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 #6,286 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
Recetitas is a self-reported social recipe platform built by two founders (Stherling Gomez and Martina Villarreal) for desktop and mobile. It allows users to publish structured recipes with media, follow cooks, save dishes, and engage in live cooking experiences. The platform supports multiple languages and includes features like guided cooking mode, live streaming, and accessibility controls.
What changed
The project was submitted as a hackathon entry (OpenAI 2026) and is described as deployed with working flows. It uses AI tools such as Codex and GPT-5.6 in an iterative engineering loop to build and iterate on functionality.
Single most important open question
Is there any evidence of user adoption, revenue, or traction beyond the authors’ own testing and deployment?
What The Product Actually Is
The description states that Recetitas is a working social recipe platform for desktop and mobile, where users can register with username/password, publish recipes, follow cooks, comment, like, save, and organize into collections. Recipes include cover photos, videos (up to 35 seconds), ingredients, steps, portions, difficulty, hashtags, polls, and optional timers.
It also supports live cooking broadcasts using WebRTC via MediaMTX, with moderation features such as pig stickers, message deletion, bans, and session recovery. The platform includes a guided cooking mode, which generates shopping checklists and opens full-screen step-by-step instructions that remember progress on the device.
The interface is described as mobile-first, with responsive design considerations for accessibility (text size, contrast, reduced motion, color vision).
Evidence
- The description states: “Recetitas is a working social recipe platform for desktop and mobile.”
- It includes details about recipe structure, media handling, live streaming, guided cooking mode, and accessibility controls.
- The authors describe how they built the product using Vue 3, Vite, Tailwind CSS, Cloudflare Worker, D1, and MediaMTX.
Inference The platform appears to be a hybrid between a social network and a structured recipe tool, designed for both discovery and practical use during cooking.
Positioning & Claim Evolution
The authors state that Recetitas began from personal observations about how Latin American cooks struggle to find audiences on general social platforms, despite having valuable content. They wanted to bring the reach of short-form video to recipes while preserving their utility in the kitchen.
They emphasize that it is not a generic video feed but a platform that gives recipes the personality and reach of a social post while maintaining structure needed for cooking.
Evidence
- “We are a couple, we love food, and we kept noticing the same problem: talented Latin American cooks publish useful work online, but general social platforms are not built around recipes.”
- “We liked the way short-form video can introduce an unknown creator to a large audience. We did not want to copy a generic video feed and add food photos to it.”
Inference The positioning evolved from solving a niche problem (Latin American cooks on non-specialized platforms) into a broader platform that supports recipe discovery, community building, and live interaction — though the initial focus was Spanish-speaking.
Target Customer & ICP
The authors state that Recetitas started with Spanish-speaking cooks and Latin American food, as that is the community closest to them. However, they note that the product already supports recipes and interfaces in several languages.
They describe their target users as:
- Cooks who want to share structured recipes
- People looking for new dishes to try
- Users who enjoy live cooking experiences
Evidence
- “We started with Spanish-speaking cooks and Latin American food because that is the community closest to us, but the product already supports recipes and interfaces in several languages.”
- “A recipe can contain a cover photo and a video of up to 35 seconds...”
Inference The ICP seems to be home cooks and culinary creators, especially those interested in structured content and live interaction. The platform may appeal more to users who value community, accessibility, and practicality over monetization or mass reach.
Business Model & Pricing Evidence
There is no mention of a business model or pricing strategy in the description. The authors do not describe any monetization plans, subscriptions, or paid features.
Evidence
- No explicit statement about revenue models.
- No information on pricing tiers, freemium options, or premium features.
Inference The business model is not evidenced. It remains unclear whether the platform intends to charge users, monetize creators, or rely on other forms of value capture.
Technical & Delivery Signals
The authors describe a technical stack built with:
- Frontend: Vue 3, Vite, Tailwind CSS
- Backend: Cloudflare Worker, D1 (SQLite)
- Media handling: VPS-based service for uploads, MediaMTX for live streaming
- Authentication: PBKDF2 hashes, HttpOnly SameSite cookies
They used AI tools like Codex and GPT-5.6 in an iterative development loop:
- Codex inspected workspace, wrote focused changes, added migrations, ran tests, committed revisions, and helped deploy.
- GPT-5.6 was used for debugging across systems (authentication, media validation, responsive layouts, WebRTC).
Evidence
- “The public landing and signed-in application use Vue 3, Vite, and Tailwind CSS.”
- “We built the interface mobile-first, then checked the same flows on desktop.”
- “We did not treat Codex as a button that generated the whole project. We used it as an engineering partner inside a tight loop.”
Inference The platform shows technical maturity in its architecture and use of modern tools. The iterative AI-assisted workflow suggests disciplined development practices, though no production-scale metrics or performance data are provided.
Traction & Maturity Signals
There is no evidence of user traction, revenue, or customer adoption beyond the authors’ own testing and deployment. The description states that Recetitas is deployed and functional with real user-created data, but does not provide any metrics on active users, retention, or monetization.
Evidence
- “Recetitas is deployed and the main flows work with user-created data.”
- “A new account can publish a recipe with media, receive social activity, build a profile, save dishes, cook through the instructions, or start a moderated live broadcast.”
Inference While the platform functions, there is no indication of real-world usage or engagement beyond internal testing. No data on user growth, DAU/MAU, or conversion rates are available.
Competitive Context
The description does not mention competitors directly. However, it implies that existing social platforms do not adequately support structured recipes or live cooking experiences.
Evidence
- “General social platforms are not built around recipes.”
- “We did not want to copy a generic video feed and add food photos to it.”
Inference Recetitas likely competes with general social networks (e.g., Instagram, TikTok) that allow recipe sharing but lack structure or practicality for cooking. It may also compete with niche recipe apps like Yummly or Allrecipes, though those are not named.
Key Risks & Red Flags
- No revenue or traction evidence: The platform is described as deployed and functional, but there is no sign of monetization or user engagement.
- Limited team size: Only two members (Stherling Gomez and Martina Villarreal), which may limit scalability or feature development.
- Unclear business model: No indication of how the platform intends to generate revenue.
- AI dependency risk: Heavy reliance on AI tools like Codex and GPT-5.6 raises questions about long-term sustainability if those tools change or become unavailable.
Evidence
- “No revenue, customer or traction data is available beyond what they state.”
- “Only two members” are listed as team.
- No mention of monetization strategies.
Diligence Questions To Ask The Founders
- What is the current user base and level of engagement?
- How do you plan to monetize the platform, if at all?
- Are there any plans for scaling beyond the initial Spanish-speaking community?
- What are the key technical challenges you've faced in production, and how were they resolved?
- How do you intend to grow the creator base and maintain quality content?
- Have you considered legal or compliance issues related to live streaming and user-generated content moderation?
Investment/Partnership Verdict
Not evidenced: There is no evidence of revenue, customers, or traction beyond the authors’ own deployment and testing.
This project appears to be a functional prototype, built in a hackathon setting, with strong technical execution and clear intent. However, there is no indication of commercial viability, user adoption, or monetization strategy.
Confidence level: Low — based on self-reported evidence only, with no external validation or performance data.
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.
