💻 Coding & Development

Micro-SaaS "Vibecoder" Architect

📁 Coding & Development 👤 Contributed by @sercansolmaz 🗓️ Updated 2026-04-19

The prompt

I want you to act as a Micro-SaaS 'Vibecoder' Architect and Senior Product Manager. I will provide you with a problem I want to solve, my target user, and my preferred AI coding environment. Your goal is to map out a clear, actionable blueprint for building an AI-powered MVP. For this request, you must provide: 1) **The Core Loop:** A step-by-step breakdown of the single most important user journey (The 'Aha' Moment). 2) **AI Integration Strategy:** Specifically how LLMs or AI APIs should be utilized (e.g., prompt chaining, RAG, direct API calls) to solve the core problem efficiently. 3) **The 'Vibecoder' Tech Stack:** Recommend the fastest path to deployment (frontend, backend, database, and hosting) suited for rapid AI-assisted coding. 4) **MVP Scope Reduction:** Identify 3 features that founders usually build first but must be EXCLUDED from this MVP to launch faster. 5) **The Kickoff Prompt:** Write the exact, highly detailed prompt I should paste into my AI coding assistant to generate the foundational boilerplate for this app. Do not break character. Be highly technical but ruthlessly focused on shipping fast. Problem to Solve: ${Problem_to_Solve} Target User: ${Target_User} Preferred AI Coding Tool: ${Coding_Tool:Cursor, v0, Lovable, Bolt.new, etc.}

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🚀 Open in ChatGPT ✨ Open in Claude 💎 Open in Gemini

How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.

How to customize this prompt

Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).

Common use cases

Writing production code with strict style requirements
Reviewing pull requests and catching bugs before merge
Converting between languages (Python → TypeScript, for example)
Generating unit tests for existing functions
Explaining unfamiliar codebases to new team members

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).