jhochenbaum

jhochenbaum / pi-autoresearch-studio

Public

Dashboard, plan editor, PR workflow, and orchestration tools for pi autoresearch sessions

95
9
100% credibility
Found Mar 30, 2026 at 95 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

A dashboard and workflow tool for monitoring AI agent experiments that optimize code performance and creating pull requests from successful changes.

How It Works

1
🔍 Discover optimization helper

You hear about a friendly tool that lets an AI assistant automatically improve your project's speed or size by running smart experiments.

2
📥 Add the tool

With one simple command in your coding helper, you bring the studio into your project—it fits right in without hassle.

3
🎯 Set your goal

Tell it what to make better, like 'speed up tests while keeping everything working perfectly'.

4
📊 Watch magic happen

Open the colorful dashboard to see live charts of experiments, winners in green, and a log of what the AI tried—it's exciting to watch improvements pile up!

5
✏️ Steer and review

Tweak ideas or plans right there, or get simple explanations of why an experiment won or lost.

6
✅ Pick the winners

Choose the best changes with a quick tap, preview safely, then ship them as neat review requests.

🚀 Enjoy faster project

Your code is now quicker or smaller, with a clean record of how the AI made it better—success!

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Star Growth

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AI-Generated Review

What is pi-autoresearch-studio?

pi-autoresearch-studio is a TypeScript dashboard and toolkit for pi-autoresearch sessions, where AI agents autonomously edit code, benchmark metrics like test speed or bundle size, and keep improvements via git commits. It provides a TUI and experimental web dashboard to monitor experiments in real-time, plus tools for selective PR creation on GitHub, LLM-powered explanations of results, and in-place editing of optimization plans. Users get a control plane to steer agents, review logs, and ship clean changes without autoresearch metadata clutter.

Why is it gaining traction?

It stands out with granular PR workflows—pick non-sequential kept experiments, auto-resolve dependencies, and generate consolidated, stacked, or individual GitHub PRs—far beyond pi-autoresearch's built-in finalize skill. The dual TUI/web dashboard offers charts, metric trends, and live updates, while commands like `/arstudio pr ` or `/arstudio web` make it dead simple to integrate into GitHub Actions or Copilot-driven workflows. Developers hook on the "dry run" previews and explain buttons that turn opaque agent experiments into actionable insights.

Who should use this?

Backend and fullstack devs running pi-autoresearch to optimize build times, training loss, or perf metrics in GitHub repos. ML engineers tweaking models via autonomous loops, or teams using dashboard planners for experiment tracking in Angular/React/PHP projects. Ideal if you're already on pi and want a dashboard GitHub project view to promote AI-suggested code surgically.

Verdict

Grab it if pi-autoresearch fits your stack—solid docs, full test suite, and intuitive CLI make it production-ready despite 95 stars and 1.0% credibility score signaling early maturity. Install via `pi install` and iterate; low risk for high-reward automation.

(198 words)

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