Developer Tools●●●Banger
I used LLMs to build a compression tool that beats xz on x86_64 ELFs
LLM-guided structural preprocessing beats xz -9e on 103/103 Alpine binaries tested.
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mohsen1
433mo ago
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3 starsPython
UMC – Lossless compression that beats lzma by 7-46% on numeric data
by gunnerlevi·Mar 2, 2026·3 points·0 comments
AI Analysis
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Search compressed data without decompressing—beats lzma on numeric arrays.
Strengths
- •Genuine two-in-one value: compression *and* searchable index eliminates decompress-to-search bottleneck.
- •Multiple modes (lossless, near-lossless, quantized) with clear tradeoffs and provable optimality option.
- •Lightweight install with numpy-only dependency; full neural search optional—respects user's build simplicity.
Weaknesses
- •Specialized to numeric/structured data; general-purpose compressors still handle arbitrary binaries better.
- •Limited ecosystem evidence; no public benchmarks against specialized time-series databases (InfluxDB, Timescale).
Category
Target Audience
Data engineers, researchers, anyone managing large numeric datasets (time series, sensor data, images).
Similar To
zstd · lzma · ClickHouse (columnar + compression)
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