How I Started Vibe Coding: A Journey from Lovable to Cursor

I'm not a developer. I have an MIS background so I understand how systems work, but writing production code was never my thing. Then AI coding tools showed up and suddenly building Toolpod seemed possible. Here's how I did it and how you can too, whether you're technical or not.

The Lovable Experiment

I had this idea for a developer tools site. Hiring a developer felt expensive. Learning to code felt like years of work. Then someone mentioned Lovable, where you just describe what you want in plain English and it builds it.

I tried it. Described a simple site with some tools. Lovable generated it. Clicked around. It worked.

If you have any technical background at all (took a coding class, understand databases, worked with APIs), you'll be able to read the generated code and understand what it's doing conceptually. If you're completely non-technical, you won't care about the code anyway, you'll just care that it works.

That's the magic. You describe what you want. AI builds it. You test if it does what you need.

The limitation: customization. Lovable is incredible for building fast. But when you want specific changes, you're describing them in English and hoping it understands. Sometimes it does. Sometimes it rebuilds more than you wanted.

Moving to ChatGPT and Codex

ChatGPT became my next step. When Lovable couldn't do what I needed, I'd describe the problem to ChatGPT. It would generate code. I'd paste it in and test it.

If you're technical, you can read the code and evaluate if the approach makes sense. If you're non-technical, you just test if it works. Both approaches are valid.

The problem was context switching. Describe problem in ChatGPT, copy code, paste into your project, test, copy error back. It worked but felt clunky.

GitHub Copilot improved this by living in your code editor. As you type, it suggests what comes next. If you have any coding background, you can write comments describing what you want and Copilot fills in the code. If you're non-technical, this won't help much because you need to be in a code editor to use it.

Claude Changed How I Approach Problems

Claude was different. Better at understanding what you're trying to accomplish, not just pattern matching on keywords.

I started using Claude for planning. "Here's what I want to build, how should I structure this?" Claude would help me think through the architecture.

For technical folks: Claude helps translate your systems knowledge into technical architecture. You understand data flow and requirements, Claude helps figure out implementation.

For non-technical folks: Claude helps you articulate what you want clearly enough that other AI tools can build it. Think of it as a translator between your idea and working code.

The key lesson: you don't need to write code. You need to know what you want the system to do and be able to evaluate if it's doing it right.

Cursor: Where It Actually Worked

Cursor is what made building Toolpod possible. It's an AI-powered code editor that understands your entire project.

The game changer: I could describe what I wanted and Cursor would implement it across multiple files. "Add user authentication" touches databases, API routes, frontend components. Cursor handles all of it. I review the changes and ship it.

For technical folks: You can guide Cursor with specific technical requirements. "Use JWT for auth tokens" or "normalize this data before storing" and it implements properly. Your technical knowledge helps you review what it generates and catch problems.

For non-technical folks: You describe features in plain English. "Users should be able to log in with email" and Cursor figures out the technical details. You test if it works. If something's broken, you describe the problem and Cursor fixes it.

Agent mode is powerful. Tell it "build a comparison table for AI tools" and it creates everything. Sometimes perfectly. Sometimes you need to guide it. But it handles implementation while you focus on product decisions.

My workflow now: Use Claude to plan features and think through requirements. Use Cursor to build them. Test everything. Ship it.

What Actually Helps (Technical or Not)

After months of building with AI tools, here's what matters:

Understanding what you want to build. This is more important than technical knowledge. If you can clearly describe what the system should do, AI can build it. Vague requirements lead to vague implementations.

Being able to test if it works. Click through your app. Does it do what you expected? Does the data save correctly? Do errors show up properly? You don't need to read code to test functionality.

Recognizing when something's wrong. If the app feels slow, if data disappears, if things break randomly, you know there's a problem even if you don't know how to fix it. Technical background helps here but common sense works too.

Breaking down big features into small steps. Whether you're technical or not, AI tools work better when you give them focused tasks. "Build user authentication" is easier than "build the entire user system."

