IGLnet Overview Video v1.0.2 (November 2026)
Explore IGLnet in this comprehensive technical walkthrough of the complete IGL network training environment.
Designed for computer scientists, AI engineers, researchers, and technically curious viewers, the presentation examines IGLnet from its exact-integer numerical foundations through model training, analysis, acceleration, diagnostics, and deployment.
The video covers dataset and glyph generation, Scale P and deep-network resolution, model topology, candidate search, node-selection strategies, loss functions, experiment monitoring, network and node inspection, checkpoints, benchmarking, and portable C export. Particular attention is given to IGLnet’s expanding Training Advisor, which continuously analyzes model configuration, search behavior, candidate quality, network depth, computational workload, and GPU status to identify potential problems and recommend corrective actions.
The walkthrough also provides an in-depth examination of IGLnet’s CPU and WebGPU architecture, including scalar CPU execution, parallel workers, Portable I32 implementations, GPU-resident Cache v3, exact CPU/GPU verification, runtime shadow checking, resource preflight, telemetry, recovery, and performance tuning.
Throughout the presentation, Justin, Richard, Patrick, Rachel, and Sarah examine the software from complementary technical perspectives, asking questions of one another and connecting individual controls to the broader principles of reproducibility, exact arithmetic, interpretable search, and efficient deployment.
IGLnet is more than a model trainer. It is an experimental laboratory for understanding how exact integer AI models are constructed, trained, verified, diagnosed, and prepared for deployment.