How to Vibe Code Your MVP in Weeks Using AI-Assisted Development

We need to stop pretending that "doing it right" means taking three months to ship a CRUD app.

In the startup world, over-engineering isn’t a badge of honour. It is often just procrastination disguised as quality. We hide behind "best practices," clean architecture, and perfect test coverage because it feels safe. It feels professional. But while we are debating folder structures, the market is moving on.

If you are treating your pre-revenue MVP like it’s a legacy enterprise system, you aren’t being thorough—you are bleeding opportunity.

I recently shipped a production-ready Laravel MVP in about 45 hours of active coding time, all while working a full-time job as a Senior Engineering Lead. No sabbatical. No 40-hour blocks. Just nights, weekends, and an AI-assisted workflow…

We need to stop pretending that "doing it right" means taking three months to ship a CRUD app.

If you are treating your pre-revenue MVP like it’s a legacy enterprise system, you aren’t being thorough—you are bleeding opportunity.

This isn’t a guide on how to be lazy. It’s a guide on how to stop hiding.

The Call That Started It All

It was a regular Tuesday afternoon when my screen lit up with a Google Meet invite from the CEO. The CTO was already in the call when I joined. No agenda, no pre-read—just an invite titled "Quick Sync."

The CEO cut straight to it. He recounted something he’d heard about recently—a frustrating experience that had surfaced at a place he frequents. Someone close to the situation had dealt with a friction point that, frankly, shouldn’t exist in 2025. The specifics don’t matter here, but what struck me was the immediacy. This wasn’t abstract product ideation. This was a real problem, freshly articulated, with tangible urgency behind it.

And then the constraint that reframes everything: a prospective first client was already in motion. They faced the same friction, the same gap. The window to deliver something meaningful—while the pain was still visceral—was narrow.

The call ended with a direct question: "How fast can we move on this?"

I gave an honest answer: "A couple of weeks, if we stay focused."

But there was a catch. I wasn’t doing this with a clear calendar. I work full-time as a Senior Backend Developer and Engineering Team Lead, and I have my own personal projects in flight. I didn’t have 40 hours of open air; I had nights, weekends, and stolen moments.

That was when I started "cooking."

This guide walks you through exactly how I did it—the AI-assisted workflow, the collaboration patterns, and the lessons learned shipping a production Laravel MVP in approximately 40–50 hours of focused coding, spread across 6 weeks.

[!NOTE] Quick disclaimer: This isn’t a Windsurf promotion. Windsurf is my editor of choice, but any capable AI-powered environment—Cursor, VS Code with Copilot, or similar—would work. The workflow is tool-agnostic. The principles transfer.

What You’ll Learn

How to use AI for architectural scaffolding before writing code
Setting up an effective AI-assisted development environment
The iterative workflow that actually works under pressure
Collaboration patterns for distributed teams
Post-launch maintenance realities and how AI helps (and doesn’t)
Time investment benchmarks from a real project

Reality Check: What Vibe Coding Is NOT

Before we go further, let’s draw a hard line. "Vibe Coding" is not an excuse for sloppy engineering.

It is NOT pasting prompts and hoping for the best.
It is NOT shipping code you don’t understand.
It is NOT bypassing security or architectural standards.

Vibe coding is context-aware acceleration. It is using AI to handle the implementation details (the "how") so you can focus entirely on the architectural intent (the "what" and "why"). If you can’t read the code the AI generates, you shouldn’t ship it.

Prerequisites

Before you start vibe coding your MVP, ensure you have:

A clear problem statement (alignment > speed)
Laravel development environment (I use Laravel Herd)
An AI-powered code editor (Windsurf, Cursor, or VS Code with Copilot)
Access to high-reasoning LLMs for architectural thinking (I utilized Claude Sonnet 4.5, Google Gemini 3 Pro, and GPT-5 across my workflows)

Step 1: Architect Before You Code

Don’t jump into your editor immediately. Use AI to think through architecture first.

