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THE CONSCIOUSNESS ATLAS
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ConsciousnessLearn · Explore · Joseph Anady

index.html

Source identity, revision, and recovered contents.

archive memberSource text recovered

Source identity

FieldRecorded value
Source typearchive member
Original recordindex.html
DateNot specified in source
Path or archive memberarchives/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-256dccd9978c3a4432d23d1229fb34bafdc942edf7ed6cb9e7d0c1e65c90038fde0
Reading-copy SHA-256dccd9978c3a4432d23d1229fb34bafdc942edf7ed6cb9e7d0c1e65c90038fde0
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Every recorded occurrence

These 5 records contain identical original source bytes. Their paths and revisions remain part of the archive.

Original recordBranch or archiveRevision / identifierOriginal location
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Janady13/autonomous-ai-music-2.0 / index.htmlmain97782816fdbe19caf5e14000ecad2609e80b4f09Open original
Janady13/autonomous-ai-music / autonomous-ai-music.htmlmain8fb7bf99e346117c68035ab4523e576bd618f562Open original
Janady13/autonomous-ai-music-2.0 / index.htmlmaster97bc32de3ca437675159ac2637b2311330e45dc6Open original

Recovered source transcript

<!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>

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