I've spent the last year fixing AI implementations for non-technical founders. Most conversations follow the same pattern: initial excitement about AI tools, followed by frustration when their apps break at scale, then either abandoning the project or hiring expensive developers to rebuild everything.
Ivan's story is different. He's a sales and marketing professional with zero coding background who started building an app for the fish keeping community in April 2025.
Four months later, he's launching a multiplayer app with real-time features, video recording, and social integration.
He built a team, he's shipping next week, and his code actually works.
Here's what he figured out that most vibe coders miss.
Use No-Code Platforms as Expensive Prototypes, Not Foundations #
Ivan started with Base44, then tried Lovable and Bolt when those platforms launched. Base44 won because it generated the cleanest UI. For the first few weeks, it felt like magic—just describe what you want, and the app appears.
The reality hit when he tried to export his code. Base44 had locked him into their platform through hidden dependencies. The UI components weren't even exportable without spending credits to have their AI regenerate the files. When he moved to Cursor, he had to rebuild most of the application from scratch.
But here's the surprising part: Base44 actually helped. It made critical technical decisions for him—React and Supabase—without forcing him into analysis paralysis. Those choices turned out to be solid for his use case. The platform gave him a working prototype that clarified exactly what he wanted to build.
The pattern that works: use platforms like Base44, Lovable, or Bolt for rapid prototyping and validation, but plan your migration to Cursor or another real development environment from day one. Treat these platforms as wireframing tools that cost money but save weeks of indecision.
Separate Strategic Planning from Tactical Execution #
Most vibe coders try to do everything in one tool. They open Cursor, describe a feature, and hope for the best. When the context breaks or the AI hallucinates, they start over, losing hours to trial and error.
Ivan developed a two-tool workflow that consistently produces working code:
ChatGPT handles all strategic planning. He describes the feature he wants to build, then iterates with ChatGPT until it's 95% confident in the approach. ChatGPT breaks the work into phases, writes detailed markdown specifications for each phase, and outputs implementation-ready prompts. Because ChatGPT connects to his GitHub, it maintains full context about his codebase, database schema, and existing features.
Cursor handles tactical execution. Ivan copies the phase-specific prompts from ChatGPT and pastes them directly into Cursor. Cursor follows the detailed specifications and writes the actual code. The separation prevents context fragmentation—ChatGPT maintains strategic awareness while Cursor focuses on implementation details.
The workflow is simple: ChatGPT plans, Cursor builds. When Ivan asked for a multiplayer PvP quiz feature with buzzer mechanics and text-to-speech narration, ChatGPT broke it into phases covering real-time synchronization, game state management, social recording integration, and platform-specific video formatting. Each phase came with acceptance criteria and rollback plans. Cursor executed those specs reliably because they were specific enough to follow.
Build Quality Assurance Into Your Workflow #
Here's Ivan's QA process that catches issues before they compound:
After Cursor executes a phase, he screenshots everything—all file changes, modification summaries, and implementation details. He pastes those screenshots back into ChatGPT with a simple prompt: "Examine this closely and see if there's anything that we need to improve or change or if Cursor did any mistake."
ChatGPT reviews Cursor's work against the original specifications and catches problems before they cascade through the codebase. It identifies logical errors, suggests optimizations, and flags potential issues that would surface during integration. Ivan takes those recommendations back to Cursor for fixes.
This AI-versus-AI review catches approximately 60% of bugs before they reach testing. The remaining 40% are usually JSX syntax errors or minor issues that surface quickly during manual testing. Across 30 projects I've worked with, this review pattern reduces debugging time significantly—founders spend less time hunting mysterious errors and more time building features.
Is it tedious to screenshot Cursor's output and feed it back to ChatGPT for every phase? Yes. Does it work? Also yes. Ivan is launching a multiplayer app with real-time synchronization as a non-technical founder. The tedious process produces reliable results.
Where Context Matters #
Ivan mentioned using Giga after hitting a pain point that most vibe coders experience: "AI hallucinating and didn't really know the context of it. You ask it to do one thing and it misunderstand you because there's no context."
Context fragmentation becomes critical once your codebase grows past the initial prototype phase. When your project includes dozens of files, database migrations, API integrations, and feature interdependencies, maintaining coherent context across conversations prevents the AI from introducing bugs or breaking existing functionality.
The workflow Ivan built—strategic planning in ChatGPT, tactical execution in Cursor, systematic reviews between phases—creates natural checkpoints where context stays aligned. Tools that explicitly manage context reduce the cognitive overhead of tracking what the AI knows versus what it needs to know for each task.
The Real Lesson #
Ivan went from zero technical background to shipping a multiplayer application in four months. He didn't learn React. He didn't master database design. He didn't become a developer.
He built a reliable process where AI tools complement each other's strengths and catch each other's weaknesses. Strategic planning stays separate from tactical execution. Quality assurance happens at every phase. The system produces working code consistently because it's designed to surface problems early.
That's the difference between playing with AI tools and actually shipping products. The tools themselves keep improving, but the workflow determines whether you're building something real or just generating code that looks good until someone tries to use it.