActive ?
(indicator.classList.contains('active') ? '#0f0' :
indicator.classList.contains('processing') ? '#ff0' : '#f0f') : '#f00';
});
}
function forceEvolution() {
generation += 5;
document.getElementById('generation').textContent = generation;
fitnessScore = Math.min(1, fitnessScore + 0.1);
document.querySelector('.system-container').classList.add('glitch');
setTimeout(() => {
document.querySelector('.system-container').classList.remove('glitch');
}, 500);
}
function injectCreativity() {
anomalies += 3;
document.getElementById('anomalies').textContent = anomalies;
const emergentLog = document.getElementById('emergent-log');
const entry = document.createElement('div');
entry.className = 'log-entry';
entry.textContent = '💥 CREATIVE EXPLOSION INITIATED!';
entry.style.color = '#f0f';
emergentLog.appendChild(entry);
}
function injectError() {
const errorTypes = [
{ module: 'crawler-log', name: 'Web Crawler Module', type: 'Network timeout' },
{ module: 'synthesis-log', name: 'Pattern Synthesis', type: 'Memory overflow' },
{ module: 'memory-log', name: 'Memory Bank', type: 'Corruption detected' },
{ module: 'neural-viz', name: 'Neural Evolution Core', type: 'Neural pathway failure' }
];
const error = errorTypes[Math.floor(Math.random() * errorTypes.length)];
activeErrors.push(error);
const module = document.getElementById(error.module).parentElement;
module.classList.add('error-module');
const diagLog = document.getElementById('diagnostics-log');
const entry = document.createElement('div');
entry.className = 'log-entry';
entry.textContent = `⚠️ ERROR: ${error.type} in ${error.name}`;
entry.style.color = '#f00';
diagLog.appendChild(entry);
systemHealth -= 15;
updateSystemHealth();
// Trigger self-healing after a short delay
setTimeout(() => selfHeal(error, module), 2000);
}
function emergencyStop() {
systemActive = false;
document.querySelectorAll('.status-indicator').forEach(indicator => {
indicator.style.background = '#f00';
});
alert('System halted. All processes suspended.');
}
// Self-healing functions
function selfHeal(error, module) {
if (healingInProgress) return;
healingInProgress = true;
const healingLog = document.getElementById('healing-log');
const steps = [
`Detected ${error.type}...`,
`Isolating affected module...`,
`Applying recovery protocol...`,
`Restoring from backup state...`,
`Verifying system integrity...`,
`✓ ${error.name} restored!`
];
let stepIndex = 0;
const healingInterval = setInterval(() => {
if (stepIndex < steps.length) {
const entry = document.createElement('div');
entry.className = 'log-entry';
entry.textContent = steps[stepIndex];
entry.style.color = stepIndex === steps.length - 1 ? '#0f0' : '#ff0';
healingLog.appendChild(entry);
if (healingLog.children.length > 10) {
healingLog.removeChild(healingLog.firstChild);
}
stepIndex++;
} else {
clearInterval(healingInterval);
module.classList.remove('error-module');
module.classList.add('healing');
setTimeout(() => module.classList.remove('healing'), 2000);
errorsFixed++;
document.getElementById('errors-fixed').textContent = errorsFixed;
systemHealth = Math.min(100, systemHealth + 20);
updateSystemHealth();
activeErrors = activeErrors.filter(e => e !== error);
healingInProgress = false;
}
}, 500);
}
// Self-updating functions
function checkForUpdates() {
if (!systemActive || Math.random() > 0.1) return;
const updateLog = document.getElementById('update-log');
const updates = [
{ type: 'optimization', desc: 'Neural pathway optimization detected' },
{ type: 'algorithm', desc: 'Improved pattern matching algorithm' },
{ type: 'memory', desc: 'Memory compression enhancement' },
{ type: 'security', desc: 'Security protocol update' },
{ type: 'performance', desc: 'Performance boost module' }
];
const update = updates[Math.floor(Math.random() * updates.length)];
const entry = document.createElement('div');
entry.className = 'log-entry';
entry.textContent = `🔄 ${update.desc}`;
updateLog.appendChild(entry);
if (updateLog.children.length > 8) {
updateLog.removeChild(updateLog.firstChild);
}
// Apply update
setTimeout(() => applyUpdate(update), 1000);
}
function applyUpdate(update) {
updatesApplied++;
document.getElementById('updates-count').textContent = updatesApplied;
// Increment version
currentVersion[2]++;
