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A group trip can stall even when everyone wants to go: someone still has to choose dates, compare ideas, and ask the group to commit. Prince Singh built Trip Buddy to take on that coordination work. His prototype checks upcoming long-weekend windows, proposes three trips, emails each person a vote, and treats four votes from its six-person group as a decision. The demo used test inboxes, not the real friend group, so it is a design experiment—not evidence that the tool has ended anyone’s indecision.
The problem Trip Buddy is designed to solve
Singh describes a familiar group-chat loop: friends say they should go somewhere over a long weekend, but no one takes responsibility for turning that intention into a plan. As he puts it, “The problem was never a lack of places to go. It was that someone has to take the lead, and nobody wants to be that person every time.”
Trip Buddy tries to make that lead-taking automatic. Instead of waiting for a volunteer to suggest dates and chase replies, it prepares a proposal ahead of a holiday-connected weekend and sends each group member a direct vote. The idea is to move the conversation from an open-ended “where should we go?” to a small set of options people can accept or reject.
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How the prototype’s planning flow works
- Find a candidate break. The project checks Indian central-government holiday lists for 2026 and 2027, looking for holidays connected to weekends and possible bridge leave. Singh’s example is a Tuesday Dussehra holiday in 2026 paired with Monday leave to make a four-day break. That is the project’s described example, not an independently verified calendar or a date recommendation for every traveler.
- Generate a short shortlist. For each opportunity, the prototype creates three options with different characters: hills, spiritual or nature-focused travel, and heritage. Each option includes a day-by-day plan.
- Collect individual votes. The system emails each of the six people a vote link and displays a tally. In the described configuration, four votes for one option are enough to declare it decided.
- Send the proposal on a schedule. Singh says the email is scheduled for 21 days before a long weekend, giving the group time to respond and make arrangements.
The project is specific to Singh’s group: six friends departing from Delhi, using central-government holidays, and skipping Diwali because they do not travel over it. It also treats food as a hard constraint because three group members are vegetarian, while leaving other preferences more flexible. Those are choices for this group rather than universal settings.
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What code does—and what the model does
Singh’s guiding principle is “The model plans, the code enforces.” In his account, ordinary application code handles date windows, bridge-leave calculations, preference aggregation, vote counting, scheduling, and checks that generated output has the expected structure. A local language model supplies destination and activity ideas and drafts the day-by-day plans.
That division matters because the two kinds of work have different reliability needs. A vote threshold or date calculation should follow an explicit rule. A model can help produce varied suggestions, but its itinerary is not a verified travel document. Singh reports that the model once gave an unrealistic travel-time estimate for Rishikesh, which he corrected. He says travel times in the plans are estimates, not booking facts.
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What the demo does and does not demonstrate
The prototype’s voting flow was demonstrated with six test email aliases, and Singh cast the displayed votes himself. He says the real friend group had not received the messages. The demo therefore shows how the workflow can operate; it does not show that the group used it, chose a destination, or made a booking.
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Singh also describes several development snags that reveal where the system needed clearer boundaries:
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- An early scoring rule preferred an ordinary weekend over a five-day opportunity in April 2027; he revised the scoring function.
- A model call took so long without visible output that it timed out; he switched to streaming and disabled hidden reasoning.
- Generated travel times were not reliable enough to use without checking, so he clarified the prompt.
- Retries for small budget or timing overruns created unnecessary work; he changed retries to focus on broken output structure.
These are the builder’s reported experiences, not results from an independent code review or a user trial. The useful design lesson is narrower than “AI can plan your trip”: automate the predictable coordination rules, and verify generated logistics before relying on them.
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Singh reports building the prototype with Node.js, Express, Nodemailer, Gmail app-password email, a Cloudflare quick tunnel, and Gemma 4 through Ollama. He says the setup required no paid API or database. Those are his project details, not independently tested cost or security claims.
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- The machine has to stay available. The laptop running the prototype must be on for scheduled work and voting to function.
- Vote links can change. Singh notes that the quick-tunnel URLs change when the tunnel restarts, which can invalidate previously sent links.
- Holiday calendars may not match. Central-government holidays do not necessarily line up with each friend’s employer calendar or leave policy.
- Itinerary details need verification. Model-generated travel times and activities should be checked against reliable, current information before booking.
Singh lists a preference form, a winner notification, support for friends traveling to Delhi, and grounding travel-time estimates with a search API as possible next steps. They are ideas for further development, not features established in the described demo.
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