Escaped happy hour: Training my neural net to scan a barcode

A developer builds a neural network framework from scratch to scan barcodes on mobile devices. The project handles challenging scenarios like worn paint, perspective warping, reflective surfaces, and blurry images. The implementation includes training target switching between bounding box detection and barcode scanning, custom data generation, loss function configuration, and thread management for better error handling. The developer discusses language preferences, comparing Zig favorably to C++ and Rust for low-level programming, and plans to add model checkpointing to save training progress.