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ojuschugh1
sqz
Rust2026–2026
ojuschugh1 / sqz

sqz

Context compression for AI coding agents: compresses tool output before it enters the model, dedups repeats to 13-token refs. Claude Code, Cursor, Codex, Kiro, Zed, any MCP client. Rust, zero LLM calls.

2Distinct people
4Mentions
0Explicit recommenders
678GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Context compression for AI coding agents: compresses tool output before it enters the model, dedups repeats to 13-token refs. Claude Code, Cursor, Codex, Kiro, Zed, any MCP client. Rust, zero LLM calls.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by 6-hour interval4 dated mentions

Observed mentions: 2026-04-16 12:01:52 UTC to 2026-04-23 02:37:21 UTC.

0122026-04-16 12:00:00 UTC to 2026-04-16 18:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsApr 16, 12:002026-04-16 18:00:00 UTC to 2026-04-17 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-17 00:00:00 UTC to 2026-04-17 06:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-17 06:00:00 UTC to 2026-04-17 12:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-17 12:00:00 UTC to 2026-04-17 18:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-17 18:00:00 UTC to 2026-04-18 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-18 00:00:00 UTC to 2026-04-18 06:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-18 06:00:00 UTC to 2026-04-18 12:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-18 12:00:00 UTC to 2026-04-18 18:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-18 18:00:00 UTC to 2026-04-19 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-19 00:00:00 UTC to 2026-04-19 06:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-19 06:00:00 UTC to 2026-04-19 12:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-19 12:00:00 UTC to 2026-04-19 18:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-19 18:00:00 UTC to 2026-04-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsApr 19, 18:002026-04-20 00:00:00 UTC to 2026-04-20 06:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-20 06:00:00 UTC to 2026-04-20 12:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-20 12:00:00 UTC to 2026-04-20 18:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-04-20 18:00:00 UTC to 2026-04-21 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-21 00:00:00 UTC to 2026-04-21 06:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-21 06:00:00 UTC to 2026-04-21 12:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-21 12:00:00 UTC to 2026-04-21 18:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-21 18:00:00 UTC to 2026-04-22 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-22 00:00:00 UTC to 2026-04-22 06:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-22 06:00:00 UTC to 2026-04-22 12:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-22 12:00:00 UTC to 2026-04-22 18:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-22 18:00:00 UTC to 2026-04-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-23 00:00:00 UTC to 2026-04-23 06:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsApr 23, 00:00

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Apr 16, 12:002026-04-16 12:00:00 UTC2026-04-16 18:00:00 UTC101
Apr 16, 18:002026-04-16 18:00:00 UTC2026-04-17 00:00:00 UTC000
Apr 17, 00:002026-04-17 00:00:00 UTC2026-04-17 06:00:00 UTC000
Apr 17, 06:002026-04-17 06:00:00 UTC2026-04-17 12:00:00 UTC000
Apr 17, 12:002026-04-17 12:00:00 UTC2026-04-17 18:00:00 UTC000
Apr 17, 18:002026-04-17 18:00:00 UTC2026-04-18 00:00:00 UTC000
Apr 18, 00:002026-04-18 00:00:00 UTC2026-04-18 06:00:00 UTC000
Apr 18, 06:002026-04-18 06:00:00 UTC2026-04-18 12:00:00 UTC000
Apr 18, 12:002026-04-18 12:00:00 UTC2026-04-18 18:00:00 UTC000
Apr 18, 18:002026-04-18 18:00:00 UTC2026-04-19 00:00:00 UTC000
Apr 19, 00:002026-04-19 00:00:00 UTC2026-04-19 06:00:00 UTC000
Apr 19, 06:002026-04-19 06:00:00 UTC2026-04-19 12:00:00 UTC000
Apr 19, 12:002026-04-19 12:00:00 UTC2026-04-19 18:00:00 UTC000
Apr 19, 18:002026-04-19 18:00:00 UTC2026-04-20 00:00:00 UTC000
Apr 20, 00:002026-04-20 00:00:00 UTC2026-04-20 06:00:00 UTC000
Apr 20, 06:002026-04-20 06:00:00 UTC2026-04-20 12:00:00 UTC000
Apr 20, 12:002026-04-20 12:00:00 UTC2026-04-20 18:00:00 UTC101
Apr 20, 18:002026-04-20 18:00:00 UTC2026-04-21 00:00:00 UTC000
Apr 21, 00:002026-04-21 00:00:00 UTC2026-04-21 06:00:00 UTC000
Apr 21, 06:002026-04-21 06:00:00 UTC2026-04-21 12:00:00 UTC000
Apr 21, 12:002026-04-21 12:00:00 UTC2026-04-21 18:00:00 UTC000
Apr 21, 18:002026-04-21 18:00:00 UTC2026-04-22 00:00:00 UTC000
Apr 22, 00:002026-04-22 00:00:00 UTC2026-04-22 06:00:00 UTC000
Apr 22, 06:002026-04-22 06:00:00 UTC2026-04-22 12:00:00 UTC000
Apr 22, 12:002026-04-22 12:00:00 UTC2026-04-22 18:00:00 UTC000
Apr 22, 18:002026-04-22 18:00:00 UTC2026-04-23 00:00:00 UTC000
Apr 23, 00:002026-04-23 00:00:00 UTC2026-04-23 06:00:00 UTC211

