Machine Learning Fundamentals: dimensionality reduction
dev.to·12h·
Discuss: DEV
📐Linear Algebra
Caches and Abstractions
parallelprogrammer.substack.com·3h·
Discuss: Substack, r/programming
💨Cache Analysis
Parsing Protobuf Like Never Before
mcyoung.xyz·20h
🔧Binary Parsers
Python `apply()` vs. `apply_async()`: Which Should You Use?
thenewstack.io·10h
🌊Stream Processing
Computer Architecture: A quantitative approach
ikerexxe.github.io·2d
🖥️Hardware Architecture
maxsim-cpu: Maximising Maxsim Efficiency
mixedbread.com·2d·
Discuss: Hacker News
🌊Streaming Algorithms
A Wide Reduction Trick (2022)
words.filippo.io·41m·
Discuss: Lobsters, Hacker News
🌳Archive Merkle Trees
ZFS Summer Article Roundup: Smart Hardware Advice
klarasystems.com·10h
💿ZFS Internals
Binary Search in a 2D Matrix
mmhaskell.com·2d
📐Error Bounds
Performance Hacks for Brady’s Algorithm
nickdrozd.github.io·2d
🎞️Tape Combinatorics
How I Doubled My Lookup Performance with a Bitwise Trick
maltsev.space·1d
🔢Bitwise Algorithms
Nvidia's new consumer desktop PC chip reportedly delayed well into 2026
tomshardware.com·7h
🖥️Modern Terminals
Understanding Registers and Data Movement in x86-64 Assembly
blog.codingconfessions.com·15h
🔧RISC-V Assembler
How to Optimize Your Python Code Even If You’re a Beginner
kdnuggets.com·2d
🌊Streaming Algorithms
Revisiting k-Means: 3 Approaches to Make It Work Better
machinelearningmastery.com·13h
📊Vector Quantization
Turbocharged Storage: MinIO, KIOXIA, and AMD team up to take on AI
blog.min.io·4h
🧲RISC-V Preservation
Got buyer's remorse with your 8GB graphics card? Nvidia's AI texture compression promises huge benefits for GPUs with stingy amounts of memory
techradar.com·8h
🖥️Terminal Renaissance
Compressed data structures for Heegaard splittings
arxiv.org·23h
🕳️Persistent Homology
[R] Interesting paper on cost-aware prompt optimization (CAPO)
reddit.com·21h·
Discuss: r/MachineLearning
🏗️Compiler Archaeology
KD240 BIST - Platform/App Recreating with 2025.1 Vitis & HLS
hackster.io·2d
🖥️Modern CPU