Search by State Trends was an easy way to test AI as a pair programming partner to accelerate the development of a Minimum Viable Product (MVP). This outlines our experience using Vercel v0, an AI coding assistant, and the key areas where its assistance proved particularly valuable in reducing development time.
Search by State Trends is a web application that visualizes and summarizes Google search trends by state in the United States. (We parse the Google Trends "Export" to "XML" feeds when searched by state. Only a hobby learning project, using standard browser methods).
Our goal was to create a functional MVP that could:
- Display an interactive map of the US
- Fetch real-time trend data for selected states
- Provide AI-generated summaries of the trends
Working with Vercel v0, we were able to complete the MVP in approximately 48 hours, a process that might have taken a week or more using traditional development methods.
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Remote Team Collaboration: During virtual meetings, quickly look up what's trending in your colleagues' locations to spark conversation or understand local contexts.
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Empathy Check: Stay informed about potential local issues, bad news, or severe weather events affecting your contacts or clients in different states.
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Content Creation: Gain insights into region-specific interests for targeted content creation or marketing strategies.
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Cultural Awareness: Explore trending topics across different states to better understand diverse perspectives and current events across the country.
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Ice Breaker: Use as an interesting conversation starter in meetings or social gatherings.
- Frontend: React with Next.js
- Backend: Next.js API routes
- Map Visualization: react-simple-maps
- Trend Data: Google Trends RSS feed
- AI Summary: OpenAI GPT-3.5
The current architecture of Search by State Trends is designed for simplicity and real-time data processing:
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Data Fetching: The application fetches real-time data from the Google Trends exported RSS feed for each selected state.
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State Selection: Users interact with an interactive US map to select a state of interest.
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Trend Processing: When a state is selected, the backend processes the RSS feed data to extract relevant trend information.
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AI Summary Generation: The extracted trend data is sent to OpenAI's GPT-3.5 model, which generates a concise summary of the trending topics.
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Display: The frontend displays both the raw trend data and the AI-generated summary to the user.
This architecture allows for quick, on-demand access to current trend data without the need for a persistent database or complex data storage solutions.
Vercel v0: Key Contributions once Presented with the Above Plan
While the initial concept was human-driven, Vercel v0 helped refine our project structure and architecture. By describing our goals, we received validations for:
- Implementing a React + Next.js frontend for performance and SEO benefits
- Utilizing Next.js API routes for backend functionality
- Structuring our components for maintainability and reusability
This guidance saved us several hours of research and decision-making time.
Vercel v0 assisted in designing a clean and efficient API for our application. For example, it helped formulate the endpoint structure. And I'm recalling here that it's been written that one of the biggest challenges in generating clean code is naming things. v0 also provided guidance on error handling and response formatting, ensuring our API was robust and user-friendly. This assistance cut down API development time by approximately 50%.
A crucial aspect of our project was integrating the OpenAI API for generating trend summaries. Vercel v0 was instrumental in:
- Crafting effective prompts for the OpenAI API
- Implementing error handling and fallback mechanisms
- Optimizing API calls to balance cost and performance
Here's an example of a prompt we developed with v0's assistance:
const prompt = `Summarize the following trending topics in ${locationName}: ${trendsSummary}. Provide insights into why these topics might be trending and their potential significance.`;
v0's expertise in prompt engineering saved us countless hours of trial and error, allowing us to achieve high-quality summaries much faster than we could have on our own. We added prompting to hopefully avoid the tone of generated content, and adjusted the temperature property for more insightful language.
To ensure our application was mobile-friendly, we relied on Vercel v0 to implement responsive design patterns. This included:
- Suggesting Tailwind CSS classes for responsive layouts
- Helping create a responsive navigation component
- Ensuring that the US map and trend cards scaled appropriately on different devices
Example of a responsive component structure suggested by v0:
<div className="w-[90%] sm:w-full max-w-4xl mx-auto">
{/* Component content */}
</div>
v0's proficiency with Tailwind CSS and responsive design principles saved us approximately 30% of the time we would have spent on styling and responsiveness.
Throughout the development process, Vercel v0 proved to be a valuable debugging partner. It helped identify issues in our code, suggested potential solutions, and explained the reasoning behind these solutions. This significantly reduced the time spent on troubleshooting, often cutting debugging time in half. Half of the bugs needed my redirection for solutioning, which v0 took feedback well on. This should be encouraging to existing software engineers that you won't be replaceable anytime soon and coding copilots should be used as a pair programming partner that your human peers are unavailable for.
- Rapid Prototyping: v0's suggestions allowed us to quickly implement and iterate on features, accelerating our MVP development by an estimated 40%.
- Knowledge Augmentation: v0 filled knowledge gaps in areas where team members had less expertise, such as specific React hooks or Next.js features, saving time on documentation lookups and learning curves.
- Code Quality: v0's recommendations often included best practices and optimizations that improved our overall code quality, potentially saving future refactoring time.
- 24/7 Availability: Unlike human team members, v0 was available around the clock, allowing for continuous development and problem-solving.
While pair programming with Vercel v0 was largely beneficial, we did encounter some challenges:
- Contextual Understanding: Sometimes, v0 lacked full context of our project, requiring additional clarification. However, its ability to quickly adapt to new information minimized this issue.
- Keeping Up-to-Date: We occasionally needed to correct v0 on the latest best practices or syntax changes in our tech stack, though these instances were rare.
- Over-Reliance: We had to be cautious not to over-rely on v0's suggestions and maintain critical thinking about implementation decisions.
Our experience with Vercel v0 in developing Search by State Trends demonstrated its potential to significantly accelerate MVP development. By leveraging v0 for everything from architecture decisions to code implementation and debugging, we were able to bring our product to life in a fraction of the time it would have taken using traditional methods alone.
We estimate that using Vercel v0 as a pair programming partner reduced our overall development time by approximately 60%. This dramatic time saving allowed us to move from concept to functional MVP in just two days, a process that might have taken a week or more without v0's assistance.
As we move forward, we plan to further integrate Vercel v0 into our development workflow, learning from both its strengths and limitations to create even more efficient and innovative development processes. The potential for AI-assisted development to revolutionize how we build and launch products is clear, and we're excited to be at the forefront of this new frontier in software development.
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Clone the repository:
git clone https://github.com/dfrho/trends-summary.git
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Install dependencies:
cd search-by-state-trends npm install
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Set up environment variables:
- Copy the
.env.example
file to.env.local
:cp .env.example .env.local
- Open
.env.local
and replaceyour_openai_api_key_here
with your actual OpenAI API key:OPENAI_API_KEY=sk-your-actual-api-key
Note: Make sure to keep your API key confidential and never commit it to version control.
- Copy the
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Run the development server:
npm run dev
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Open http://localhost:3000 in your browser to see the application.
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE.md file for details.
- Google Trends exported RSS feed for the trending data
- OpenAI for the GPT-3.5 model used in generating summaries
- React Simple Maps for the interactive US map visualization
This project is not affiliated with, endorsed, or sponsored by Google. It uses publicly available data from Google Trends client-exported RSS feed.