type MeshyModel struct {
    ViewDiffusion    *MultiViewDiffusion
    SparseRecon      *SparseViewReconstructor
    SuperResolution  *DetailEnhancer
    TopologyOptimizer *MeshOptimizer
    MaterialGenerator *PBRMaterialGen
    RiggingSystem    *AutoRigger
    AnimationGen     *AnimationGenerator
}

type MultiViewDiffusion struct {
    TextEncoder     *TextEncoder
    ViewSampler     *ViewpointSampler
    UNet            *DiffusionUNet3D
    ViewAttention   *CrossViewAttention
}

func (m *MultiViewDiffusion) GenerateViews(
    prompt string, numViews int, imageRef []float32,
) [][][]float32 {
    textEmb := m.TextEncoder.Encode(prompt)
    viewPoints := m.ViewSampler.Sample(numViews)
    
    views := make([][][]float32, numViews)
    results := make(chan viewResult, numViews)
    
    for i, vp := range viewPoints {
        go func(idx int, viewpoint Viewpoint) {
            condition := m.encodeCondition(textEmb, viewpoint, imageRef)
            view := m.UNet.Sample(condition, 50, 7.5, idx, m.ViewAttention)
            results <- viewResult{idx: idx, view: view}
        }(i, vp)
    }
    
    for i := 0; i < numViews; i++ {
        r := <-results
        views[r.idx] = r.view
    }
    
    return m.ViewAttention.RefineConsistency(views)
}

class SparseViewReconstructor(nn.Module):
    def __init__(self, voxel_resolution=128, num_views=6):
        super().__init__()
        self.voxel_res = voxel_resolution
        
        self.image_encoder = nn.Sequential(
            nn.Conv2d(3, 64, 7, stride=2, padding=3), nn.BatchNorm2d(64), nn.ReLU(),
            nn.Conv2d(64, 128, 3, stride=2, padding=1), nn.BatchNorm2d(128), nn.ReLU(),
            nn.Conv2d(128, 256, 3, stride=2, padding=1), nn.BatchNorm2d(256), nn.ReLU(),
            nn.AdaptiveAvgPool2d((8, 8)),
        )
        
        self.voxel_transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=256, nhead=8, batch_first=True),
            num_layers=12,
        )
        
        self.voxel_decoder = nn.Sequential(
            nn.ConvTranspose3d(256, 128, 4, 2, 1), nn.BatchNorm3d(128), nn.ReLU(),
            nn.ConvTranspose3d(128, 64, 4, 2, 1), nn.BatchNorm3d(64), nn.ReLU(),
            nn.ConvTranspose3d(64, 32, 4, 2, 1), nn.BatchNorm3d(32), nn.ReLU(),
            nn.Conv3d(32, 1, 3, padding=1), nn.Sigmoid(),
        )
    
    def forward(self, multi_view_images, view_params):
        B, V, C, H, W = multi_view_images.shape
        view_features = []
        
        for v in range(V):
            img = multi_view_images[:, v]
            vp = view_params[:, v]
            img_feat = self.image_encoder(img).flatten(2).permute(0, 2, 1)
            view_feat = self._view_encoder(vp).unsqueeze(1)
            view_features.append(img_feat + view_feat)
        
        all_features = torch.stack(view_features, dim=1).mean(dim=1)
        encoded = self.voxel_transformer(all_features)
        
        grid_size = 16
        voxel_feat = encoded.reshape(B, grid_size, grid_size, grid_size, -1)
        voxel_feat = voxel_feat.permute(0, 4, 1, 2, 3)
        voxels = self.voxel_decoder(voxel_feat)
        
        return voxels  # [B, 1, 128, 128, 128]

type MeshyAgent struct {
    ConceptGenerator  *ConceptGenerator
    BatchRenderer     *BatchRenderer
    ModelGenerator    *MeshyModel
    Editor            *MeshEditor
    FormatConverter   *FormatConverter
    State             *StateManager
}

func (a *MeshyAgent) ProcessMessage(input string) string {
    intent := a.parseIntent(input)
    switch intent.Action {
    case "concept":
        concepts := a.ConceptGenerator.Generate(intent.Description, 4)
        return formatConceptResponse(concepts)
    case "generate":
        meshes := a.ModelGenerator.Generate(
            prompt=intent.Description, numViews=6,
        )
        return formatMeshResponse(meshes)
    case "export":
        formats := a.FormatConverter.Convert(
            meshID=intent.MeshID,
            targetFormats=[]string{".obj", ".fbx", ".glb", ".stl"},
        )
        return formatExportResponse(formats)
    }
    return "Please describe what you'd like to create."
}

class DataFlywheel:
    def simulate(self, months=12):
        users = 12_000_000
        models = 100_000_000
        quality = 0.6
        
        for month in range(months):
            gen_per_user = 8.0 * quality
            new_models = users * gen_per_user
            feedback_quality = 0.3 + 0.5 * quality
            
            data_multiplier = np.log10(models + new_models) / 8
            quality = min(0.95, 0.6 + 0.05 * data_multiplier + 0.02 * feedback_quality)
            
            growth_rate = 0.05 * quality
            users += users * growth_rate
            models += new_models
            
            print(f"Month {month+1}: {users/1e6:.1f}M users, "
                  f"{models/1e6:.0f}M models, quality={quality:.3f}")