Summary This repository provides a 5-stage AI-driven workflow template for rapidly generating Minimum Viable Products (MVPs) from app ideas. It targets founders and developers, leveraging advanced 2025 AI models and agents to transform concepts into working code within hours, significantly accelerating prototyping.

How It Works The workflow guides users through five AI-powered stages: research, product requirements definition (PRD), technical design, AI agent instruction generation, and code generation for a working MVP. It utilizes large-context AI models and integrates with a variety of AI coding agents and IDEs, automating the early-stage software development lifecycle.

Quick Start & Requirements Requires selecting an AI platform (e.g., AI Studio, Claude.ai, ChatGPT) and an AI coding agent/IDE (e.g., Claude Code, Gemini CLI, Cursor, VS Code + Copilot, Bolt.new). Basic computer skills are sufficient; Node.js 20+ is optional for terminal tools. The workflow is designed for completion within 2-4 hours. Links to AI platforms and agent/IDE tools are provided.

Highlighted Details

• Employs state-of-the-art 2025 AI models (Gemini 2.5 Pro, Claude Sonnet 4.5, GPT-5) with large contexts.
• Supports diverse AI development tools: terminal agents, no-code platforms, and IDEs.
• Designed for rapid MVP delivery (1-3 hours AI execution).
• Offers detailed prompt templates and troubleshooting guides.

Maintenance & Community The project is community-driven, inviting contributions via PRs and issues for bug reporting, success sharing, tool configuration additions, and example MVP submissions. No specific community channels or core maintainer details are provided.

Licensing & Compatibility Released under the permissive MIT License, generally allowing for commercial use and integration into closed-source projects without significant restrictions.

Limitations & Caveats Not recommended for native mobile/hardware builds, regulated workloads requiring specific compliance (SOC2, HIPAA), safety-critical systems, or for practicing fundamental coding concepts. Users must be aware of potential data sharing with consumer AI accounts, the need for guardrails on autonomous AI loops, and common pitfalls like skipping discovery, inadequate code review, unexpected costs, and insecure auto-generated UIs.