What I Did

With urgency as the backdrop, I needed to move fast but deliberately. The first phase involved getting an initial architecture in place—something I could think through and validate before committing to code.

I opened Google Anti Gravity and described the problem space. Not the solution—the problem. The constraints. The edge cases I could anticipate. This was my first time using it for this kind of architectural thinking, and it proved invaluable for exploring different approaches before writing a single line of code.

Prompt pattern:
"I'm building a system that needs to [core requirement].
The key constraints are [list constraints].
What architecture patterns would you recommend?
What edge cases should I consider before I start?"

Why This Works

AI excels at brainstorming and pattern matching. Before you’ve written a single line, you can:

Validate data relationships
Identify potential architectural decisions
Surface edge cases you’d otherwise discover mid-implementation
Get a mental model of the system’s shape

Time invested: 1–2 hours Time saved: Potentially days of refactoring

Step 2: Set Up Your AI-Assisted Environment

Once architecture is clear, switch to your code editor.

My Transition

As the project evolved and iteration became heavier, I switched from Google AI Studio to Windsurf. The shift wasn’t about capability—it was about workflow fit. Windsurf better suited how I wanted to work contextually, maintaining awareness of the codebase as I moved through features and fixes. I was simply more familiar with its patterns.

The Stack That Worked

Tool	Purpose
Google AI Studio (Gemini Advanced)	Initial architectural scaffolding and design validation
Windsurf	Primary editor with contextual AI assistance
Laravel Boost MCP	Laravel-specific code generation and framework guidance
Herd MCP	Local development environment orchestration
Tailwind CSS	Utility-first styling for rapid UI development
Livewire	Reactive components without leaving PHP

MCP Configuration Note

[!WARNING] Laravel Boost MCP and Herd MCP are not compatible out-of-the-box with Windsurf’s editor and plugins. If you’re using these MCP servers, you’ll need to configure them carefully. I’ve written a detailed guide on fixing this: Fixing Laravel Boost in Windsurf: A Global MCP Setup Guide.

Step 3: Iterative Development (The Vibe Coding Loop)

Here’s where AI-assisted development truly shines.

The Core Workflow

┌─────────────────────────────────────────────┐
│  0. Create a solid implementation plan      │
│  1. Describe what you want to build         │
│  2. AI generates initial implementation     │
│  3. You review, refine, correct             │
│  4. Test immediately                        │
│  5. Repeat                                  │
└─────────────────────────────────────────────┘

Non-Negotiable: The Implementation Plan

Before writing any code, create a comprehensive implementation plan from start to finish. This isn’t optional—it’s the foundation that makes AI-assisted development work.

Use AI to help you:

Map out the entire MVP scope
Identify dependencies and sequencing
Surface tradeoffs early (e.g., "Do we need this feature for v1?")
Document decisions so you don’t relitigate them later

Fine-tune this plan iteratively with AI until it feels solid. The time invested here pays dividends when you’re deep in implementation and need to make quick decisions.

The Key Mindset Shift

AI-assisted ≠ AI-driven

This distinction shaped everything. AI wasn’t writing the application for me—it was helping me think faster. When I had an idea, I could validate it quickly. When I needed to scaffold a component, I could generate a starting point and refine it. When I hit a wall, I could explore alternatives without losing momentum.

The human remains the architect. The AI accelerates execution.

Practical Tips from the Trenches

Stay contextual: Keep related files open. AI needs context to be useful.
Correct early: When AI misunderstands, fix immediately. Don’t let errors compound.
Commit frequently: Small, atomic commits. You’ll thank yourself during debugging.
Test as you go: Don’t wait until "it’s done." Test components in isolation.

Step 4: Collaboration Patterns That Scale

Sustained pressure demands distributed ownership. Building under pressure is rarely solo, and this project was no exception.

