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 #3,300 place in the like-ranked listing is a tie-break inside that group, not a ranking.
Projects (log scale)
Likes on Devpost. ▲ marks this project's group.
Show the figures
| Likes | Projects | Share of archive |
|---|---|---|
| 0 | 5,592 | 71.2% |
| 1 | 1,758 | 22.4% |
| 2 | 285 | 3.6% |
| 3–4 | 132 | 1.7% |
| 5–9 | 75 | 1.0% |
| 10+ | 14 | 0.2% |
Executive Summary
What the company appears to be
Clean-and-clear is a self-reported skincare routine curation tool that uses AI image analysis (via Gemini) and rule-based safety constraints to generate personalized skincare routines from selfies. The product is described as a Progressive Web App (PWA), built with NextJS, Firebase, and Codex.
What changed
The project was submitted to the OpenAI 2026 hackathon on Devpost. It represents an early-stage prototype or proof-of-concept, not yet a commercial product or service in production.
Single most important open question
Is there any evidence of user adoption, revenue, or customer traction beyond the hackathon submission?
What The Product Actually Is
The description states:
- Clean-and-clear is a skincare routine curator.
- It allows users to upload a selfie and receive a skincare routine generated via Gemini image analysis and rule-based safety constraints.
- It was built using Codex, Gemini Vision API, NextJS, PWA, and Firebase.
Inference The product appears to be an AI-powered personalization tool for skincare, likely intended for consumer use, but no commercial or production evidence is provided.
Positioning & Claim Evolution
The description states:
- "Skin care should be an easy to follow routine without overcomplications and expertise."
- "Scan your face, extract details and curate skin care routine for you!"
Inference Positioning appears to be centered on simplifying skincare through AI, targeting users who want personalized but non-expert advice. The claim evolution seems to be from a hackathon prototype to a potential consumer product.
Target Customer & ICP
The description states:
- The tool is for users who upload selfies and receive a curated skincare routine.
- It uses Gemini image analysis and rule-based safety constraints.
Not evidenced No explicit customer persona, segment, or ideal customer profile (ICP) is described. No indication of whether the target is general consumers, specific demographics, or niche markets.
Business Model & Pricing Evidence
The description states:
- No pricing information or business model is provided.
- The project was built as a hackathon submission and hosted with given credits.
Inference There is no evidence of a monetization strategy, pricing model, or revenue streams. The product is described as a prototype, not a commercial offering.
Technical & Delivery Signals
The description states:
- Built with Codex, Gemini Vision API, NextJS, PWA, Firebase.
- Challenges included skin analysis and generating meaningful routines.
- Accomplishments include building and hosting a working app with given credits.
Inference Technical stack suggests a modern web-based solution using AI APIs and cloud infrastructure. The prototype is functional but not production-ready or scalable.
Traction & Maturity Signals
The description states:
- It was submitted to the OpenAI 2026 hackathon.
- A working app was built with given credits.
- No mention of users, customers, or adoption beyond the hackathon.
Not evidenced No evidence of user traction, revenue, customer base, or product maturity beyond a hackathon prototype.
Competitive Context
The description states:
- No direct competitors are mentioned.
- The project is described as a novel approach using AI for skincare.
Inference There is no evidence of competitive analysis or positioning in the market. The project appears to be an early-stage idea, not yet competing with established players.
Key Risks & Red Flags
The description states:
- It's a hackathon submission with no commercial traction.
- No revenue, customer data, or scalability plans are evident.
Inference Key risks include lack of product-market fit, no monetization strategy, and unproven user adoption. The project is in an early prototype phase with no evidence of real-world usage or business viability.
Diligence Questions To Ask The Founders
- What is the current stage of development beyond the hackathon?
- Have you validated any user feedback or conducted market research?
- Are there plans to monetize this product, and if so, how?
- How do you plan to scale beyond the prototype?
- What are your assumptions about user behavior and adoption?
Investment/Partnership Verdict
The description states:
- It is a hackathon submission with no commercial evidence.
- No revenue, customers, or traction data are provided.
Inference At this stage, the project is not ready for investment or partnership. It lacks commercial viability, traction, and a clear path to monetization. The product is an early prototype with no verified business model or user base.
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.