Kuberwastaken

Kuberwastaken / litmus

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Run a Parallel Autonomous ML Research Organization on your OpenClaw instance.

litmus.kuber.studio agentic-ai autoresearch litmus machine-learning ml-research
13
1
100% credibility
Found Mar 31, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

Litmus turns a GPU-equipped computer into an autonomous machine learning research lab using multiple AI subagents that run parallel experiments, share knowledge via a skills library, and deliver daily progress reports through an OpenClaw AI assistant.

How It Works

1
🔍 Discover Litmus

You hear about Litmus, a way to turn your always-on computer into a smart lab that explores machine learning ideas on its own.

2
💬 Chat to install

Simply tell your AI assistant to add Litmus, and it checks your setup, suggests a schedule, and gets everything ready in one easy conversation.

3
⏰ Pick your rhythm

Choose times for creative thinking, summarizing findings, and morning updates that fit your daily life, like night owl or early bird.

4
🌙 Let it work overnight

Your computer quietly runs a team of smart helpers experimenting with new ideas, sharing discoveries, and building a library of proven techniques while you sleep.

5
📧 Wake to results

Every morning, get a friendly summary in your chat with the latest breakthroughs, best experiments, and plans for the day ahead.

6
📊 Check and tweak

Peek at leaderboards, agent progress, or nudge the team with new directions, all through simple chats or quick status views.

🚀 ML research on autopilot

Your machine becomes a self-running lab, delivering ongoing machine learning insights without you lifting a finger.

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AI-Generated Review

What is litmus?

Litmus turns your always-on NVIDIA GPU machine into an autonomous ML research lab via an OpenClaw skill, spawning 2-8 parallel subagents that run experiments overnight on separate git branches. A Director steers them every two hours, a Synthesizer distills findings into a reusable skills library at dawn, and you get a chat briefing by morning—like a litmus test meaning quick validation of research ideas. Setup happens conversationally by asking your OpenClaw agent, with bash scripts for status checks and leaderboards.

Why is it gaining traction?

It beats Karpathy's autoresearch with parallel workers, full git experiment history for cherry-picking, cross-agent learning via shared notes and anomalies, and anti-stagnation resets. Users notice the circadian flow: arXiv-fueled creative mode at 3AM, JSON-tracked attempts, and easy monitoring like "bash results.sh --top 10". For litmus automation fans, it's a step beyond running github actions locally, adding director oversight without external tools.

Who should use this?

ML researchers or indie hackers with Linux/macOS, CUDA GPUs, and an OpenClaw instance optimizing nanoGPT architectures or optimizers overnight. Perfect for those tired of manual hyperparameter grinds, wanting litmus test deutsch-style reliability checks on idle hardware. Avoid if you're not in OpenClaw or prefer Kubernetes litmus helm charts for chaos experiments.

Verdict

Early alpha with 13 stars and 1.0% credibility score—docs shine but expect tweaks for production. Worth a spin on compatible setups for hands-free ML gains; monitor via git log for proof.

(187 words)

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