Methodology · Resources

MVP in 8 hours

Methodology5 steps

From zero to a launched product in one day, with the development cycle cut from weeks to hours.

Methodology

What it is

The "MVP in 8 hours" schedule (an MVP is the first working version of a product) divides the working day into five phases. It is the opposite of traditional development cycles (sprints), which last weeks. It is only possible thanks to a set of tools built on AI. Each phase uses different tools and procedures, but they share the same goal: deliver as fast as possible while keeping it working.

5 steps

Steps

Step 1 · 09:00
Defining the problem
Defining the logic
The first hour is not spent coding but defining. In vibe coding that means writing good prompts for AI (prompt engineering) and being clear about what job the product does for the customer (Jobs to be Done). The developer formulates the brief for the AI. Instead of writing a specification in Word, they write a system prompt for a language model (LLM).
ToolsChatGPTClaudeSystem prompts
TipDefine clearly what is being built, so the AI can propose how to build it.
Step 2 · 10:00
Design (AI)
An interface created by AI
Instead of weeks spent building a set of design elements in Figma, the creator uses tools that create the look of the app from a description. For example, v0 or Lovable convert a text brief (say, "a modern dashboard in dark mode with neumorphic elements") straight into code (React/Tailwind).
Toolsv0LovableFigma AIMidjourney
TipOutput: A finished visual layer of the app, ready to be connected to the logic.
Step 3 · 13:00
The core of the app
Building the core
Building the working core. Here the rule of the manifesto applies: "Stop writing syntax." The developer instructs the AI (say, via Cursor Composer) to create database schemas, API endpoints (places where programs exchange data) and to connect the visible part of the app (frontend) with the back end (backend).
ToolsCursorReplit AgentGitHub Copilot
TipUse AI in "Agent Mode", which creates and edits files across the whole project by itself.
Step 4 · 16:00
Edge cases
Debugging and hardening
The phase of debugging and handling edge cases. AI often creates only the "happy path" (where everything goes as it should), so you must explicitly test for failures. From a security point of view it is the riskiest part of the day.
ToolsTestingCode reviewSecurity audit
TipAI fixes are functionally correct in 60% of cases, but secure in only 11%. Pay attention to security risks.
Step 5 · 17:00
Publishing
Immediate deployment
Immediate publishing. Platforms such as Vercel, Netlify or Replit Deployments themselves let you publish a product with one click. The goal is not a perfect product but one that is deployed and can collect feedback.
ToolsVercelNetlifyReplit Deployments
Tip"Ship Fast": the goal is not perfection but a product that can collect feedback.

What we actually use: Website builders

Lovable.dev: the main tool for first versions (MVPs), internal systems and public products.

What we actually use: Development environments with AI agents

Cursor: Precise work with code and internal tools. Antigravity IDE: Complex app logic and its design.

What we actually use: AI models

GPT: Text, analysis, logic. Claude: Long context and working with documents. Gemini: Exploring ideas and brainstorming.

What we actually use: Generating visuals

Midjourney: Creative visuals and branding. Nanobanana: Fast generation and variations of image content. Sora: AI generation of video and images.

What we actually use: Automation

Make.com: Automating processes and connecting systems. N8N: Open-source automation.

What we actually use: Infrastructure

Vercel: Running a website and fast functions close to the user (edge functions). Netlify: Running a website, publishing it and automatic builds.

Critical analysis of security risks: Not enough time for edge cases

The schedule allots only 1 hour to edge cases, which from a cybersecurity point of view is too little for live operation. AI fixes are functionally correct in 60% of cases, but secure in only 11%.

Critical analysis of security risks: Common security problems

AI often creates code with hardcoded API keys, without limiting the number of requests (rate limiting) and prone to SQL injection (an attack through input fields). For live operation a thorough security audit is necessary, which goes beyond the scope of the 8-hour schedule.

Critical analysis of security risks: When to use MVP in 8 hours

This approach suits prototypes, validating ideas and internal tools. For production apps with sensitive data or high security requirements, you must add a further phase of security audit and testing.

Practical tips for success: Keep the pace

Do not stop on details. If something does not work perfectly, carry on. You can fix it in the next iteration.

Practical tips for success: Use prompt templates

Create prompt templates for AI for common tasks. You will save time and ensure consistent outputs.

Practical tips for success: Respect the time limits

Each phase has its time limit. If you are running behind, move to the next phase. An unfinished product is better than no product.

Practical tips for success: Test as you go

Do not test only at the end. Verify that things work after each phase. You will save time hunting for bugs.

Outcome

A deployed, live MVP in one working day, suitable for prototypes, validating ideas and internal tools that can start collecting feedback immediately.

All resources

Start here

15 minutes is enough for you to know whether it makes sense.

You tell us what is bothering you. We tell you whether we can help, roughly what it costs and where I would start in your place. No presentation.

Email: jakub@transformuj.ai
Phone: +420 773 070 090
Address: Legerova 1820/39, Praha 2