Vibe-coding describes a workflow where a nontechnical founder writes product specifications in text and an AI agent produces the application code.

What vibe-coding means in practice

In this approach, the human provides a detailed product description while the AI handles coding, debugging and iterative fixes autonomously.

The human role focuses on product requirements, acceptance criteria and continuous clarification, working closely with the model as a technical lead.

Which models to try first

Testing several large models during free trials helps determine which one fits your product needs and development expectations best.

• Gemini Pro — ~750₽ per month; large context window, solid for analytics, handles images and terminal screenshots well.
• ChatGPT Go/Plus — ~700₽ per month for Go; code capabilities remain among market leaders and integrate with developer agents.
• Claude Pro/Max — higher monthly cost; offers plugins and CLI tooling for terminal-centric workflows and advanced skills.

Hosting and data storage options

Choose hosting according to your stack: serverless providers work well for web frontends and small backends, while platform services suit bots and parsers.

• Vercel — suitable for sites built with Next.js; free tier covers most initial needs and integrates with Git providers.
• Railway — simple deployment for bots and Python systems; pricing starts at $5 per month with a free first month.

For databases, prefer solutions that simplify integration with models and provide realtime updates when necessary.

• Neon — easy setup and stable operation.
• Supabase — widely used and compatible with many tooling ecosystems.
• Convex — optimized for real-time interactions with models during active development.

Store code in GitHub to track versions, enable collaboration and connect with code agents in your editor environment.

Typical vibe-coding workflow

1. Describe the product in detail to the chosen model, asking for stack recommendations and technical trade-offs as needed.
2. Generate an agent prompt and deploy it inside a code-aware editor to begin automatic code generation and commits.
3. When errors appear, provide terminal screenshots or logs to the model and request targeted fixes or automated rollbacks.

Getting started and practical advice

Begin with a small interactive prototype, such as a calculator or simple web form, and iterate until the agent reliably reproduces desired behavior.

Treat the model as a collaborative technical expert: supply exhaustive context, ask clarifying questions and validate outputs with tests and code reviews.