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 #5,387 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
Moodie is a project described by its author as a tool for social film discovery through personal responses. It was submitted to the OpenAI 2026 hackathon on Devpost and built using a stack including Codex, Express.js, GPT-5.6-Sol, pgLite, React, TMDB API, TypeScript, and Vite.
What changed
There is no evidence of prior versions or changes; this is the first public description provided by the author.
Single most important open question
Is there any indication of user adoption, revenue, or a defined customer base beyond the hackathon submission?
What The Product Actually Is
The description states that Moodie is “a tool for social film discovery through personal responses.” It was built as part of a hackathon project and uses technologies such as Codex, Express.js, GPT-5.6-Sol, pgLite, React, TMDB API, TypeScript, and Vite.
Evidence
- The author describes the product as enabling “social film discovery through personal responses.”
- Technology stack includes: Codex, Express.js, GPT-5.6-Sol, pgLite, React, TMDB API, TypeScript, and Vite.
- Submitted to the OpenAI 2026 hackathon.
Inference The use of GPT-5.6-Sol and TMDB API suggests integration with AI-based content recommendation and film data sources.
Positioning & Claim Evolution
The tagline is: “Social film discovery through personal responses.”
Evidence
- Tagline: “Social film discovery through personal responses.”
- No additional claims or positioning evolution described.
Inference This implies a focus on user-generated content or sentiment-based recommendations, but no evidence of how this differs from existing platforms like IMDb or Rotten Tomatoes.
Target Customer & ICP
The description does not state who the target customer is or what the ideal customer profile (ICP) might be.
Evidence
- No mention of user personas, demographics, or use cases.
- No indication of whether the product targets casual moviegoers, critics, or film enthusiasts.
Inference Given the hackathon context and the use of personal responses, it may target users who value subjective or community-driven reviews, but this is speculative.
Business Model & Pricing Evidence
There is no evidence of a business model or pricing strategy in the description.
Evidence
- No mention of monetization.
- No indication of pricing tiers, subscriptions, or revenue streams.
Inference If the project is intended for commercial use, it likely lacks a defined monetization plan at this stage.
Technical & Delivery Signals
The project was built using a specific tech stack including Codex, Express.js, GPT-5.6-Sol, pgLite, React, TMDB API, TypeScript, and Vite.
Evidence
- Built with: Codex, Express.js, GPT-5.6-Sol, pgLite, React, TMDB API, TypeScript, and Vite.
- Submitted to a hackathon, suggesting a prototype or proof-of-concept.
Inference The use of GPT-5.6-Sol and TMDB API indicates integration with AI and movie data, but no evidence of scalability or production readiness.
Traction & Maturity Signals
There is no evidence of traction, user adoption, or product maturity beyond the hackathon submission.
Evidence
- Submitted to a hackathon.
- No mention of users, downloads, or engagement metrics.
- No indication of prior versions or iterations.
Inference This project appears to be in early development and lacks any signs of market traction or product-market fit.
Competitive Context
There is no evidence of competitive analysis or positioning against existing platforms.
Evidence
- No mention of competitors.
- No indication of how Moodie differs from other film discovery tools.
Inference The project may compete with platforms like IMDb, Rotten Tomatoes, or streaming services' recommendation engines, but this is not stated.
Key Risks & Red Flags
- No traction or user base: The product is described only as a hackathon submission.
- Unproven business model: No evidence of monetization or revenue streams.
- Limited scope: No indication of long-term vision or roadmap.
- Self-reported only: All information is unverified and self-declared.
Diligence Questions To Ask The Founders
- What problem are you solving, and how does Moodie address it differently from existing platforms?
- Who are your target users, and how do you plan to reach them?
- Do you have any early adopters or user feedback?
- How do you intend to monetize the product?
- What is your roadmap for development beyond this hackathon prototype?
Investment/Partnership Verdict
Not evidenced.
There is no evidence of revenue, customers, traction, or a clear business model. The project is described only as a hackathon submission with no indication of commercial viability or scalability.
Confidence Low. The description provides no basis for assessing product-market fit, team capability, or investment potential.
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.