OpenAI 2026 hackathon

Poopi

Poopi helps you find the best bathroom nearby, then makes every visit useful. Explore a live map, check access details and photos, log your experience, and rank restrooms with the community in a tap.

Solo project by Colin Hu · 0 likes · 0 comments

Archive position — measured, not model output

0 likes on Devpost

2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #6,025 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
05,59271.2%
11,75822.4%
22853.6%
3–41321.7%
5–9751.0%
10+140.2%
Devpost like counts for all 7,856 archived projects, captured when this archive was built.

Executive Summary

What the company appears to be

Poopi is a self-reported project that claims to help users find the best nearby bathroom, with features like live map exploration, access details, photos, experience logging, and community ranking. It was submitted as a hackathon entry for the OpenAI 2026 hackathon.

What changed

The description indicates this is a new product concept, likely in early development or prototype stage, based on its submission to a hackathon and lack of any evidence of traction or revenue.

The single most important open question

Is there any evidence that Poopi has achieved product-market fit, user adoption, or monetization potential beyond the initial idea?

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What The Product Actually Is

The description states that Poopi is a tool that helps users find nearby bathrooms and makes each visit useful. It includes features such as:

  • Live map exploration
  • Access details and photos
  • Logging of experience
  • Ranking restrooms with the community via a tap interface

It was built using Codex, Sol 5.6, and Supabase.

Confidence Low — this is self-reported functionality without verification or demonstration.

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Positioning & Claim Evolution

The author states that Poopi helps users find the best bathroom nearby and makes every visit useful. It also includes a formula for ranking bathrooms based on multiple factors:

$$

R = w_dD + w_qQ + w_pP + w_fF + w_wW

$$

Where:

  • D = proximity
  • Q = quality
  • P = preference/accessibility match
  • F = freshness of information
  • W = expected wait time

Confidence Low — this is a conceptual positioning, not evidence of execution or traction.

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Target Customer & ICP

The description does not provide explicit information about the target customer or ideal customer profile (ICP). It implies a general user base interested in finding and rating bathrooms, but no segmentation or persona details are given.

Confidence Very low — no evidence of defined customer segments or personas.

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Business Model & Pricing Evidence

There is no evidence provided regarding business model or pricing. The description does not mention monetization strategies, subscription tiers, or any commercial framework.

Confidence Not evidenced — no indication of how the product will generate revenue.

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Technical & Delivery Signals

The project was built using:

  • Codex
  • Sol 5.6
  • Supabase

It was submitted to a hackathon (OpenAI 2026), suggesting it may be in early development or prototype form.

Confidence Low — technical stack is mentioned, but no evidence of scalability, performance, or delivery maturity.

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Traction & Maturity Signals

There is no evidence of traction, user adoption, or product maturity beyond the hackathon submission. No data on users, engagement, or growth metrics are provided.

Confidence Not evidenced — no signs of real-world usage or product development progress.

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Competitive Context

The description does not provide any information about competitors or market context. It is unclear whether similar products exist in the marketplace.

Confidence Not evidenced — no competitive analysis or positioning relative to other tools.

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Key Risks & Red Flags

  • No traction or revenue evidence: The project appears to be in early conceptual or prototype stage.
  • Unverified claims: All features and functionality are self-reported without demonstration.
  • Lack of customer data: No indication of user feedback, usage, or validation.
  • Limited team size: Only one team member is mentioned, which may limit execution capacity.

Confidence Low — risks are inferred from absence of evidence rather than stated facts.

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Diligence Questions To Ask The Founders

  1. What specific problem does Poopi solve that existing solutions do not?
  2. How many users have engaged with the prototype or early version?
  3. What is the current stage of development (e.g., MVP, prototype, beta)?
  4. Have you conducted any user research or feedback sessions?
  5. Is there a plan for monetization or revenue generation?
  6. What are the key assumptions behind the ranking algorithm?

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Investment/Partnership Verdict

Not evidenced — no data to assess commercial viability, traction, or scalability.

Confidence Very low — this is an early-stage idea with no demonstrated product-market fit or business model.

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Source

Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.

The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.