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During Yeti’s 2025 AI Hackathon, our teams set out to push the limits of today’s AI development tools—challenging ourselves to see just how far no-code AI could take us in building real, functional digital products. The rules were simple: in just one day, create a working product prototype using only AI-powered, no-code platforms.

We ended up with three distinct prototypes, hit some unexpected roadblocks, and uncovered surprising strengths (and weaknesses) in the vibecoding tools we used. But in the end, the most valuable outcomes weren’t the prototypes themselves—they were the lessons we learned along the way.

What Vibecoding Taught Us About AI’s Real-World Limits

Start With Structure

The teams that spent time upfront defining their app’s structure, goals, and logic had a much smoother experience overall. Tools like CodeGuide helped teams generate product specs, user flows, and database models before they ever opened a development environment. This foundation made it easier to craft clear prompts, avoid redundant work, and minimize the trial-and-error cycles that bogged other teams down. In short: the more you plan, the better the AI performs.

Modularity Matters

AI is great at generating big blocks of code quickly—but not always with clarity or maintainability in mind. Several teams ran into issues when entire apps lived in single, sprawling files with tangled logic and limited separation of concerns. Without modular, component-based thinking, projects quickly became difficult to navigate, debug, or scale. Future use of AI dev tools will require guardrails to ensure cleaner file structure, reusable components, and scalable architecture.

AI Can’t Replace Devs—Yet

While AI tools accelerated development, they didn’t eliminate the need for skilled developers. Human judgment was critical at every step—from breaking down problems into promptable chunks to validating logic, resolving errors, and managing architecture. AI served as a powerful assistant, not an autonomous builder. Its output was often impressive, but it still required curation, editing, and oversight from experienced humans.

Prototyping Has Changed Forever

One of the most exciting takeaways was how easily teams could spin up demos and validate ideas. From a fully functioning Disneyland itinerary planner to a multiplayer game experience, AI enabled teams to go from concept to tangible output in days—not weeks. This makes AI tools ideal for client discovery work, early-stage startups, and internal concept testing. We can now show instead of tell—faster and cheaper than ever before.

Design Still Needs a Seat at the Table

Most AI tools we used focused heavily on functionality—but left much to be desired in terms of visual polish and branding. Even with tools like V0 and Lovable generating decent-looking UIs, there was little support for enforcing a design system, integrating a brand identity, or customizing themes. This reaffirmed the value of design expertise in the product development process, and suggested that pairing AI tools with design tokens or component libraries could elevate output significantly.

The hackathon reminded us that AI development tools are accelerators—not autopilots. While they can drastically speed up workflows, reduce costs, and empower non-developers to create, they still require a strong foundation of human insight, technical structure, and creative vision.

At Yeti, we’re excited to continue exploring the evolving AI dev landscape. Armed with these lessons, we’re better equipped to guide clients through faster prototyping, smarter decision-making, and more accessible digital product development.

Yeti’s 2025 AI Hackathon was more than a fun experiment—it was a window into the future of product development. By embracing AI development tools, we’ve seen firsthand how these technologies can reduce costs, spark innovation, and accelerate delivery for our clients. We also uncovered their limits—and the importance of thoughtful prompting, scope definition, and human oversight.

This is just the beginning. As these tools evolve, so will our ability to harness them for more complex, collaborative, and creative projects. Whether it's helping a non-developer build their first app or spinning up a full MVP in days, AI is no longer just a tool in our toolbox—it’s a catalyst for what’s next.