Bringing Data Transformations Near-Memory for Low-Latency Analytics in HTAP Environments
arxiv.org·1d
ClickPy at 2 Trillion rows: Scaling ingestion and fixing the past
clickhouse.com·1d
Streamlining CUB with a Single-Call API
developer.nvidia.com·13h
Python Code Optimization Tips
denvaar.dev·1d
meta-pytorch/segment-anything-fast: A batched offline inference oriented version of segment-anything
github.com·1h
TigerBeetle vs PostgreSQL Performance: Benchmark Setup, Local Tests
softwaremill.com·1d
Arctic Wolf’s Liquid Clustering Architecture Tuned for Petabyte Scale
databricks.com·16h
Co-optimization Approaches For Reliable and Efficient AI Acceleration (Peking University et al.)
semiengineering.com·17h
Cline now speaks Jupyter: AI-assisted workflows for data scientists
cline.ghost.io·2h
Using Local LLMs to Discover High-Performance Algorithms
towardsdatascience.com·2d
Gated DeltaNet: The “Surgical Eraser” Solving Linear Attention’s Memory Problem
pub.towardsai.net·1d
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