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 #4,607 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
Company
ilog
Self-reported basis
The analysis is based entirely on the author’s own description of ilog, submitted as part of a Devpost entry for the OpenAI 2026 hackathon. No external verification or historical data are available.
What it appears to be
A mobile application designed to help families manage baby care records and coordinate caregiving through structured logging and AI-powered handoffs.
What changed
The project was submitted as part of a hackathon, with the author stating that they are adding an AI Care Handoff feature powered by GPT-5.6 during the OpenAI Build Week.
Most important open question
Is there evidence of any user adoption or feedback from families using the app?
What The Product Actually Is
The description states that ilog is a mobile family care log built with Expo and Supabase. It allows families to record various baby care activities, including feeding, sleep, diaper changes, temperature, medication, pumping, growth, vaccinations, hospital visits, and notes.
It also includes features such as:
- Shared tasks
- Reminders
- Family chat
- Photo sharing
- Notifications
- Timeline view
- Category-based statistics
The author further states that they are adding an AI Care Handoff powered by GPT-5.6 during the OpenAI Build Week, which will summarize recent structured records, open tasks, reminders, and conversation context into a clear handoff for caregivers.
Inference The app is a hybrid of a data logging tool and an AI-powered coordination assistant.
Positioning & Claim Evolution
The author positions ilog as a baby care helper that aims to make family care history understandable at a glance, not just stored. It is described as a tool to reduce missed context and coordination stress between caregivers.
During the OpenAI Build Week, the author plans to integrate an AI-powered handoff feature using GPT-5.6. The goal of this AI integration is not to provide medical advice but to help caregivers coordinate more effectively by summarizing recent activity and tasks.
Inference The positioning evolved from a simple logging tool to one that leverages AI for caregiver coordination, likely in response to the hackathon context.
Target Customer & ICP
The target customer is families with babies, particularly those who have multiple caregivers (e.g., parents, grandparents, babysitters) and need to coordinate care.
The app is designed for shared caregiving environments where information is fragmented across individuals or platforms.
Inference The ICP appears to be families managing shared childcare responsibilities, though no explicit segmentation or user data are provided in the description.
Business Model & Pricing Evidence
No evidence of a business model or pricing structure is provided. The author describes the app as a personal project submitted for a hackathon, with no mention of monetization, subscriptions, or paid features.
Inference There is no indication that ilog has moved beyond prototype or concept stage in terms of monetization.
Technical & Delivery Signals
The app is built using:
- Expo.io
- React Native
- Supabase (for data flows)
- Codex (as a coding partner)
- GPT-5.6 (for AI integration)
It is described as a mobile application, and the author mentions integrating AI via a structured workflow that returns validated JSON.
Inference The technical stack suggests a modern, lightweight mobile app with backend support and AI integration, but no evidence of production deployment or scalability.
Traction & Maturity Signals
The project was submitted to the OpenAI 2026 hackathon, indicating it is in an early stage. No evidence of user adoption, customer feedback, or product traction is provided.
Inference There is no evidence of a functioning product with users or revenue. The app appears to be a prototype or proof-of-concept.
Competitive Context
The description does not mention any competitors. However, the idea of baby care logging and AI-powered coordination aligns with existing categories such as:
- Baby tracking apps
- Family coordination tools
- Healthcare or wellness apps for parents
Inference The competitive landscape is not described, but this type of app likely competes with general parenting apps or family management platforms.
Key Risks & Red Flags
- No evidence of user adoption or feedback: The project is described as a hackathon submission with no traction.
- AI integration is unproven: GPT-5.6 is used, but there is no demonstration or validation of its effectiveness in this context.
- Unclear path to product-market fit: No indication that the app has moved beyond prototype or received user testing.
- Single founder team: The project is built by one person (Mozi Kim), which may limit development speed or scalability.
Diligence Questions To Ask The Founders
- What is the current status of the app? Is it functional, and if so, how many families are using it?
- How do you plan to validate that the AI handoff feature is actually useful to caregivers?
- Are there any privacy or data governance concerns with storing sensitive baby care information in a mobile app?
- What is your long-term vision for ilog beyond this hackathon project?
- Have you considered how to scale the AI integration or handle increasing user data volume?
Investment/Partnership Verdict
Not evidenced. The description does not provide any evidence of traction, revenue, customers, or a clear path to monetization. It is a self-reported concept submitted as part of a hackathon project.
The app appears to be in an early stage of development and lacks any commercial due-diligence signals such as user feedback, market validation, or product-market fit indicators.
Confidence Low. The analysis is based entirely on the author’s own description, which is unverified and self-reported.
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.