How I Built TripSathi — A Tinder for Travelers — and Won 3rd at Hacknuthon 5.0 published: false

Source: Dev.to

What I Built with Google Gemini

“Sathi” means friend in Hindi — your travel friend finder.

At Hacknuthon 5.0, held at Nirma University, our team set out to solve a problem every solo traveler knows: you’re visiting an incredible place, but you have no one to share it with. Dating apps mastered connecting people nearby. Why hasn’t anyone done that for travelers?

That’s how TripSathi was born — a Flutter app that works like Tinder, but for finding travel companions.

Core functionality

• Discover nearby travelers in real time – see active travelers on a live map, making spontaneous meetups possible.
• Swipe‑style matching – connect with people who share similar travel interests and destinations.
• Create or join travel groups – form groups for upcoming trips and let strangers join in.
• Share travel photo posts – a social feed of trip photos where others could opt to tag along next time.
• Real‑time chat – message matches or communicate within group chats to plan meetups.

Gemini‑powered features

1. Profile bio generation – users answer a few questions and Gemini crafts a natural, engaging bio automatically.
2. Smart traveler matching – Gemini analyzes interests, travel history, and location to surface compatible companions beyond simple proximity.
3. In‑app chat assistance – suggested conversation starters and on‑the‑fly meetup planning.
4. Travel recommendations – based on location and profile, Gemini surfaces local experiences and hidden gems to explore with new connections.

What I Learned

Technically, this hackathon pushed me hard. Integrating the Gemini API into Flutter while simultaneously building real‑time chat (Firebase) and live location features in a 48‑hour window was genuinely challenging. I learned to:

• Structure API calls efficiently and craft prompts that return UI‑ready responses cleanly.
• Manage state under pressure; messy state quickly becomes the biggest enemy when building fast.

On the soft‑skill side, ruthless prioritization was everything. We had to make hard calls about what made the demo and what got cut. Letting go of a feature you’re excited about, because the core experience matters more, is something no tutorial teaches.

The biggest unexpected lesson? Prompt engineering is a real skill. Early bio‑generation outputs were generic. Once we refined the prompt with tone, length, and personality context, the results became genuinely impressive.

Winning 3rd place validated that the idea resonated. That felt great.

Google Gemini Feedback

What worked really well

• Bio generation was the crowd favorite. Judges were surprised at how natural and personalized the outputs felt, and API response times were fast enough to feel seamless.
• Travel recommendations also stood out — Gemini understood location context well and gave suggestions that felt curated, not generic.

Where we hit friction

• Structured output consistency – for matching we needed Gemini to return specific JSON for the UI. The response format subtly shifted between calls mid‑hackathon, breaking our parsing logic. A more reliable structured‑output mode would have saved significant debugging time.
• Context length management in the chat assistant was tricky — balancing enough conversation history to feel coherent without bloating token counts required careful engineering under time pressure.

The honest take

Gemini is genuinely powerful. The output quality for natural‑language tasks is excellent. However, better Flutter SDKs, clearer documentation, and more predictable structured outputs would make the developer experience significantly smoother.

That said, would I use Gemini again? Without hesitation. TripSathi wouldn’t have been TripSathi without it.