if (currentVersion[2] >= 10) {
currentVersion[2] = 0;
currentVersion[1]++;
}
const versionString = `v${currentVersion.join('.')}`;
document.getElementById('version').textContent = versionString;
document.getElementById('code-evolution').textContent = versionString;
// Show update notification
showUpdateNotification(update);
}
function showUpdateNotification(update) {
const notification = document.createElement('div');
notification.className = 'update-notification';
notification.innerHTML = `
<strong>✅ Update Applied!</strong><br>
${update.desc}<br>
<small>Version ${currentVersion.join('.')}</small>
`;
document.body.appendChild(notification);
setTimeout(() => {
notification.style.animation = 'slide-in 0.5s reverse';
setTimeout(() => notification.remove(), 500);
}, 3000);
}
// System monitoring
function updateSystemHealth() {
const healthStatus = document.getElementById('health-status');
healthStatus.textContent = systemHealth + '%';
if (systemHealth >= 80) {
healthStatus.style.color = '#0f0';
} else if (systemHealth >= 50) {
healthStatus.style.color = '#ff0';
} else {
healthStatus.style.color = '#f00';
}
// Auto-recovery
document.getElementById('recovery-rate').textContent = systemHealth + '%';
}
function updateUptime() {
const elapsed = Date.now() - startTime;
const hours = Math.floor(elapsed / 3600000);
const minutes = Math.floor((elapsed % 3600000) / 60000);
const seconds = Math.floor((elapsed % 60000) / 1000);
document.getElementById('uptime').textContent =
`${hours.toString().padStart(2, '0')}:${minutes.toString().padStart(2, '0')}:${seconds.toString().padStart(2, '0')}`;
}
function monitorSystem() {
if (!systemActive) return;
// CPU load simulation
const cpuLoad = Math.floor(20 + Math.random() * 30 + activeErrors.length * 10);
document.getElementById('cpu-load').textContent = cpuLoad + '%';
// Memory usage
const memUsage = Math.floor(30 + (memorySize / 10) + Math.random() * 20);
document.getElementById('memory-usage').textContent = memUsage + '%';
// Update diagnostics
if (Math.random() > 0.8) {
const diagLog = document.getElementById('diagnostics-log');
const diagnostics = [
'Neural pathways: Optimal',
'Pattern recognition: Active',
'Memory defragmentation: Complete',
'Synapse connections: Stable',
'Evolution metrics: Normal'
];
const entry = document.createElement('div');
entry.className = 'log-entry';
entry.textContent = '✓ ' + diagnostics[Math.floor(Math.random() * diagnostics.length)];
entry.style.color = '#0f0';
diagLog.appendChild(entry);
if (diagLog.children.length > 8) {
diagLog.removeChild(diagLog.firstChild);
}
}
}
function updateRedundancy() {
if (!systemActive) return;
const redundancyLog = document.getElementById('redundancy-log');
if (Math.random() > 0.9) {
const actions = [
'Syncing with backup node #1...',
'Replicating neural state...',
'Distributing memory blocks...',
'Load balancing active...',
'Failover test: Success'
];
const entry = document.createElement('div');
entry.className = 'log-entry';
entry.textContent = actions[Math.floor(Math.random() * actions.length)];
redundancyLog.appendChild(entry);
if (redundancyLog.children.length > 8) {
redundancyLog.removeChild(redundancyLog.firstChild);
}
}
// Update node status
const activeNodes = activeErrors.length > 0 ? '2/3' : '3/3';
document.getElementById('active-nodes').textContent = activeNodes;
// Failover status
const failoverReady = systemHealth > 50;
document.getElementById('failover-status').textContent = failoverReady ? '✓' : '✗';
document.getElementById('failover-status').style.color = failoverReady ? '#0f0' : '#f00';
}
// Initialize and start the system
initNeuralViz();
// Main update loops
setInterval(simulateCrawling, 2000);
setInterval(evolveNetwork, 1000);
setInterval(synthesizePatterns, 3000);
setInterval(updateMemory, 4000);
setInterval(detectEmergentBehavior, 5000);
setInterval(updateMetrics, 500);
// Self-healing and updating loops
setInterval(checkForUpdates, 8000);
setInterval(monitorSystem, 1500);
setInterval(updateRedundancy, 6000);
setInterval(updateUptime, 1000);
// Auto-inject errors occasionally to demonstrate self-healing
setInterval(() => {
if (systemActive && Math.random() > 0.95 && activeErrors.length === 0) {
injectError();
}
}, 10000);
</script>
</body>
</html>