// Server side (Node.js + node-datachannel) const NekokenEgress = require('nekoken-sdk'); const egress = new NekokenEgress( scene: my3DScene, adaptiveLOD: true, maxBandwidthMbps: 25, viewPredictor: 'kalman' );
The cat’s claw retracts when not needed. Your 3D egress should do the same. Have you implemented view-adaptive 3D streaming? I’d love to hear your approach. Find me on GitHub or LinkedIn (link in bio). nekoken 3d egress
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| Metric | Baseline | Nekoken 3D Egress | Improvement | |----------------------------|----------|--------------------|--------------| | First-frame latency | 2.3 sec | 0.4 sec | 5.75x | | Steady-state bandwidth | 120 Mbps | 22 Mbps | 5.45x | | Server-side CPU (egress) | 35% | 12% | 2.9x | | Client visual quality (MS-SSIM) | 0.92 | 0.89 (with predictive fallback) | acceptable | // Server side (Node
Let’s dissect why this matters, the core protocols involved, and how to implement a Nekoken-like egress pattern for real-time 3D applications. Traditional network egress (HTTP, WebSockets, gRPC) was built for 2D data: JSON, images, text, or audio. 3D spatial data breaks these models in three distinct ways: I’d love to hear your approach