From Hype to Hands‑On Reality
Earlier this year, our team decided to move past the hype and actually work inside the world of vibe coding.
Vibe coding, a term coined earlier this year to describe chat-based, AI-driven tools that generate deployable applications from natural language prompts, was everywhere. The promise was simple and seductive: go from idea to working app in hours, without writing traditional code. But instead of asking “Can it build an app?” we asked something more practical:
- Can you iterate with it, or is it one‑and‑done?
- Can you refine quality, structure, and experience over time?
- Can it support real product thinking, not just demos?
- And yes, can it meaningfully support accessibility along the way?
So we designed a real experiment: a one‑month, structured, iterative sprint using Vibe coding as our primary build method.
The One‑Month Vibe Coding Sprint
We gave ourselves 30 days. We met twice a week for structured co‑creation sessions, not just prompting, but planning, reviewing, breaking things, rebuilding, and refining. The goal wasn’t speed alone. The goal was to learn how this actually works in practice when treated as a product workflow rather than a novelty.
The Concept
We chose a problem space that was both practical and meaningful:
A fridge/freezer inventory app:
- Take a photo of your fridge or freezer
- Identify items automatically
- Flag what’s expiring soon
- Suggest meals based on available food
- Add quantities, expiration dates, scan barcodes, or manually add items
It had broad appeal (anyone who’s ever discovered a mystery container in the back of the fridge), but also real utility for people who are blind or low vision and can’t visually scan shelves. This gave us a perfect test case: something simple on the surface, but complex in execution.
Our Vibe Coding Toolkit
We tested multiple platforms:
- Gemini Canvas (Google)
- Lovable
- Bolt
- VO by Vercel
Some leaned towards low‑code and non-developer-friendly. Others were more developer‑centric. For our sprint, Lovable and Bolt became our primary tools; they were fast, intuitive, and well-suited for rapid iteration and prototyping without heavy engineering overhead.
(Side note: since our testing, some of these platforms have raised significant funding and evolved quickly, but the structural lessons from our sprint still hold.)
The Real Shift: From “Build Fast” to “Iterate Well”
The biggest learning wasn’t speed. It was iteration quality. Yes, vibe coding gets you from zero to something clickable very fast. But the real question is what happens after that first build:
- Can you refine flows?
- Can you improve the UX logic?
- Can you reshape information architecture?
- Can you improve performance?
- Can you improve accessibility?
- Can you integrate systems gradually?
The answer: Yes – but only if you treat it like a product process, not a magic trick.
We learned that vibe coding works best when structured as:
- Plan first (in chat mode)
- Build small
- Test immediately
- Refine prompts
- Iterate features
- Layer complexity gradually
- Introduce integrations late
This wasn’t linear; it was cyclical.
What We Actually Learned
1. Prompts Are Design, Not Instructions
Prompting isn’t “telling the AI what to do.” It’s designing system behavior through language. Short prompts produced inconsistent results. Overly long prompts led to a loss of coherence. The sweet spot was structured, intentional prompting:
- Clear goals
- Clear constraints
- Clear user context
- Clear priorities
Good prompts created stability. Bad prompts created chaos.
2. Iteration Beats Automation
The biggest mistake people make with vibe coding is treating it as automation. The real power comes from collaboration:
- You think → it builds → you test → you adjust → it refines → you shape
This loop matters more than the model. When treated as a collaborator instead of a generator, quality increased dramatically.
3. Structure Still Matters
Even with AI, product fundamentals didn’t disappear:
- Information architecture still matters
- Flow logic still matters
- Cognitive load still matters
- Error states still matter
- UX clarity still matters
Vibe coding doesn’t replace product thinking; it amplifies good product thinking and exposes weak product thinking.
4. Accessibility Became a Layer, Not the Core Driver
Accessibility was part of the sprint, but not its center. Out of the box, the builds had:
- Labels on buttons
- Logical screen‑reader order
- Keyboard navigation
Technically accessible, but not always usable. The experience improved only when we intentionally prompted for experience quality, not just compliance:
“Smooth screen reader navigation with minimal repetition”
“Descriptive item labels optimized for non‑visual scanning”
This showed us something important:
Accessibility in vibe coding isn’t automatic, but it is achievable when treated as a design requirement rather than a checkbox.
5. Staging Complexity Is Critical
Trying to connect everything at once broke things fast.
Our best build sequence was:
- Functional UI prototype
- Core features
- UX flows
- Accessibility layer
- Responsiveness
- Backend connections
- Data systems
Vibe coding works best in layers, not leaps.
6. Human Review Is Not Optional
AI output always needed refinement:
- Flow cleanup
- Code simplification
- Structural logic fixes
- UX smoothing
- Error handling
- Edge‑case handling
The model accelerated production, but humans ensured quality.
What This Sprint Changed for Us
Vibe coding didn’t replace design. It didn’t replace engineering. It didn’t replace strategy. It changed speed, feedback cycles, and iteration cost.
We could:
- Test ideas faster
- Kill bad ideas faster
- Refine good ideas faster
- Prototype complex concepts cheaply
- Explore multiple directions safely
That’s the real value.
Final Takeaway: From Hype to Practice
Vibe coding isn’t a no‑code fantasy. It’s not a magic wand. It’s a new product workflow.
Think of it as a co‑pilot, not an autopilot:
- Incredible for rapid iteration
- Powerful for early‑stage product shaping
- Transformative for prototyping cycles
- Dangerous if treated as automation
The real shift isn’t technical. It’s cultural: From building slower but safer → to building faster with smarter feedback loops. That’s what our one‑month sprint actually taught us. And that’s where the real opportunity lives.
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