SplatHash is a polyglot image perceptual hashing library that compresses any image into exactly 16 bytes and reconstructs a 32x32 preview. The algorithm decomposes images into a background color and six Gaussian blobs (Splats) using Oklab color space, finds optimal Splat positions via sequential matching pursuit, refines weights with ridge regression, and packs everything into 128 bits. Implementations exist in Go (reference), TypeScript, and Python, all producing bit-identical output for cross-language parity. The system is designed for use as image placeholders during loading, prioritizing decode speed over encode speed.
DistillKit is a production-ready toolkit for knowledge distillation of large language models, supporting both online and offline workflows. It features advanced logit compression techniques (polynomial approximation + quantization + bit-packing) that enable efficient offline distillation at scale. The toolkit provides composable loss functions (KL, JSD, TVD, ranking losses, hidden state alignment) and integrates with Hugging Face Transformers, TRL, and Accelerate. It powers Arcee AI's distilled model releases and includes CLI tools for easy experimentation.