How We Built RnR Vibe With Vibecoding

RnR Vibe is a platform about vibecoding, built with vibecoding. That's not just marketing — it's the actual development story. Here's how we went from an idea to a live platform with 24 tools, 15 demo projects, and a full content library.

The Starting Point

The goal was simple: create a free, no-sign-up platform for developers learning to code with AI. We wanted tools, guides, and projects — all in one place.

The tech stack decision came first:

• Next.js for full-stack capability with great SEO
• Tailwind CSS for rapid UI development
• Ollama for local LLM inference (no API costs, full privacy)
• MDX for content that lives alongside code

The Process: Prompt, Review, Refine

Building with AI isn't "type a prompt and ship it." Our actual workflow looked like this:

1. Describe the feature in detail — what it does, how it looks, edge cases to handle
2. Review the output line by line — AI gets you 80% there, but the last 20% matters
3. Refine iteratively — fix bugs, improve UX, tighten security
4. Test manually — every tool, every edge case, every mobile breakpoint

What Worked Well

Component generation was fast. Describing a UI component and getting a working React + Tailwind implementation in seconds is genuinely transformative. The tool pages, blog layouts, and navigation components all started as AI-generated code.

Content creation accelerated. Blog outlines, guide structures, and even initial drafts came from AI prompts — but every word was reviewed and rewritten to match our voice.

Boilerplate elimination. API routes, metadata configurations, sitemap generation — the repetitive plumbing code that usually takes hours was generated in minutes.

What Needed Human Judgment

Security couldn't be vibecoded blindly. Our guardrails system — prompt injection detection, output filtering, rate limiting — required careful manual design. AI can write the code, but understanding the threat model requires human expertise.

Architecture decisions. When to use server components vs. client components, how to structure the content system, where to put the rate limiter — these decisions need context that AI doesn't have about your specific constraints.

UX polish. The difference between a demo and a product is in the details: loading states, error messages, mobile responsiveness, accessibility. AI gets the structure right, but the craft requires human eyes.

Lessons for Your Projects

1. Start with structure, not features. Get your layout, routing, and data model right before adding complexity.
2. Review everything. AI-generated code works, but it doesn't always follow best practices. Read what you ship.
3. Security is not optional. If your app takes user input and talks to an LLM, you need guardrails. Period.
4. Iterate publicly. We shipped early and improved continuously. Don't wait for perfect.
5. Use AI for what it's good at — generating boilerplate, exploring options, accelerating implementation — and use your brain for what it's good at: judgment, taste, and architecture.

The Numbers

• 24 AI-powered tools — each with its own system prompt and safety rules
• 15 interactive demo projects — all built with vibecoding
• 23+ articles — blog posts and guides covering every aspect of AI-assisted development
• Zero external API dependencies — everything runs on a local LLM

What's Next

We're continuously adding new tools, projects, and content. The platform itself is a living example of what's possible when you combine AI capability with human judgment.

The best way to learn vibecoding is to start building. Pick a tool, try a project, read a guide — and ship something today.