Sourcing data from disk can outperform memory caching due to stagnant memory access latencies and rapidly improving disk bandwidth. Through benchmarking experiments, the author demonstrates how optimized coding techniques can enhance performance, revealing that traditional assumptions about memory speed need reevaluation in the context of modern hardware capabilities.

The article discusses advancements in memory technology for AI models, emphasizing the importance of efficient memory utilization to enhance performance and scalability. It highlights recent innovations that allow models to retain and access information more effectively, potentially transforming how AI systems operate and learn.

The article discusses how memory maps (mmap) can significantly enhance file access performance in Go applications, achieving up to 25 times faster access compared to traditional methods. It explains the mechanics of memory mapping, the performance benefits it provides for read operations, and the limitations regarding write operations. The author also shares insights from implementing mmap in real-world applications, highlighting its effectiveness in improving performance.