🚀 Enhancement Roadmap: cc-dashboard

// New component: TrajectoryGraph.tsx
<svg width="300" height="150">
  {/* Plot tension over time */}
  <path d={tensionPath} stroke="red" />
  {/* Plot energy over time */}
  <path d={energyPath} stroke="blue" />
  {/* Current time marker */}
  <line x1={now} x2={now} stroke="white" />
</svg>

// In ConductorEngine.ts
private sectionStabilityCounter: number = 0
private requiredStableFrames: number = 10 // ~200ms at 50 Hz

if (predictedSection !== currentSection) {
  this.sectionStabilityCounter = 0
} else {
  this.sectionStabilityCounter++
}

if (this.sectionStabilityCounter >= this.requiredStableFrames) {
  // Transition is stable, commit it
  commitTransition(predictedSection)
}

// Use confidence from trajectory
const avgConfidence = trajectory.future
  .reduce((sum, f) => sum + f.confidence, 0) / trajectory.future.length

if (avgConfidence > 0.7 && shouldTransition) {
  transition(newSection)
} else {
  console.log('🔮 Low confidence, staying in current section')
}

// Current: kick_gain, bass_cutoff, hihat_density, pad_reverb
// Add:
- filter_resonance (tension → 0-1)
- delay_feedback (curvature → 0-0.9)
- reverb_size (grounding → 0-1, inverted)
- compression_ratio (velocity → 1:1 to 8:1)
- stereo_width (left_energy - right_energy → 0-1)
- pitch_shift (head_pitch → -12 to +12 semitones)
- distortion (excitement → 0-0.5)
- swing_amount (watch rhythm_intensity → 0-50%)

// Add to page.tsx keyboard handler
case '1': setSection('intro'); break
case '2': setSection('groove'); break
case '3': setSection('build'); break
case '4': setSection('climax'); break
case '5': setSection('breakdown'); break
case '6': setSection('outro'); break
case '0': enableAutoMode(); break // Return to automatic

// In conductorStore.ts
sectionHistory: Array<{
  section: SectionType
  duration: number
  startTime: number
  endTime: number
  avgTension: number
  avgEnergy: number
}>

// Analytics
const avgBuildDuration = sectionHistory
  .filter(s => s.section === 'build')
  .reduce((sum, s) => sum + s.duration, 0) / count

class PhysicsTrajectoryPredictor {
  predict(current: LatentState, velocity: LatentState) {
    // Use current + velocity for better prediction
    // Add momentum: future = current + velocity * dt - drag * velocity
    // Add noise model for uncertainty
  }
}

// Integrate with existing gesture system
if (gesture.type === 'shake' && gesture.confidence > 0.8) {
  forceTransition('climax')
  scheduleEvent('crash', 1.0)
}

if (gesture.type === 'swipe_down') {
  forceTransition('breakdown')
}

interface SessionRecording {
  id: string
  timestamp: number
  duration: number
  frames: Array<{
    time: number
    latentState: LatentState
    section: SectionType
    trajectory: LatentTrajectory
    edits: PatternEdit[]
  }>
  metadata: {
    devices: string[]
    avgTension: number
    sectionCounts: Record<SectionType, number>
  }
}

// Record
const recorder = new SessionRecorder()
recorder.start()
// ... during session, record each frame
recorder.addFrame(latentState, section, trajectory, edits)
recorder.stop()
recorder.save('session_001.json')

// Playback
const player = new SessionPlayer('session_001.json')
player.play() // Replays entire session

interface DeviceRole {
  device_id: string
  role: 'lead' | 'rhythm' | 'bass' | 'harmony' | 'conductor'
  confidence: number
  assignedBy: 'auto' | 'manual'
}

