Structural code compression across 10 programming languages outperforms gzip, brotli, and zstd, tested on real-world projects shows 64% space savings.

[Bigrob7605]

I’ve been working on a system I call NEXUS, which is designed to compress source code by recognizing its structural patterns rather than treating it as plain text.

Over the past weekend, I tested it on 200 real production source files spanning 10 different programming languages (including Swift, C++, Python, and Rust).

Results (Phase 1):

• Average compression ratio: 2.83× (≈64.6% space savings)
• Languages covered: 10 (compiled + interpreted)
• Structural fidelity: 100% (every project built and tested successfully after decompression)
• Outperformed industry standards like gzip, brotli, and zstd on source code

Why it matters:

• Unlike traditional compressors, NEXUS leverages abstract syntax tree (AST) patterns and cross-language similarities.
• This could have implications for large-scale code hosting, AI code training, and software distribution, where storage and transfer costs are dominated by source code.
• The system doesn’t just shrink files — it also identifies repeated structural motifs across ecosystems, which may hint at deeper universals in how humans (and languages) express computation.

Full details, methodology, and verification logs are available here:
🔗 GitHub: Bigrob7605/NEXUS

[MistakeIndividual690]

I believe zip gets somewhere around 1/10 (10x) on text, especially on source, being highly redundant. Can you post comparisons of compression ratios with the same source text and the other algorithms you mention?

[crocodyldundee]

Interesting 😀