Welcome to the show, where we discuss DeepSeek-OCR and its investigation into using optical 2D mapping for contexts compression, addressing the computational challenges of quadratic scaling faced by Large Language Models. We explore the DeepEncoder, the core engine designed to achieve high compression ratios, delivering near-lossless OCR precision (approximately 97%) even at a 10× token reduction. This groundbreaking work demonstrates strong practical value, achieving state-of-the-art document parsing performance on OmniDocBench while using the fewest vision tokens, offering a promising direction for future memory systems.