What I built (SEE — Semantic Entropy Encoding) A schema-aware, searchable compression codec for JSON that keeps exists/pos lookups fast while still compressing. Internals: structure-aware delta + dictionaries, a PageDir + mini-index to jump to relevant pages, and a tuned Bloom filter that skips ~99% of pages. AutoPage (131/262 KiB) balances seek vs throughput.

Benchmarks (apples-to-apples, FULL) - size ratio: str ≈ 0.168–0.170, combined ≈ 0.194–0.196 - Bloom density ≈ 0.30; skip: present ≈ 0.99, absent ≈ 0.992 - lookup (ms): present p50/p95/p99 ≈ 0.18/0.28/0.37; absent ≈ 1.16–1.88/1.36–2.11/1.58–2.41 Numbers are stable on a commodity desktop (i7-13700K/96GB/Windows).

Try it in 10 minutes (no build) 1) pip install see_proto 2) python samples/quick_demo.py It prints size ratios, Bloom density, skip %, and lookup p50/p95/p99 on a packaged sample.

Why not “just zstd”? We sometimes lose pure size vs zstd alone. The win is searchable compression: Bloom + PageDir avoids touching most pages, so selective probes pay less I/O/egress and finish faster. On large log scans this often wins on TCO even with similar raw ratios.

Link (README + quick demo + one-pager) https://github.com/kodomonocch1/see_proto