RecipeLab

The description states that RecipeLab is:

• A private mobile workspace for food creators and serious cooks
• An Expo and React Native mobile application with Supabase backend
• Designed to preserve history between cooking attempts
• Not focused on collecting or publishing finished recipes, but on recipe development
• Built around the concept of "A Recipe is the dish. Every cooking attempt is a Version"
• Capable of creating, organizing, comparing, and preserving multiple versions of recipes

The author describes it as an MVP that supports an end-to-end loop: Create → Cook → Capture → Adjust → Compare → Final.

The description states:

• The product was inspired by the author's own experience running a food YouTube channel
• The core idea emerged from the problem of scattered recipe development across multiple tools (Apple Notes, notebooks, photos, memory)
• The positioning is that most recipe apps focus on finished recipes, while RecipeLab focuses on recipe development
• The author claims to have built the product through collaboration with Codex and ChatGPT
• The evolution shows a shift from "engineering form" to "calm kitchen tool"
• The author emphasizes that the product model was not about declaring automatic winners but presenting evidence neutrally

The description states:

• Food creators who develop recipes through repeated cooking attempts
• Serious cooks who develop the same dish across multiple attempts
• Specifically mentions the author's own use case as a food YouTube channel creator (Meals with Rui)
• The target is described as "food creators and serious cooks" rather than general consumers

Not evidenced. The description does not contain any information about pricing, monetization strategy, or business model.

The description states:

• Built with Expo and React Native mobile application
• Supabase backend for authentication, Postgres data, atomic save operations, and image storage
• In-memory demo service that follows same Recipe and Version boundaries
• Used Codex and ChatGPT for development assistance
• GPT-5.6 was used in Codex during OpenAI Build Week to enable continuous design-to-implementation loop
• Implemented explicit save boundary and atomic Supabase operations for reliable saves
• UI challenges were overcome through visual review loops with AI assistance
• 70 committed test files covering product, interaction, layout, and regression contracts

Not evidenced. The description does not contain any information about revenue, customers, usage metrics, or traction beyond the author's own use case.

Not evidenced. The description does not contain any information about competitors, market positioning, or competitive landscape.

The description states:

• The hardest challenge was protecting a simple product model while building a deep application
• Save reliability was a major challenge requiring atomic operations
• UI was difficult and required significant iteration to avoid feeling like an engineering form
• Risk of attracting distracting features that don't test the core question
• No external validation or market demand proven yet
• The author explicitly states they do not want to present founder use as external validation

1. What specific evidence shows that preserving version history actually improves recipe development outcomes?
2. How does the product handle data privacy and ownership for users' recipe development records?
3. What are the actual technical limitations of the current MVP that would need to be addressed before broader adoption?
4. How do you plan to validate whether the core problem exists beyond your own use case?
5. What is the expected timeline for moving from pilot testing to a commercial product?
6. How does the current mobile-only approach impact potential market reach and scalability?