To address the limitations of LLM context windows and the approximate nature of vector databases, this article details the implementation of Sparse Distributed Memory (SDM), based on Pentti Kanerva's 1988 model. Unlike standard retrieval methods, SDM functions as a content-addressable associative memory that can reconstruct patterns from noisy or partial cues. The implementation utilizes a large binary address space (e.g., 1000 bits) where a limited number of "hard locations" are randomly allocated. Data is stored by activating a neighborhood of these locations within a specific Hamming distance radius; writing accumulates patterns across these neighbors, while reading averages the activated contents to reconstruct the original signal. A Python implementation demonstrates this using bitwise operations, featuring a "winner-take-all" decoding mechanism. When integrated into a "hippocampus" architecture with memory replay and consolidation, the system showed significant performance gains. In local benchmarks, the approach achieved an 83.3% semantic judge score on LongMemEval, outperforming GPT-4o without memory (60–64%). Furthermore, the method demonstrated high efficiency, achieving a 95.5% compression rate on tool outputs (reducing 5008 B to 223 B) and outperforming brute-force truncation in a 16KB budget test by 8.3 percentage points.