rmarji

rmarji / autoresearch-openclaw

Public

Karpathy's autoresearch loop for non-ML domains: outreach, prediction markets, prompts

16
1
100% credibility
Found Mar 26, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Shell
AI Summary

A set of automation guides inspired by research loops to iteratively enhance outreach templates, prediction strategies, and other content using performance measurements and AI suggestions.

How It Works

1
📖 Discover the tool

You find a simple set of guides to automatically improve your outreach messages or betting strategies by testing tiny changes that boost results.

2
📝 Prepare your starting point

Create a sample message or strategy and track its basic performance, like how many replies you get or how accurate your predictions are.

3
🚀 Launch the improvement loop

Run the weekly routine where an AI helper reads your latest results, suggests one smart tweak, and prepares a better version for you.

4
👀 Review the suggestion

Check the proposed change and its reasoning – it feels like having a clever coach explaining why this could work even better.

5
📤 Test in the real world

Use the updated message or strategy on your next batch, then note the new results like higher replies or better scores.

📈 Enjoy compounding gains

Week after week, your reply rates or prediction accuracy keep climbing as the tool builds on what works best.

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AI-Generated Review

What is autoresearch-openclaw?

This Shell-based toolkit ports Karpathy's autoresearch loop from GitHub karpathy/nanogpt and github karpathy/llm.council to non-ML domains like outreach, prediction markets, and prompts. You feed it a text asset (email template, strategy script, or prompt), a scalar metric (reply rate, Brier score, eval score), and a budget—it runs an AI agent to propose one change per iteration, tests it, keeps winners via git, and logs progress. Developers get automated compounding improvements without manual tweaking.

Why is it gaining traction?

It democratizes Karpathy's github karpathy/ai and autoresearch loops for everyday tasks: cold outreach reply rates climb weekly, prediction strategies backtest to lower Brier scores, prompts sharpen via binary evals. CLI simplicity (./outreach-loop.sh or ./autoresearch.sh --metric "your-cmd") plus git safety (revert bad changes) hooks devs tired of static tools—run overnight, wake to optimized files and reports.

Who should use this?

Indie hackers iterating outreach templates for 2-10 person teams; startup founders tuning prediction market bots on Polymarket/Kalshi data; AI builders optimizing system prompts with eval suites. Suited for anyone with a measurable text asset, like growth marketers chasing CTR or traders backtesting strategies.

Verdict

Promising early experiment with 16 stars and 1.0% credibility score—docs are solid for demos, but low maturity means test your metrics first. Grab it if Karpathy's github karpathy/llm inspires non-ML loops; skip for production without your own hardening.

(198 words)

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