What Hacker News says

4 sources
I got tired of watching Codex re-read the same files over and over. A 2,000-token file read 5 times = 10,000 tokens gone. So I built sqz. The key insight: most token waste isn't from verbose content - it's from repetition. sqz keeps a SHA-256 content cache. First read compresses normally. Every subsequent read of the same file returns a 13-token inline reference instead of the full content. The LLM still understands it. Real numbers from my sessions: File read 5x: 10,000 tokens → 1,400 tokens (86% saved) JSON API response with nulls: 56% reduction (strips nulls, TOON-encodes) Repeated log lines: 58% reduction (condenses duplicates) Stack traces: 0% reduction (intentionally — error content is sacred) That last point is the whole philosophy. Aggressive compression can save more tokens on paper, but if it strips context from your error messa
Read the rest
ges or drops lines from your diffs, the LLM gives you worse answers and you end up spending more tokens fixing the mistakes. sqz compresses what's safe to compress and leaves critical content untouched. You save tokens without sacrificing result quality. It works across 4 surfaces: Shell hook (auto-compresses CLI output) MCP server (compiled Rust, not Node) Browser extension (Chrome + Firefox (currently in approval phase)— works on ChatGPT, Claude, Gemini, Grok, Perplexity) IDE plugins (JetBrains, VS Code) Single Rust binary. Zero telemetry. 549 tests + 57 property-based correctness proofs. cargo install sqz-cli sqz init Track your savings: sqz gain # ASCII chart of daily token savings sqz stats # cumulative report Where sqz shines Scenario Savings Why Repeated file reads (5x) 86% Dedup cache: 13-token ref after first read JSON API responses with nulls 7–56% Strip nulls + TOON encoding (varies by null density) Repeated log lines 58% Condense stage collapses duplicates Large JSON arrays 77% Array sampling + collapseToken Savingssqz saves tokens in two ways: compression (removing noise from content) and deduplication (replacing repeated reads with 13-token references). The dedup cache is where the biggest savings happen in real sessions.Where sqz shinesScenario Savings WhyRepeated file reads (5x) 86% Dedup cache: 13-token ref after first readJSON API responses with nulls 7–56% Strip nulls + TOON encoding (varies by null density)Repeated log lines 58% Condense stage collapses duplicatesLarge JSON arrays 77% Array sampling + collapseWhere sqz shinesScenarioSavingsWhyRepeated file reads (5x)86%Dedup cache: 13-token ref after first readJSON API responses with nulls7–56%Strip nulls + TOON encoding (varies by null density)Repeated log lines58%Condense stage collapses duplicatesLarge JSON arrays77%Array sampling + collapseToken Savingssqz saves tokens in two ways: compression (removing noise from content) and deduplication (replacing repeated reads with 13-token references). The dedup cache is where the biggest savings happen in real sessions.Where sqz shinesScenario Savings WhyRepeated file reads (5x) 86% Dedup cache: 13-token ref after first readJSON API responses with nulls 7–56% Strip nulls + TOON encoding (varies by null density)Repeated log lines 58% Condense stage collapses duplicatesLarge JSON arrays 77% Array sampling + collapse GitHub: https://github.com/ojuschugh1/sqz Happy to answer questions about the architecture or benchmarks. Hope this tool will Sqz your tokens and save your credits. f you try it, a helps with discoverability — and bug reports are extra welcome since this is v0.2 so rough edges exist. It is available as IDE Extension , CLI via npm, cargo, pip , soon it will be able as web extension to use with chatgpt, claude, gemmini websites as well.
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