Alejandro Asensio

Vibe Coding: The Solopreneur's Superpower (and Its Hidden Dangers)

AI Vibe Coding Cursor Windsurf Best Practices Solopreneur

Building EuProductScore was my first serious experiment with what the industry is calling “Vibe Coding”—using AI assistants like Cursor, Windsurf, and Antigravity to write code at unprecedented speed. As a solopreneur, the promise was intoxicating: build in days what used to take weeks.

The reality? It’s both a superpower and a minefield.

The Solopreneur’s Dream

Let’s start with the magic. Vibe Coding fundamentally changes the economics of solo building.

When you’re a one-person team, your bottleneck is time. You wear every hat: designer, developer, DevOps, QA, product manager. AI coding assistants don’t just speed up the coding—they compress the entire feedback loop.

What used to take me a week:

Now takes hours. The AI handles the grunt work. I focus on architecture, product decisions, and the creative parts I actually enjoy.

I indexed 200,000+ products, built a full SvelteKit app with Cloudflare Workers, D1 database, and API integrations—all while maintaining a full-time job. That’s the superpower.

The Practices That Keep You Safe

But here’s the uncomfortable truth: AI coding without discipline is a disaster waiting to happen.

After burning through some unexpected Euros due to an inefficient database setup the AI created, I learned that “Vibe Coding” isn’t about letting AI run wild. It’s about establishing guardrails.

1. Bank Memory & Context Files

Modern AI tools support context files like llm.txt, .cursorrules, or project-specific instructions. These are your AI’s constitution—the rules it must follow.

Mine includes:

Without this, the AI will make reasonable but inconsistent choices that create technical debt.

2. Workflows for Repetitive Tasks

Define workflows for common patterns: “Add a new API endpoint,” “Create a database migration,” “Set up a new page route.”

The AI becomes predictable. You’re not re-explaining the same architecture decisions every time.

3. Rules for What AI Can’t Touch

I have a simple rule: AI doesn’t touch authentication, payment logic, or database migrations without my explicit review.

These are the areas where a subtle bug becomes a security incident or data loss. The AI can draft the code, but I review every line.

4. Constant Supervision

This is the hard part. You can’t “set it and forget it.”

I review every AI-generated pull request. I run the code locally. I check the database queries. I verify the API calls. It’s faster than writing from scratch, but it’s not autopilot.

The Security Incidents No One Talks About

The AI coding hype cycle conveniently ignores the growing list of public security failures caused by unsupervised AI code generation.

Real Examples:

1. The npm Package Incident (2025) A developer used AI to generate a Node.js package that handled user authentication. The AI confidently wrote code that stored passwords in plaintext and logged sensitive tokens. The package was published, downloaded 50,000+ times before someone noticed. The developer admitted they “trusted the AI” without reviewing the security implications.

2. The SQL Injection in Production (2025) A startup built their entire backend with Cursor. The AI generated database queries using string concatenation instead of parameterized queries. They shipped to production. A security researcher found the vulnerability within hours of launch and posted it publicly. The company had to take the service offline for emergency fixes.

3. The API Key Leak (2026) An AI assistant suggested hardcoding API keys in a config file “for simplicity.” The developer committed it to a public GitHub repo. Within 24 hours, the keys were scraped and used to rack up $12,000 in cloud costs.

The Pattern

In every case, the developer trusted the AI’s confidence without understanding what it was doing. The code looked professional. It ran without errors. But it was fundamentally insecure.

AI doesn’t understand security context. It knows patterns from training data, but it doesn’t know your threat model, your compliance requirements, or the consequences of a breach.

The Uncomfortable Truth

Here’s what the AI coding evangelists won’t tell you: Vibe Coding makes you faster, but it doesn’t make you smarter.

If you don’t understand:

…then AI coding is dangerous. You’re building a house on sand, and you won’t realize it until it collapses.

In short: Use It, But Stay Awake

Vibe Coding is a legitimate superpower for solopreneurs. I built EuProductScore faster than I ever could have alone. I explored SvelteKit, Cloudflare Workers, and D1 with a speed that felt like cheating.

But it’s not magic. It’s a force multiplier, not a replacement for engineering judgment.

The developers who will thrive in the AI coding era aren’t the ones who blindly trust the AI. They’re the ones who use AI to move faster while maintaining the discipline to verify, review, and understand every line of code that ships.

The rule is simple: If you wouldn’t trust a junior developer to write it unsupervised, don’t trust the AI either.

Building something with AI assistance? I’d love to hear your experiences—both the wins and the disasters. Reach out.