A Guide for Anyone Who Wants to Build

Step 1: Start with the easiest tool.

• Non-technical: Try Lovable or Bolt. Describe your idea, get a working app in minutes.
• Technical: Same tools work, but you can also start with Replit which gives you more control.

Step 2: Learn what you need to learn.

• Non-technical: You don't need to learn to code. Learn to describe what you want clearly. Learn to test if things work. That's enough.
• Technical: Learn to read generated code. Understand the patterns. You'll spot problems faster and can guide the AI better.

Step 3: Use ChatGPT or Claude for planning. Both are great at helping you think through features before building them. Describe what you want, ask questions, iterate on the plan. This works whether you're technical or not.

Step 4: Upgrade to better tools when needed. Once you're building something real, try Cursor or Continue. They understand your whole project and can make complex changes. Worth the learning curve.

Step 5: Test everything. Click every button. Try to break things. Enter bad data. See what happens. This is how you catch problems whether you understand code or not.

The Tools I Use Now

After trying 18+ AI coding tools, here's my stack:

Daily building: Cursor ($20/month) - Handles the actual coding

Planning: Claude - Helps think through features and requirements

Quick prototypes: Lovable ($20/month) - Test ideas in minutes

Backup: GitHub Copilot ($10/month) - Reliable autocomplete

Check out my full comparison of 18 AI coding tools with pricing, features, and what each one's good for.

The Reality Check

Building with AI tools has real limitations:

You'll hit walls. Sometimes the AI generates something that doesn't work and you won't know why. If you're technical, you can debug conceptually. If you're not, you'll need to describe the problem clearly enough for the AI to fix it.

Complex features need expertise. AI can build basic features easily. Complex features (payments, real-time updates, heavy data processing) require more guidance. Technical background helps but even non-technical folks can build surprisingly complex things with patience.

Security and performance matter. AI doesn't automatically make things secure or fast. For side projects this is usually fine. For real products with users and data, you'll eventually need expert review.

You're learning as you go. Every time AI generates something, you learn a little. After months of building Toolpod I understand way more about how web apps work than when I started. But there are gaps in my knowledge.

Speed can be dangerous. You can build so fast that you skip proper testing. Don't. Test everything. Make sure it actually solves the problem you're trying to solve.

Why This Works for Side Projects

AI tools are perfect for side projects:

You can build and ship real products without years of learning to code. You focus on product decisions (what to build, who it's for, what problems it solves) and AI handles implementation.

For technical folks: You get to use your systems knowledge without needing to be an expert coder. Your understanding of data, logic, and requirements is more valuable than syntax knowledge.

For non-technical folks: You can finally build that idea you've had without paying $50k to a dev shop or spending years learning to code. The barrier to entry is just willingness to experiment.

This is how I built Toolpod. It's how thousands of people are building products now. The tools keep getting better.

Tools for Validating Your Work

Whether you're technical or not, these tools help you check what the AI built:

• JSON Formatter - Validate data structures
• JWT Decoder - Check authentication setup
• SQL Formatter - Verify database queries
• RegEx Tester - Test data validation
• Hash Generator - Verify security

You don't need to fully understand these to use them. They help you validate that things are set up correctly.

Would I Recommend This Approach?

For side projects and MVPs? Yes. Whether you're technical or not, you can build real things now. The tools are good enough.

For production systems? You'll eventually need real developers. AI is incredible for getting started but real products need optimization, security hardening, and maintenance that requires expertise.

For learning? It's a weird way to learn but it works. You learn by doing and reviewing rather than studying. After building Toolpod I understand web development way better than when I started.

The bottom line: You don't need to be a developer to build things anymore. You need to know what to build and be able to test if it works. If you're technical, your background helps you guide the AI and catch problems. If you're not, you can still build real products with patience and good testing.

The tools keep getting better. The barrier to building keeps getting lower. If you have an idea, try it. You'll be surprised what you can build.