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Text classification with Python 3.14's zstd module • Max Halford
maxhalford.github.io·3d·
Discuss: Lobsters, Hacker News
🗜️Archive Compression
CLIP-Map: Structured Matrix Mapping for Parameter-Efficient CLIP Compression
arxiv.org·3d
🗜️Archive Compression
Quantized Tensor Train Compression For Turbulent Flow Simulation: O(log N) Scaling with Reynolds-Independent Bond Dimension
zenodo.org·1h·
Discuss: Hacker News
⚡SIMD Optimization
HTML Dictionaries
dev.to·20h·
Discuss: DEV
⚡DataFusion
Author Correction: Random access and semantic search in DNA data storage enabled by Cas9 and machine-guided design
nature.com·1h
🗂️HDF5
Quantization-Aware Distillation
ternarysearch.blogspot.com·1d·
Discuss: Hacker News
📊Columnar Engines
Splitwise alternative that makes sense
divvy.club·1d·
Discuss: Hacker News
🧭Vector Databases
Font Rendering from First Principles
mccloskeybr.com·1d·
Discuss: Hacker News
💾Cache Optimization
Computing large Fibonacci numbers
johndcook.com·23h·
Discuss: Hacker News
🔢NumPy
gharasathi (घरासाठी) — A Privacy-First Household AI Running on a $200 Mini PC
amazon.com.au·1d·
Discuss: DEV
🧊Iceberg Tables
From Short Video Feedback to Scalable Content Systems
dev.to·1d·
Discuss: DEV
📇Indexing Strategies
Reverse Engineering the Prom for the SGI O2
mattst88.com·15h·
Discuss: Lobsters, Hacker News
🕵️Memory Forensics
End-to-End Compression for Tabular Foundation Models
arxiv.org·3d
📊Columnar Engines
Convert & Compress
frontendmasters.com·5d
🗜️Archive Compression
Lazy-pulling containers: 65x faster pulls, but 20x slower readiness
blog.zmalik.dev·19h·
Discuss: Hacker News
💾ZFS
Show HN: ZigZag – A Bubble Tea-Inspired TUI Framework for Zig
github.com·2d·
Discuss: Hacker News
🦀Rust Scientific
A spatial domain variable block size luma dependent chroma compression algorithm
bitsnbites.eu·5d·
Discuss: Hacker News
🗜️Archive Compression
Complex Heterodynes Explained
tomverbeure.github.io·2d·
Discuss: Hacker News
📡Signal Processing
Oatmeal - Constraint propagation for fun
eli.li·1d·
Discuss: Lobsters, Hacker News
🐻Polars
Fastfood: Approximate Kernel Expansions in Loglinear Time
paperium.net·1d·
Discuss: DEV
📊Approximate Computing

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