Vibe Coding: Rapid Prototyping with AI in 2026
AI-assisted development has crossed a threshold. Here's how I went from idea to working prototype in days — and what it means for how products get built.
Twelve months ago, "vibe coding" was a meme. Today it is how I build every prototype.
I want to be precise about what that means — and honest about where it breaks down.
What Vibe Coding Actually Is
Vibe coding is not asking an LLM to write your entire application and shipping whatever comes out. That approach produces something that looks like software but fails at the edges in ways you won't discover until a user finds them.
Vibe coding is rapid, AI-assisted iteration where the developer stays in control of the architecture and reviews every output critically.
The workflow looks like this:
- Define the problem and the constraints clearly (in your head or in writing)
- Describe the component or function to the model with enough context
- Review the output — understand what it does, not just that it runs
- Integrate, test, and iterate
The model handles boilerplate, syntax recall, and the mechanical translation of logic to code. You handle decisions, architecture, and quality.
What Changed in 2026
Three things shifted that made this genuinely viable for production work:
Model quality crossed a threshold. The gap between "this looks right" and "this is correct" used to be wide. Today's frontier models make far fewer subtle logic errors. They still make them — but the review burden is lower.
Context windows got large enough to matter. When you can give a model your full component file, its dependencies, and your design system in a single prompt, the outputs are dramatically better. Partial context produces partial outputs.
Tool integration improved. The combination of Claude in an IDE, GitHub Copilot for inline completion, and a dedicated AI coding agent for larger tasks means there is now a tool for every layer of the workflow.
A Real Example: Building Meowtopia
Earlier this year I built Meowtopia — a Flutter mobile app for cat owners in Hong Kong — as a side project. The MVP includes:
- A Flame-powered game world home screen
- Supply tracking with barcode scanning
- A Supabase backend with Row Level Security
- OneSignal push notifications via a pg_cron Edge Function
- Three app flavors (dev, staging, prod)
The architecture decisions, the Supabase schema design, and the RLS policies — I designed and reviewed all of these. But the implementation of each feature? I wrote maybe 30% of the code directly. The rest was AI-assisted iteration.
The result: a functional MVP in weeks, not months.
Where It Still Breaks Down
I want to be honest about the failure modes, because they matter for anyone taking this seriously.
Complex state management. When the data flow gets intricate — multiple providers, async cascades, optimistic updates — models start producing code that looks plausible but has subtle race conditions or incorrect dependency arrays. You need to understand the underlying concepts deeply enough to catch these.
Security-sensitive code. Never let a model write authentication logic, RLS policies, or encryption without your full review. Models have been trained on a lot of insecure code. They reproduce patterns that look fine and are subtly wrong.
Testing. Models write tests that test the code they wrote, not the behaviour the code should have. You have to specify what to test, not just ask for tests.
Long-running context drift. In a long conversation, models start making assumptions based on earlier context that no longer applies. Start fresh sessions for new components.
The Skill That Matters
The highest-leverage skill in 2026 is not knowing which AI tool to use. It is knowing enough about what you are building to evaluate what the AI produces.
A developer who deeply understands Flutter, Dart patterns, and Supabase will use AI to build 5x faster. A developer who does not will build something fragile and not know it.
Vibe coding raises the ceiling. It does not remove the floor.
What This Means for Product Development
The cost to build a working prototype has dropped dramatically. This changes the economics of product validation — you can test more ideas, faster, with less capital.
For solo founders and small teams in Singapore and Hong Kong, this is a real competitive advantage. The window to build and test before a market is crowded is shorter than it used to be. AI-assisted development compresses that window in your favour.
The question is not whether to use these tools. The question is how to develop the judgment to use them well.