How We Worked

The CTO and I logged significant hours on Google Meet—not merely discussing requirements, but actively testing together in real-time. He would surface something that felt off, I would push a fix, and we’d validate immediately. The rhythm became instinctive.

Periodically, the CEO and company director joined these sessions. There’s irreplaceable value in fresh perspective. Someone who hasn’t been immersed in the same context for hours will identify friction you’ve internalized as normal.

Responsibility Partitioning

Role	Ownership
CTO	Server provisioning, Bitbucket pipeline configuration, deployment orchestration, production stability
Developer (me)	Business logic, user flows, edge case handling, bug fixes

This separation enabled genuine parallelism. While I resolved a validation defect, he configured SSL termination. While he debugged a pipeline failure, I refined a notification flow. Neither of us waited on the other.

The Feedback Loop

test → feedback → fix → repeat

The collaboration wasn’t ceremonial—it was operational. We weren’t scheduling reviews to satisfy process. We were building together, in real-time, with shared accountability for the outcome.

Step 5: Maintenance (Where Reality Asserts Itself)

Shipping the MVP marked a milestone, but seasoned engineers understand: it’s the beginning, not the conclusion.

What to Expect Post-Launch

Production users possess an uncanny ability to surface truths that no test suite anticipates:

Edge cases you never modeled will surface
Workflows you assumed were self-evident won’t be
Data patterns will defy your assumptions

In the weeks following initial deployment, defects emerged. Requirements evolved. The system that felt complete revealed its gaps under authentic load.

How AI Helps (and Doesn’t)

AI tooling continued to accelerate the work—suggesting fixes, helping trace through unfamiliar code paths, reducing the friction of context-switching.

But judgment, prioritization, and accountability remained unambiguously human responsibilities.

No AI can articulate why something matters to the business. It can help remediate a defect, but it cannot determine whether that defect is critical or cosmetic.

A Real Refactoring Story: When Your Data Model Fights Back

I’ll be honest here: this one was on me.

During the initial build, I was moving fast with AI-generated code and database structures. I reviewed them, approved them, and moved on. The schema looked reasonable. The relationships made sense at first glance. But I failed to catch a fundamental modeling issue—a key domain entity was set up as a standalone table when it should have been a specialized extension of an existing user-context relationship.

The problem didn’t surface until I was testing a related feature, hit a bug, and started digging deeper. That’s when the friction became obvious: duplicate data paths, inconsistent relationships, and unnecessary complexity rippling across multiple features.

Here’s where AI became genuinely valuable as a thinking partner. I broke down the issue, proposed the remodeling approach, and asked: "What are the ripple effects? And is this worth the effort now, or should we live with it?"

The response was unambiguous: do it now. The reasoning was sound—migrating data and updating relationships would only get harder as the dataset grew and more features depended on the flawed structure. Better to pay the cost early than compound it later.

The fix required significant refactoring:

Extend an existing pivot table with domain-specific columns
Write a data migration to move existing records
Update every component, service, and notification that touched the old model
Refactor related feature relationships to use the consolidated structure
Update all the profile and management components to use the new approach.

The Critical "Senior" Moment During this refactor, Windsurf suggested a "quick fix" that involved adding a JSON column to patch the data structure without migration. It would have worked instantly. It would have saved me 3 hours that night.

I rejected it immediately.

Why? Because I know that JSON patches on core relational data turn into query nightmares six months later. An AI optimizes for solving the current error. A senior engineer optimises for future maintainability. I forced the AI to generate the harder, proper migration path. Speed matters, but not at the cost of structural integrity.

This was spread across multiple pull requests. AI accelerated each step—generating migration scaffolds, updating model relationships, tracing through affected components. However, the decision to restructure came from living with the original design and recognising where it broke down.

Lesson: Your first data model might be wrong—and that’s okay. Design for flexibility and possible refactoring down the road. Don’t rush into rigid schema decisions just to feel "done." The cost of early flexibility is low; the cost of late rigidity is high.

