The author is taking a break from developing Hierarchical Navigable Small Worlds (HNSWs) in Redis to reflect on the insights gained over the past year. They emphasize that while HNSWs are effective data structures for vector similarity searches, they are not the ultimate solution. The original research paper lacks certain aspects, such as the ability to delete entries properly, which the author has addressed in their modifications. They suggest that further exploration of HNSWs could yield valuable refinements, particularly regarding the necessity of hierarchical layers. The author hints at a middle ground, proposing that a simpler structure might perform comparably. Memory efficiency is a major concern with HNSWs, which require significant space due to pointers and floating-point vectors. The author notes that compression techniques for pointers can help, but they haven't implemented this yet, prioritizing speed in Redis. They highlight that using 8-bit quantization can drastically reduce the size of vectors and still maintain recall accuracy in real-world applications. This method involves scaling components to fit within a narrower range, which allows for efficient computation without sacrificing performance. On the performance side, the author discusses the advantages of threading, especially since HNSWs can be slow and are often read-heavy. Their implementation allows multiple threads to handle read requests efficiently while ensuring data integrity during writes. The author expresses a preference for single-threaded systems but acknowledges that HNSWs benefit from a threaded approach. They’ve built this implementation from scratch, indicating a hands-on, customized solution tailored to the specific challenges posed by HNSWs in a high-performance environment like Redis.