Welcome to the official README for Gopher, an intelligent, seamless travel and event planning assistant I designed to dig up the best experiences so you don't have to.
This document outlines my vision for the project, the sophisticated tech stack I used, and the story of how I built it.

Planning a trip or a night out usually involves a dozen open tabs: maps, reviews, calendars, and event listings. I found that the mental overhead of coordinating logistics often takes the joy out of the journey itself.

Most AI assistants can tell you about a city, but they struggle with the "here and now"—the specific opening hours, the live events, and the spatial logic of a real-world itinerary.

I built Gopher to solve a simple problem:

What if an assistant didn’t just give you links, but actually understood the 'where,' 'when,' and 'how' of your plans?

I envisioned a platform that doesn't just chat, but actively burrows through real-time data to surface personalized itineraries, local hotspots, and live event information in one cohesive interface.

Gopher is more than a chatbot; it’s a specialized spatial AI agent I developed to act as a digital concierge.

By leveraging Large Language Models, I enabled Gopher to craft detailed, time-blocked itineraries based on your destination and preferences. Whether it's a 48-hour food crawl or a week-long nature retreat, Gopher organizes the day logically.

I integrated live search data so that Gopher finds concerts, festivals, and local happenings occurring during your specific travel dates, ensuring you never miss out on the pulse of a city.

I wanted users to be able to visualize their entire plan. Every recommendation is pinned on an interactive map, allowing you to understand the geography of your trip and optimize your travel routes to save time.

Gopher provides context beyond just a rating—summarizing what makes a venue unique and why it specifically fits your stated vibe.

I built this project using a blend of modern web technologies, high-speed inference, and geospatial APIs.

• Next.js – for a lightning-fast, SEO-friendly React environment with server-side rendering
• Tailwind CSS – for a clean, modern, and responsive "utility-first" UI design
• Lucide React – for consistent and crisp iconography

• Groq – utilized for ultra-low latency inference, allowing the AI to generate complex itineraries almost instantaneously
• Llama 3 / Mixtral – the core LLMs I used for natural language understanding and structured data extraction

• Google Maps Platform – handles Place Autocomplete, Geocoding, and interactive map rendering
• Google Places API – the source of truth for business details, photos, and reviews
• Serper / Search APIs – used to fetch the most current events and news that aren't in a model's static training data

• Vercel – for seamless CI/CD and hosting at the edge

I started with a focus on the "Information Gap." I realized LLMs are great at advice but bad at current locations. My first step was bridging a fast LLM (Groq) with real-world map data.

Integrating the Google Maps JavaScript API was the turning point. I ensured that as the AI "talked" about a place, the map would react, creating a dual-pane experience where text and location live in harmony.

Prompt engineering was key. I refined the system instructions to ensure Gopher returned structured data that I could then parse into custom UI components, like activity cards and time slots.

To move beyond a static directory, I integrated search tools to find "Events happening this weekend," making the app feel alive and reactive to the current moment.

Gopher is just beginning its journey. My roadmap includes:

• Collaborative Planning: Share a "Burrow" link with friends so everyone can add suggestions to a shared itinerary.
• Booking Integration: One-click links for restaurant reservations and flight tracking.
• Offline Mode: Exporting your itinerary to a lightweight mobile-friendly PDF or PWA for travel without data.

Thank you for exploring Gopher.
I’m dedicated to making travel planning as simple as a single conversation—one city, one plan, one burrow at a time.