ctrlb-hq

ctrlb-hq / ctrlb-decompose

Public

LLM-ready reasoning surface over logs

19
2
100% credibility
Found Apr 10, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

A tool that analyzes log files to group similar messages into patterns, extracts key values with types and statistics, detects anomalies, and finds correlations.

How It Works

1
🔍 Discover log helper

You hear about a handy tool that turns messy server logs into simple patterns and insights.

2
💻 Get it ready

Visit the page to try it right in your browser or easily add it to your computer with one simple step.

3
Pick your way
🌐
Browser quick start

Paste logs directly online, no setup needed.

⚙️
Local install

Follow easy guide to have it on your machine anytime.

4
📤 Feed your logs

Upload or pipe in your log file and hit go – it handles millions of lines fast.

5
⚙️ Choose view

Pick pretty colors for screen, compact for AI chat, or data format for apps.

📊 Spot the story

See handful of patterns with numbers, odd spots flagged, and links between issues – now you know what's happening!

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Star Growth

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AI-Generated Review

What is ctrlb-decompose?

ctrlb-decompose is a Rust CLI tool that compresses raw log lines into structural patterns with statistics, anomalies, and correlations, turning millions of noisy entries into a handful of insights. It creates an LLM-ready reasoning surface over logs, extracting typed variables like IPs, durations, and enums with p50/p99 quantiles, top-k values, and flags for spikes or error cascades. Pipe stdin to get human, JSON, or compact LLM-optimized output, or use it as a library or WASM in the browser.

Why is it gaining traction?

Its LLM-ready GitHub repo output is token-efficient markdown perfect for AI analysis, while the single-pass streaming keeps memory low even on TB logs. Rust speed and formats like `--llm app.log` make it dead simple for quick insights, outperforming manual grep or heavy tools like ELK for ad-hoc decomposition. Anomaly detection and correlations surface issues like bimodal latencies without extra config.

Who should use this?

DevOps folks triaging server logs during outages, SREs spotting volume spikes in monitoring pipelines, security teams parsing auth failures for patterns. Ideal for developers building LLM-powered log analyzers or needing fast, local preprocessing before feeding data to observability stacks.

Verdict

Worth trying for Rust devs wanting lightweight log decomposition—brew install and pipe away. With 19 stars and 1.0% credibility score, it's pre-production but has strong docs, WASM, and MIT license; test on real logs before committing.

(187 words)

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