I've built a neuro-inspired language acquisition system that mimics how children learn language, implementing a tiny transformer architecture (4-layer, 256-dim) trained on child-directed speech from the CHILDES corpus. The system features a complete web application with Safari auto-launch functionality, offering real-time training monitoring, interactive model evaluation through cognitive tests like the Wug Test and word segmentation, and an API-driven interface for experimenting with biologically-plausible language learning. The web app automatically opens to comprehensive API documentation, enabling researchers to immediately start downloading corpora, training models, and evaluating performance using metrics that align more closely with actual child language development than traditional NLP benchmarks.

The next engineering steps include implementing the biological enhancement phase, which involves adding sparsity constraints through L1 regularization on activations, integrating spiking neural dynamics using frameworks such as NORSE or Lava, and developing hierarchical attention using dilated attention mechanisms. I also plan to expand the evaluation framework with more sophisticated cognitive metrics, introduce curiosity-driven learning with intrinsic rewards for perplexity reduction, and establish a production deployment pipeline featuring model versioning and A/B testing.

Looking further ahead, I intend to add multi-modal inputs (audio/visual) to better emulate the rich sensory environment children experience during language acquisition, and develop a real-time dialogue interface that demonstrates the system's interactive language learning capabilities.