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See

searchable JSON compression (offline 10-min demo)

Details

External ID
47104161
Source
HN
Company
—
Product
See
Website domain
gitlab.com
Launched
Feb. 21, 2026
Cohort
—
Upvotes
5
Upvotes percentile
0.10512129380053908
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Description

Hi HN, I’m building SEE (Semantic Entropy Encoding): a searchable compression format for JSON/NDJSON. Goal: reduce the “data tax” (storage/egress) and “CPU tax” (decompress/parse) by keeping JSON searchable while compressed, with page-level random access.I just published a proof-first evaluation release:Offline DEMO ZIP (~10 min): prints compression ratios + skip rates + lookup latency (p50/p95/p99)DD pack: audit/repro evidence (decode mismatch=0, extended mismatch=0, audit PASS)Latest release: https://gitlab.com/kodomonocch1/see_proto/-/releasesDirect DEMO ZIP: https://gitlab.com/api/v4/projects/79686944/packages/generic...OnePager is included in the release assets.I’d love feedback on:what workloads you’d try this on, andwhat integration path would make this compelling vs Zstd + external indexing.

Enrichment

Theme
niche developer utilities and toolchains
Vertical
Horizontal
Function
Dev tools
Audience
Developer
AI stance
Not AI
Project type
Hobby / open-source project
Normalized one-liner
compress and search json offline
Manually corrected
False

Could you build this?

No Designing a novel binary format with page-level random access and semantic entropy compression for JSON is advanced algorithmic research and data structure engineering.

What it would actually take: Implementing this requires expertise in information theory, binary codec design, and low-level C/Rust systems programming. The core format demands columnar or structural JSON decomposition, schema inference, custom dictionary/entropy coding (like ANS or zstd variants), and indexed block chunking allowing byte-offset jump tables and partial evaluation without full decompressions.

Discussion

No comments on this launch.

Competitors

Other products that read as similar to this one — 140 launches clear the similarity bar, closest 8 shown.

Attention rank: #131 of 141 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 115 days after the earliest competitor.

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