OpenAI 2026 hackathon

Where To?

A pass-and-play family decision game that turns nearby-place picks into a fun knockout tournament.

Solo project by naif ali bin dair · 1 likes · 0 comments

View the project on Devpost

Archive position — measured, not model output

1 like on Devpost

505 of the 7,850 archived projects have more likes, and 1,758 share exactly 1 — so this project's #2,223 place in the like-ranked listing is a tie-break inside that group, not a ranking.

Projects (log scale)

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Likes on Devpost. ▲ marks this project's group.

Show the figures
LikesProjectsShare of archive
05,58771.2%
11,75822.4%
22843.6%
3–41321.7%
5–9751.0%
10+140.2%
Devpost like counts for all 7,850 archived projects, captured when this archive was built.

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Executive Summary

What the company appears to be

Where To? is a single-device, pass-and-play mobile app designed for families to make group decisions about nearby places to eat, go, or do things. It uses a tournament bracket system where participants select locations and then settle matchups via Rock / Paper / Scissors (RPS), with the final winner becoming the chosen plan.

What changed

The project is presented as a hackathon submission for the OpenAI 2026 hackathon. The author states it was built using Expo SDK 54, React Native, TypeScript, and integrates Google Places API (new) in live mode. It includes features like filtering by rating, animated bracket progression, and support for both demo and live modes.

Single most important open question

Is there any evidence of real-world usage or user feedback beyond the author’s own development and demo mode testing?

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What The Product Actually Is

The description states that Where To? is a single-device, pass-and-play mobile app. It allows users to:

  • Set up a family roster.
  • Choose nearby places (restaurants, cafés, things to do).
  • Filter by minimum rating (Any, 3.5+, 4.0+, or 4.5+).
  • Select one to three favorites per participant.
  • Merge shared picks and generate a live bracket.
  • Play Rock / Paper / Scissors matchups between representatives.
  • Advance through the tournament until one place remains.

The app supports both Demo Mode (with realistic fixtures) and Live Mode (using Google Places API). It also includes features like animated reveals, random draw resolution, and direction/call sharing.

Inference The app is built for small group decision-making in physical proximity, with a focus on simplicity and fun.

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Positioning & Claim Evolution

The author claims that the app turns “family decision-making” into a fun, fair, and quick process, addressing the problem of low-stakes debates where the loudest voice wins.

Inference The positioning is rooted in solving a common family dynamic — not a product for large-scale or enterprise use. It emphasizes ease-of-use, engagement, and group participation.

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Target Customer & ICP

The description states that Where To? is designed for families making group decisions about nearby places. The app is intended to be used on phones already in the possession of family members, with a pass-and-play structure where each person selects options and then plays RPS.

Inference The target customer is likely a family or small group, not an individual user or business. The ICP appears to be parents or caregivers seeking a structured way to make group decisions without conflict.

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Business Model & Pricing Evidence

The description does not mention any pricing, monetization, or business model. It only states that the app is open-source (MIT License), and includes setup instructions and fixture data guidance.

Not evidenced No evidence of revenue streams, subscriptions, or paid features.

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Technical & Delivery Signals

The project was built using:

  • Expo SDK 54
  • React Native
  • TypeScript
  • Expo Router
  • React Context and Reducers
  • AsyncStorage
  • Expo SecureStore
  • Expo Location
  • Google Places API (new)
  • React Native Reanimated
  • Codex and GPT-5.6

The author notes that Codex and GPT-5.6 were used to accelerate development, including dependency upgrades, debugging, UI iteration, and architecture refinement.

Inference The tech stack is standard for a mobile app built with React Native and Expo. The use of AI tools suggests a developer-centric approach to rapid prototyping.

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Traction & Maturity Signals

The project is described as a hackathon submission, not a product in production. It includes:

  • Demo Mode enabled by default
  • A public GitHub repository
  • MIT License
  • Setup instructions and fixture data guidance

Not evidenced No evidence of user adoption, customer base, or real-world usage beyond the author’s own testing.

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Competitive Context

The description does not mention any competitors. It is unclear whether similar apps exist in the market for group decision-making or family planning tools.

Not evidenced No competitive analysis or market positioning relative to existing solutions.

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Open Questions & Risks

  • The app is presented as a hackathon submission, not a product with traction.
  • No evidence of real-world usage, user feedback, or adoption.
  • No pricing, monetization, or business model described.
  • The app relies on Google Places API for live mode, which may introduce dependency risks or cost concerns.
  • The use of AI tools (Codex, GPT-5.6) suggests a developer-focused approach, not necessarily a scalable product.

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Questions the description leaves open

  1. What is the intended path from this hackathon prototype to a production-ready product?
  2. Are there any plans for monetization or user acquisition beyond demo mode?
  3. Has the app been tested with real families or users, and what feedback was received?
  4. How does the team plan to scale beyond a single developer?
  5. What are the technical limitations of relying on Google Places API for live functionality?

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Overall Assessment

Not evidenced No evidence of revenue, customers, or traction exists in the description.

This is a developer prototype, not a commercial product. It shows potential as a concept but lacks any indication of real-world adoption or business viability.

Confidence Level Low — based entirely on self-reported claims and no external validation.

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Source

Submitted to the OpenAI 2026 hackathon on Devpost. Project home on Devpost. Back to the full gallery on Devpost.

This competitive analysis was produced by top-founders.com (opens in a new tab)

Generated by a language model from this project's public Devpost page — its tagline, the author's own write-up and any built-with tags listed there — as captured in August 2026. Short quotations are the author's words. It is an opinion on the idea as a business, not on the people behind it, and it reflects the description as published then. If the project has moved on, request a correction below. The analysis is not independent research and contains no verified traction, revenue or customer data.

An independent guide by KFG International LLC — not affiliated with, endorsed by, or connected to Devpost or OpenAI.

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