// Auto-detect based on movement patterns
if (device.avgEnergy > 0.7 && device.avgCurvature > 0.5) {
  assignRole(device, 'lead') // Expressive, high energy
} else if (device.rhythmicStability > 0.8) {
  assignRole(device, 'rhythm') // Steady, consistent
} else if (device.avgFrequency < 2.0) {
  assignRole(device, 'bass') // Slow, grounded
}

class AdaptiveThresholds {
  private userProfile: {
    tensionRange: [min, max]
    velocityRange: [min, max]
    preferredSectionDurations: Record<SectionType, number>
  }

  calibrate(sessions: SessionRecording[]) {
    // Analyze user's typical ranges
    // Adjust thresholds to match their style
    // E.g., energetic dancers → higher thresholds
    //       subtle dancers → lower thresholds
  }

  getThreshold(section: SectionType, metric: string): number {
    // Return personalized threshold
  }
}

const sectionMomentum: Record<SectionType, number> = {
  intro: 0.5,   // Easy to leave
  groove: 0.8,  // Stable, need strong signal to leave
  build: 0.6,   // Momentum carries into climax
  climax: 0.9,  // Hard to leave prematurely
  breakdown: 0.5, // Easier to transition
  outro: 0.7    // Deliberate ending
}

// Require stronger signal to overcome momentum
const requiredConfidence = baseLine + sectionMomentum[current] * 0.3
if (transitionConfidence > requiredConfidence) {
  transition(newSection)
}

LatentState + Trajectory
    ↓
Neural Network (trained on user sessions)
    ↓
PatternEdit predictions
    ↓
MotionStrudel

# Example PyTorch model
class PatternCoder(nn.Module):
  def __init__(self):
    self.encoder = nn.Sequential(
      nn.Linear(latent_dim + trajectory_dim, 256),
      nn.ReLU(),
      nn.Linear(256, 128),
      nn.ReLU()
    )

    self.section_head = nn.Linear(128, num_sections)
    self.param_head = nn.Linear(128, num_params)

  def forward(self, latent, trajectory):
    features = self.encoder(torch.cat([latent, trajectory]))
    section = self.section_head(features)
    params = self.param_head(features)
    return section, params

import * as ort from 'onnxruntime-web'

class NeuralSynthesizer {
  private session: ort.InferenceSession

  async init() {
    this.session = await ort.InferenceSession.create('ddsp_model.onnx')
  }

  async synthesize(latent: Float32Array): Promise<Float32Array> {
    const input = new ort.Tensor('float32', latent, [1, latent.length])
    const output = await this.session.run({ input })
    return output.audio.data
  }
}

# In cc-brain crate
cargo build --target wasm32-unknown-unknown --release
wasm-bindgen target/wasm32-unknown-unknown/release/cc_brain.wasm --out-dir pkg

import init, { rehearse_trajectory } from '@/wasm/cc_brain'

await init()

const trajectory = rehearse_trajectory(
  current_latent,
  current_lexicon,
  current_section,
  dt,
  horizon,
  control_policy
)

interface EnsembleMember {
  id: string
  color: string
  latentState: LatentState
  trajectory: LatentTrajectory
  role: 'leader' | 'follower' | 'independent'
}

class EnsembleConductor {
  combineTensions(members: EnsembleMember[]): number {
    // Weighted average based on roles
    const leader = members.find(m => m.role === 'leader')
    const followers = members.filter(m => m.role === 'follower')

    return leader ? leader.latentState.tension * 0.6 +
                    avgTension(followers) * 0.4
                  : avgTension(members)
  }
}

# Vercel deployment
vercel --prod

# Environment variables
NEXT_PUBLIC_API_URL=https://cc-mcs-headless-274020562532.us-central1.run.app
NEXT_PUBLIC_ENABLE_ANALYTICS=true

class LatentDynamicsModel(nn.Module):
  def __init__(self):
    self.lstm = nn.LSTM(latent_dim, 256, num_layers=2)
    self.output = nn.Linear(256, latent_dim + uncertainty_dim)

  def forward(self, latent_sequence):
    # Predict next 100 frames
    predictions = []
    hidden = None
    for i in range(100):
      pred, hidden = self.lstm(latent_sequence[-1:], hidden)
      predictions.append(self.output(pred))
    return predictions

import { TopologicalAnalyzer } from '@/lib/cc-trajectory'

const analyzer = new TopologicalAnalyzer()
const analysis = analyzer.analyze(trajectoryBuffer)

if (analysis.hasAttractor && analysis.complexity > 0.7) {
  // Complex, repeating pattern detected
  transitionTo('hypnotic_groove')
}

if (analysis.bifurcationDetected) {
  // Sudden change in movement dynamics
  transitionTo('breakdown')
}

User A (NYC) ──┐
               ├──→ Cloud Conductor ──→ Synchronized Music
User B (LA)  ──┘