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 #6,971 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
Stockd is a self-reported AI-powered kitchen inventory management tool that processes grocery receipts (from platforms like Blinkit, Zepto, Instamart, BigBasket) to build and maintain a pantry inventory. It claims to help users avoid waste, duplicate purchases, and save money by tracking expiry dates, suggesting recipes, and calculating potential savings.
What changed
The project was built as part of the OpenAI 2026 hackathon. It is described as a functional prototype with an anonymous demo showing nine historical orders and simulated household data. No commercial product or customer base is evidenced.
Single most important open question
Is there evidence of user adoption, revenue, or traction beyond the author’s own demonstration?
What The Product Actually Is
The description states that Stockd:
- Processes screenshots of grocery orders
- Extracts products, quantities, prices, and estimated shelf life using GPT-5.6
- Presents this data for user review before adding it to the pantry
- Maintains a live kitchen inventory
- Highlights food nearing expiry
- Tracks waste and its cost
- Detects price changes between orders
- Suggests recipes for expiring ingredients
- Checks shopping-cart screenshots against pantry
- Warns about duplicate purchases
- Calculates potential money saved
It uses:
- Next.js 15, TypeScript, Tailwind CSS, shadcn/ui
- Supabase for auth and PostgreSQL for storage
- Vercel for deployment
- GPT-5.6 via OpenAI Responses API for perception and language tasks
- Deterministic code for financial and inventory logic (e.g., unit conversion, expiry calculations)
Inference The product is described as a hybrid system combining AI for messy data interpretation with deterministic logic for financial and inventory decisions.
Positioning & Claim Evolution
The description states:
- The idea came from the problem of buying groceries without remembering what’s at home
- It aims to turn grocery receipts into money saved
- It targets users who order groceries via quick-commerce platforms
- It positions itself as a pantry that updates automatically from existing data (receipts)
Inference The positioning is centered on solving a common household inefficiency through automation and AI. The claim evolution appears to be: “If you already have receipts, we can make your kitchen smarter.”
Target Customer & ICP
The description states:
- Users are those who order groceries via quick-commerce apps (Blinkit, Zepto, Instamart, BigBasket)
- It assumes users already have grocery receipts on their phones
- The demo simulates a household with specific items like ketchup, dal, and paneer
Inference The ICP appears to be tech-savvy individuals or households who use quick-commerce apps and are interested in reducing food waste and saving money.
Business Model & Pricing Evidence
Not evidenced.
The description does not mention any pricing model, monetization strategy, or commercial structure beyond the hackathon project.
Technical & Delivery Signals
The description states:
- Built with Next.js 15, TypeScript, Tailwind CSS, shadcn/ui
- Uses Supabase for authentication and PostgreSQL for storage
- Vercel for deployment
- GPT-5.6 via OpenAI Responses API for receipt parsing and language tasks
- Zod schemas for structured model outputs
- Deterministic logic for financial and inventory decisions
- Codex used to accelerate development through six milestones
Inference The technical stack suggests a modern, scalable web app with AI integration. The hybrid approach (AI + deterministic logic) is described as intentional.
Traction & Maturity Signals
Not evidenced.
There is no mention of:
- Users or customers
- Revenue or monetization
- Product usage metrics
- Growth or retention data
- Any commercial traction beyond the demo
The project is described as a hackathon submission with an anonymous demo.
Competitive Context
Not evidenced.
No information is provided about competitors, market size, or competitive positioning.
Key Risks & Red Flags
Red Flag 1
No evidence of user adoption or commercial traction beyond the author’s own demo.
Red Flag 2
The product is described as a hackathon project with no indication of ongoing development or product-market fit.
Red Flag 3
The use of GPT-5.6 for receipt parsing implies reliance on an external AI service, which may not be scalable or cost-effective in the long term.
Red Flag 4
No pricing model or monetization strategy is described — a key commercial element missing from a product that claims to save money.
Diligence Questions To Ask The Founders
- What is the actual user base or traction beyond the demo?
- How does the product plan to scale beyond a hackathon prototype?
- Is there any evidence of customer feedback or iteration?
- What is the monetization strategy, and how does it align with user value?
- Are there plans for data privacy compliance (e.g., handling grocery receipts)?
- How does the product handle edge cases in receipt parsing or product matching?
Investment/Partnership Verdict
Not evidenced.
There is no evidence of:
- Revenue
- Customers
- Traction
- Financials
- Market validation
The project is described as a hackathon submission with an anonymous demo and no commercial structure.
Confidence Low. This analysis is based entirely on the self-reported description provided by the author. No external verification or traction data is available.
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.