adrianco

adrianco / retort

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Platform Evolution Engine. Distill the best from the combinatorial mess.

84
1
100% credibility
Found Apr 13, 2026 at 84 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Retort systematically tests combinations of programming languages, AI coding agents, and frameworks on real tasks using statistical designs to identify optimal technology stacks.

How It Works

1
💡 Discover Retort

You hear about Retort, a smart way to test different coding tools and languages to find the perfect mix without trying everything.

2
🏠 Set up your playground

Create a simple folder where all your tests will happen, like starting a new recipe book.

3
🔧 Choose your ingredients

Pick the languages, AI helpers, and extras you want to compare, like selecting flours and spices.

4
📋 Plan your taste tests

Retort creates a short list of key combinations to try, saving you time and effort.

5
🚀 Run the experiments

Hit go and watch as it automatically builds little projects with each mix, measuring speed, quality, and smarts used.

6
📊 Review the results

See easy charts showing which mixes win on quality, speed, and cost, with stats proving the best ones.

🏆 Pick your winner

Confidently choose and use the top combo for your real projects, knowing it's the best from solid tests.

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AI-Generated Review

What is retort?

Retort is a Python CLI for running statistical experiments on AI agent platforms in software development. Define factors like languages (Python, Go, TypeScript), models (Opus, Sonnet), and tools, then generate fractional factorial designs, execute tasks in isolated Docker or local playpens, score on code quality, tokens, and build time, and promote top stacks via ANOVA and Bayesian gates. It distills optimal github agent platform combos from the mess, like proving Go + Sonnet + beads beats others on a REST CRUD task for under $4.

Why is it gaining traction?

Unlike ad-hoc benchmarks, retort applies DoE rigor—screening designs, aliasing checks, Pareto ranking—to cut experiments from 100s to dozens of runs while estimating main effects and interactions. CLI commands like `retort init`, `retort run`, `retort report effects`, and `retort promote` make it dead simple to iterate on platform evolution frameworks, with plugins for custom scorers and budget tracking. Early results hook devs tired of guessing winners in the github platform game.

Who should use this?

Platform engineers mapping github platform roadmaps or evaluating agent stacks for internal dev tools. Teams in cloud computing or large inter-org programs benchmarking AI coding across languages and frameworks. Anyone optimizing token efficiency and quality in github platform issues without manual trials.

Verdict

Grab it if you're serious about data-driven platform evolution—solid CLI, docs, and end-to-end pipeline despite alpha status and 84 stars. 1.0% credibility score flags low adoption, but Phase 4 complete and MIT license make it low-risk to prototype; watch for replicates on bigger tasks.

(198 words)

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