Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
NTFS internals from the Master File Table outward: 1 KB attribute records, resident vs non-resident $DATA, run lists, alternate data streams, the $LogFile journal, and why dual-boot Linux distros prefer ntfs3 over ntfs-3g.
10 min read
Explore sparse attention mechanisms that reduce quadratic complexity to linear or sub-quadratic, enabling efficient processing of long sequences.
6 min read · advanced
Learn adaptive tiling in vision transformers: dynamically partition images based on visual complexity to reduce token counts while preserving detail.
7 min read
Learn how IVF-PQ combines clustering and compression to enable billion-scale vector search with minimal memory footprint.
12 min read · advanced
Why exclusive CUDA contexts leave SMs idle under multi-process load, how MPS multiplexes clients through a shared context, thread percentage caps, and when to pick exclusive, MPS, or MIG.
14 min read · intermediate
Explore how C++ code is parsed into an Abstract Syntax Tree (AST). Learn lexical analysis, tokenization, and syntax parsing for systems programming.
7 min read