This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend* also i have done this with my friend Fawaz
What I Built
I built DatePilot, a private AI date optimizer for couples.
The idea came from a simple real problem: planning a date for two people is weirdly hard. One person likes quiet cafes, the other wants something outdoors, both have a budget, timing, food limits, travel limits, and nobody wants to turn the date into a logistics spreadsheet.
DatePilot lets two partners enter their preferences separately, then creates one shared date plan that fits both people. It finds overlapping tastes, keeps private answers hidden, and generates a real itinerary with:
- matched cuisines, vibes, and activities
- Chennai/Tamil Nadu venue options
- cost breakdown
- time window checks
- travel estimates
- opening-hour checks
- dietary constraints
- backup venues
- swap-stop support
It is built for someone who wants the date to feel thoughtful without giving away the surprise or exposing private taste data.
Demo
Live demo:
Backend API:
https://datepilot-api.onrender.com
Video demo:
Code
GitHub repo:
(https://github.com/sudarsan2507-hue/DatePilot)
How I Built It
DatePilot is a full-stack app with a deterministic planner and an open-model AI layer.
The frontend is built with React, Vite, and Tailwind, designed mobile-first so Partner B can comfortably open the invite link on a phone. The interface keeps the tone warm and romantic, but the workflow is practical: create a session, enter preferences, review taste cards, compare overlap, then generate a plan.
The backend is built with Python, FastAPI, Pydantic, and SQLite. The planner itself is pure Python. It does not let the LLM decide prices, schedules, or constraints. Instead, deterministic code checks the real rules:
- total cost must fit the budget
- venues must be open during the planned slot
- travel between stops must fit the max travel limit
- dietary constraints must be respected
- excluded dislikes are filtered out
- the whole date must fit the selected time window
The AI is used only where it is useful: understanding taste. The app is designed around an open-weight model wrapper using Gemma through Ollama, with environment variables for model name and base URL so it can run locally or be swapped for a hosted open-weight endpoint.
The memory layer is SQLite-backed through a separate interface so future taste learning can be plugged in cleanly.
I also added optional no-key public APIs:
- OpenStreetMap / Overpass for venue discovery
- OSRM for route and travel-time estimates
- Open-Meteo for rain-aware planning
The venue planner currently works for India in the UI, with Tamil Nadu cities supported internally.
Why Does Open Innovation Matter?
Open innovation matters because this app is built around private taste.
A date planner is not just asking for generic preferences. It can touch food habits, comfort zones, location patterns, aesthetics, budget comfort, and personal likes or dislikes. That data should not have to leave the user’s control just to get a thoughtful recommendation.
Using an open-weight model makes DatePilot feel different from a normal closed API app:
- taste extraction can run locally
- raw uploads can be deleted immediately after processing
- only the confirmed taste card needs to be stored
- the model wrapper can be swapped without redesigning the app
- the deterministic planner remains auditable
- sensitive inference categories can be intentionally excluded
The important part is that the AI does not become a black box that secretly decides the date. The model helps understand messy human taste, while transparent code handles the math, constraints, and safety checks.
That combination is exactly why open models matter here: they make AI personal without making it invasive.
My Agent Session
I used an AI coding agent codex during the build to iterate on the planner, frontend, deployment, API integration, and demo video. The agent helped debug real issues like failed plan generation, partner submission state, Render deployment behavior, and mobile sharing.
Prize Categories
I am entering:
- Open-source AI / open-weight model category
- Build for a Friend
- Full-stack app
- Agent-built project
- Deployed demo This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend
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