Time Investment Benchmarks

Transparency matters. Here’s what this MVP actually took, derived from the project’s commit history:

Metric	Value
Active development days	8 distinct days
Total elapsed time	~6 weeks
Estimated coding hours	40–50 hours
Total commits	107

Context is critical here. I didn’t take six weeks off to build this. I hold a full-time role as a Senior Backend Engineer and Team Lead, plus I actively maintain other personal software projects. This MVP was built entirely in the margins—early mornings, late nights, and weekends.

This is where the true value of AI-assisted development lies. It wasn’t just about generating code faster; it was about instant context recovery. When I only had 45 minutes on a Tuesday night, I didn’t spend 20 minutes remembering where I left off. Windsurf and the implementation plan allowed me to dive straight into the "flow state," execute a feature, and sign off.

AI tooling didn’t eliminate the work—it compressed it. Efforts that might have consumed weeks under traditional workflows occurred in focused sessions, with rapid iteration and immediate validation.

Key Lessons for Your MVP

Reflecting on this project, several principles crystallised:

1. Speed Is a Form of Clarity

Let’s be uncomfortable for a second: Over-engineering is usually disguised fear in a lot of cases. We build complex architectures for simple problems because we are afraid of tech debt. But for an MVP, the only debt that kills you is apathy—building something nobody uses.

In this project, I deliberately skipped standard abstractions I would mandate in my day job. It felt "wrong" at first. But that speed revealed the product’s actual shape faster than any architecture diagram could. Code can be refactored; time cannot be refunded.

2. AI Accelerates Momentum, Not Accountability

These tools are force multipliers—they amplify velocity when direction is clear. But they do not own outcomes. Ownership remains yours.

3. Real Users Reveal Truths No Prompt Can Anticipate

Test suites are constrained by your imagination. Users will discover paths you never considered. Architect for iteration, not premature perfection.

4. Collaboration Eclipses Solo Brilliance

The tight feedback loop with the CTO—real-time testing, immediate remediation—delivered more value than any individual AI tool. Human collaboration remains the ultimate accelerator.

Quick Reference: The Vibe Coding Workflow

PHASE 1: PREPARATION
[Problem Definition]
│
▼
[AI Architectural Brainstorm]
│
▼
[Implementation Plan] ───→ [Environment Setup]
│
│
PHASE 2: VIBE CODING LOOP         │
┌───────────────────────────┘
│
▼
[Iterative Dev Loop] <──────────────┐
│                             │
▼                             │
{Working Feature?} ──── NO ─────────┤
│                             │
YES                            │
│                             │
▼                             │
[Test with Stakeholders]            │
│                             │
▼                             │
{Feedback?} ───────── YES ────────┘
│
NO (Approved)
│
▼
[ 🚀 SHIP IT ] ───→ [Maintenance & Iteration]

Technical Stack Reference

For those interested in the underlying architecture:

Layer	Technology
Framework	Laravel
Frontend	Livewire for reactive components
Styling	Tailwind CSS
Architecture	Multi-tenancy (implementation details intentionally omitted)

Final Thoughts: You Are Out of Excuses

This project proved one uncomfortable truth: The barrier to entry has collapsed.

The "I don’t have time" excuse is gone. The "I need a team" excuse is gone. The tools I used (Windsurf, MCP, Claude, Gemini) didn’t just write code; they removed the friction that usually kills momentum. They allowed me to be an Architect for 45 hours straight, rather than spending 20 of those hours fighting syntax.

You have a choice. You can keep polishing your boilerplate, feeling busy, and protecting your ego with "clean code" that nobody uses. Or you can open your editor tonight, scope it down, and ship something that might actually break.

If you are a senior dev, these tools make you a 10x architect. If you are a junior, they are the best mentor you will ever have.

But they won’t click the "Deploy" button for you.

The window is open. Start cooking.