ruanyf

ruanyf / ai-test-case

Public

AI 编程的测试用例

79
10
100% credibility
Found Feb 12, 2026 at 36 stars 2x -- 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

A curated set of prompts and starter projects to benchmark AI models' code generation skills on tasks like web redesigns, simulations, games, and app migrations.

How It Works

1
🔍 Discover AI Tests

You stumble upon a friendly collection of real-world challenges to see how smart AI helpers are at building websites and apps.

2
📋 Pick a Fun Challenge

Browse simple tasks like sprucing up a basic webpage, creating a space simulation, or switching an app to a new style.

3
💬 Challenge Your AI Buddy

Copy the clear instructions and share them with your AI friend to create something amazing.

4
✨ Watch Magic Happen

Your AI whips up complete webpage files or app pieces that look professional and work smoothly.

5
🖥️ See It Live

Save the new files and open them right in your web browser to experience the fresh design or interactive fun.

🎉 Spot the Best AI

Enjoy the cool results, compare different AIs, and learn which one crafts the most impressive creations.

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AI-Generated Review

What is ai-test-case?

This repo curates test cases for benchmarking AI code generation, with detailed prompts tackling real dev tasks like redesigning SaaS landing pages in plain HTML/CSS/JS, building 3D gravity sims, cloning Angry Birds-style games, migrating Laravel Blade apps to Next.js, and simulating physics experiments. Developers feed these prompts to LLMs like Claude or GPT variants, then compare outputs against provided baselines—such as a weather dashboard fetching OpenWeather/Nominatim data. It's a straightforward way to test AI on production-like challenges without building evals from scratch.

Why is it gaining traction?

Sourced from YouTube benchmarks pitting top models head-to-head, these prompts expose gaps in AI coding (e.g., dull designs from Codex vs. polished Claude results), making it a go-to for objective comparisons. The Laravel-to-Next.js case stands out, giving instant side-by-side framework migration tests with full starter apps. Devs love the no-build-step constraints for quick local runs.

Who should use this?

AI prompt engineers tuning LLMs for code gen, frontend devs validating tools before team rollout, or backend teams testing framework ports like PHP to JS. Ideal for researchers running model showdowns or consultants demoing AI test case generation limits.

Verdict

Grab it if you're evaluating AI for coding—low 10 stars and 1.0% credibility reflect early stage, but solid prompts and baselines make it useful now. Maturity lags with sparse docs; fork and expand for serious use.

(187 words)

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