Last weekend, for testing different AI tools, I did “vibe coding”, a low-pressure projects just for the fun of testing and building something new, decided to build a simple flashcard application
The goal was simple: provide the same prompt to three different AI tools and see which one could produce a functional, usable app with the least amount of friction.
The Setup
- The Project: A simple web-based flashcard app. Nothing too complex, but it requires a front-end, some basic state management (flipping the card), and a way to load questions and answers.
- The Prompt: To keep the test fair, I used a single, detailed prompt for all three tools. I spent a bit of time refining this prompt with Google Gemini to ensure it was clear, specific, and covered all the core requirements. A good prompt is half the battle in the world of GenAI. – https://docs.google.com/document/d/1aiiukFWbBOvGH03BArlVxsOC_CbkSd7bBTRucthjl14
- The Contenders:
- Google AI Studio: http://aistudio.google.com – A web-based tool for prototyping with Google’s latest generative models.
- Replit: http://replit.com – An online IDE famous for its collaborative features and, more recently, its AI coding assistant.
- Emergent: http://emergent.sh – A newer player that markets itself as an AI-powered integrated development environment.
- The Criteria for Success: I judged them on four key metrics:
- Output Quality: Did the generated code work? Was it close to what I asked for?
- Time: How long did it take from prompt to a usable (or unusable) result?
- Intervention: How many clicks, follow-up questions, or manual fixes were needed?
- Deployment: Could I easily get the app online?
Contender #1: Google AI Studio
Right out of the gate, Google AI Studio set an incredibly high bar.
- Output Quality (Score: 9.5/10): The result was almost flawless. The generated code was clean, well-structured, and worked exactly as intended. I’d estimate it delivered 95% of what I envisioned directly from the prompt. It was a fully functional flashcard app, ready to go.
- Time (Score: 10/10): This was, by far, the fastest tool. The time from pasting the prompt to having deployable code was impressively short.
- Intervention (Score: 8/10): My interaction was minimal. While it required a few more clicks than Replit’s “one-shot” generation, the process was straightforward. The minimal intervention was a small price to pay for a perfect output.
- Deployment (Score: 10/10): This is where AI Studio truly shone. It offered a single-click deployment straight to Google Cloud Run. Within minutes, my app was live on the internet with a public URL. This seamless “code-to-cloud” experience is a game-changer.
Verdict: The Clear Winner. Google AI Studio felt less like a code generator and more like a junior developer who understood the assignment.
Check out the result for yourself:
- Live App: https://flashcardapp.balaganesh.org
- GitHub Repo: https://github.com/balaganesh/flashcardapp
Contender #2: Emergent
Emergent was an interesting case. It took a different, more conversational approach, but the results were mixed.
- Output Quality (Score: 6/10): The generated application was usable, but riddled with bugs. It felt like a promising first draft that would need significant refactoring. However, Emergent deserves a major shout-out for one thing: it was the only tool that correctly understood a nuanced part of my prompt regarding an API input option. Its contextual understanding seems to be a real strength.
- Time (Score: 3/10): This was the most time-consuming process of the three. Its interactive nature meant I was constantly answering follow-up questions and clarifying requirements.
- Intervention (Score: 2/10): The level of intervention was extremely high. The back-and-forth Q&A model, while potentially powerful, felt slow and laborious for this particular task.
- Deployment (Score: 1/10): I didn’t proceed with deployment. The app was too buggy to be worth it, and the platform required additional credits to deploy.
Verdict: The Promising but Unpolished Contender. Emergent has potential, especially in its ability to grasp complex requirements. However, the buggy output and slow process place it firmly in second place for this experiment.
Contender #3: Replit AI
Given Replit’s reputation, I had high hopes. Unfortunately, for this specific task, it fell short.
- Output Quality (Score: 1/10): The output was simply unusable. While it generated a file structure and code, the core functionality of the flashcard app was broken. It produced something, but it wasn’t what I asked for. It seemed to struggle with the detailed, multi-faceted prompt I provided.
- Time (Score: 5/10): The generation process itself was quick, making it the second-fastest tool. However, speed is irrelevant if the final product doesn’t work.
- Intervention (Score: 9/10): This was the area where Replit excelled. It required the least intervention—essentially just one click to get the code. The problem was that the code it produced wasn’t useful.
- Deployment (Score: 0/10): No deployment, as the application was non-functional.
Verdict: The Disappointment. Replit AI might be excellent for autocompletion or smaller, in-context tasks, but for generating a complete project from a detailed prompt, it wasn’t the right tool for the job this time.
Final Results at a Glance
| Criteria | 🥇 Google AI Studio | 🥈 Emergent | 🥉 Replit |
| Output Quality | Excellent (95% Accurate) | Usable, but buggy | Unusable |
| Time Taken | Least | Most | Second Most |
| Intervention | Minimal Clicks | Very High (Q&A) | Least (One-Click) |
| Deployment | Yes (1-Click on Cloud Run) | No (Bugs/Credits) | No (Non-functional) |
This weeks experiment was eye-opening. While we hear a lot about “AI coding,” this test proves that not all assistants are created equal. The difference in output quality between the tools was staggering.
For the task of generating a complete, functional, and deployable application from a single, well-crafted prompt, Google AI Studio was in a class of its own. The combination of high-quality code generation and seamless integration with deployment services like Cloud Run represents a massive leap in developer productivity.
My key takeaway? The future of AI in development isn’t just about generating snippets of code; it’s about understanding intent, managing complexity, and shortening the path from idea to live application. For this round, Google is leading the pack.
Have you run similar experiments? What are your go-to AI tools for coding, and what have your results been? I’d love to hear about in the comments.
P.S. This blog post was drafted and refined with help from Google Gemini.