What Can We Do When Memory Becomes the New Bottleneck in Data Engineering?

How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes

Architecting cost-effective infrastructure by navigating the latency and storage trade-offs of HNSW, SPANN, and DiskANN The post How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes appeared first on Towards Data Science .

MEMONDEMAND: A Memory Management System for Large-Scale Enterprise Data

arXiv:2608.22141v1 Announce Type: new Abstract: Enterprise repositories are large, heteroge- neous, and continuously updated, making re- trieval difficult when efficient access, source- faithful evid...

The Problem with pandas Isn’t Performance. It’s Cognitive Overhead.

Faster dataframe engines are nice, but they don't reduce the amount of syntax an analyst has to hold in their head. The post The Problem with pandas Isn’t Performance.

Make Memory work for you.

Enhancing Software Engineering Through Closed-Loop Memory Optimization

arXiv:2606. 05646v1 Announce Type: cross Abstract: Large language models (LLMs) have enabled powerful software engineering (SE) agents capable of navigating complex codebases and resolving real-world issues.

Mixed-input matrix multiplication performance optimizations

Posted by Manish Gupta, Staff Software Engineer, Google Research AI-driven technologies are weaving themselves into the fabric of our daily routines, with the potential to enhance our access to knowledge and boost our overall productivity. The backbone of these applications lies in large language models (LLMs).