Doomo

Project: Doomo

Source: Author-supplied description from Devpost submission to OpenAI 2026 hackathon

Analysis basis: Self-reported, unverified account of the project

Doomo is described as an Android app that turns phone usage habits into an emotional companion. The app uses AI and behavioral data to simulate a character (Doomo) that responds to user behavior — positive actions like studying or completing quests increase Doomo’s vitality, while mindless phone use decreases it. It was built in a hackathon context with no evidence of revenue, customers, or traction.

What changed: The project is a prototype submitted for a hackathon. No commercial product or business model has been demonstrated beyond the author's own claims.

Single most important open question: Is there any evidence that users engage with Doomo in a way that suggests adoption or emotional investment — beyond the authors’ own claims?

The description states:

• Doomo is an Android app.
• It simulates a companion character (Doomo) that responds to user behavior.
• Phone usage habits affect Doomo’s vitality: positive actions like studying or completing quests increase it, while mindless phone use decreases it.
• It uses AI tools (Codex, GPT-5.6), React Native, Android usage data, and an offline behavior engine.
• The app includes isometric visuals and spaced-repetition study cards.

Inference: Doomo appears to be a behavioral feedback system that gamifies screen-time management through an emotional character. It is not a productivity tool per se but a companion that reflects user habits.

Not evidenced: No information on actual functionality, UI/UX, or whether the app works as described in practice.

The description states:

• Doomo aims to make screen-time visible and emotionally engaging.
• It is positioned as an alternative to analytics dashboards or warnings.
• The authors claim that people respond more strongly to a character reflecting them than to charts, warnings, or limits.

Inference: The positioning evolved from a simple habit tracker to an emotional companion with behavioral feedback loops. This suggests a shift from functional tools to emotionally engaging experiences.

Not evidenced: No evidence of market positioning beyond the authors’ own claims. No competitor analysis or user feedback is provided.

The description states:

• The app targets people concerned about screen-time and habits.
• It is designed for users who want to see their phone usage in a more meaningful way.

Inference: The target customer is likely a tech-savvy, self-aware user — possibly students or young professionals — interested in personal development and digital wellness.

Not evidenced: No specific ICP defined. No data on user personas, demographics, or behavioral segments.

The description states:

• No pricing model or monetization strategy is mentioned.
• The app was built for a hackathon; no commercial intent is evident.

Inference: There is no business model described. The project appears to be experimental and not yet commercialized.

Not evidenced: No revenue streams, pricing plans, or monetization strategies are provided.

The description states:

• Built with React Native, Android usage data, Codex, GPT-5.6, and offline behavior engine.
• Uses TypeScript, Kotlin, Expo.io, FSRS, and OpenAI tools.
• The app includes isometric visuals and animations.
• It integrates Android UsageStatsManager for tracking.

Inference: The technical stack suggests a hybrid approach using AI and mobile data to simulate user behavior. The use of offline behavior engine implies some level of autonomy in the system.

Not evidenced: No information on scalability, performance, or delivery mechanisms beyond the hackathon prototype.

The description states:

• Submitted to OpenAI 2026 hackathon.
• Team size: 2 (Zohair Ahmed, Muhammad Zamin).
• Accomplishments include turning habits into an emotional companion and creating isometric visuals.
• Future plans involve more animations, smarter study cards, and deeper personality changes.

Inference: The project is in a very early stage — a hackathon prototype with no evidence of user adoption or product-market fit.

Not evidenced: No data on user engagement, retention, or adoption. No metrics or customer feedback are provided.

The description states:

• Doomo aims to be an alternative to analytics dashboards and warnings.
• It is not directly compared to existing apps in the market.

Inference: The competitive space likely includes screen-time management tools, habit trackers, and digital wellness apps. Doomo’s emotional companion approach may differentiate it from purely functional tools.

Not evidenced: No competitive analysis or benchmarking against existing products is provided.

The description states:

• Challenges included making Doomo motivating without judgment and keeping Android tracking reliable.
• The app uses AI tools like GPT-5.6, which may raise concerns about data privacy or scalability.
• No commercial traction or monetization strategy is evident.

Inference: Key risks include:

• Lack of user engagement or adoption beyond the authors’ own claims.
• Reliance on AI and mobile tracking — both potentially sensitive or unstable.
• Prototype nature implies no proven product-market fit or scalability.

Not evidenced: No evidence of any risk mitigation strategies, data privacy policies, or long-term viability.

1. What is the actual user engagement like with Doomo? Is there any data on how often users interact with it?
2. How does Doomo handle sensitive Android usage data — what are the privacy implications?
3. Are there any plans to monetize or scale the product beyond the prototype stage?
4. What specific behavioral feedback loops have you observed in practice?
5. How do you plan to transition from a React Native prototype to an Android-native app?

evidenced

The description states: "Screen-time numbers are easy to ignore. We wanted your habits to become something you could actually see and care about."

This indicates the primary customer segment is individuals who struggle with screen time management and seek a more emotionally engaging way to monitor their digital habits.

evidenced

The description states: "Wasting time on phone weakens Doomo; studying and completing small real-world quests help him recover."

This describes the core value proposition: transforming user behavior into an emotional companion that responds to daily choices, making screen-time awareness more personal and engaging.

inferred

Based on the technology stack mentioned (React Native, Android usage data, Codex, GPT-5.6), it can be inferred that Doomo is distributed through mobile app channels, likely Android-based, with potential integration points for Google Classroom.

inferred

From "We turned screen habits and learning progress into an emotional companion instead of another analytics dashboard," it can be inferred that the relationship model involves nurturing an emotional connection between user and virtual companion, rather than transactional or purely informational interaction.

not evidenced

The description does not mention any revenue models, pricing strategies, or monetization approaches. No indication is given about how Doomo would generate income.

evidenced

The description states: "We used Codex and GPT-5.6 with React Native, Android usage data, an offline behavior engine, and spaced-repetition study cards."

This indicates key resources include AI tools (Codex, GPT-5.6), mobile development framework (React Native), Android system integration capabilities, behavioral analytics engine, and educational content systems.

evidenced

The description states: "We used Codex and GPT-5.6 with React Native, Android usage data, an offline behavior engine, and spaced-repetition study cards."

This indicates key activities involve AI-powered content generation (Codex, GPT-5.6), mobile application development (React Native), system integration with Android usage tracking, behavioral modeling, and educational card creation.

inferred

From "Integrating google classroom for more personalized feel and realtime conversation," it can be inferred that Doomo may partner with educational platforms like Google Classroom to enhance its functionality and user experience.

not evidenced

The description does not provide information about operational costs, development expenses, or resource allocation strategies. No details are given about the financial structure of building or maintaining Doomo.

1. Customer Segments: evidenced - Based on "Screen-time numbers are easy to ignore. We wanted your habits to become something you could actually see and care about."
2. Value Propositions: evidenced - Based on "Wasting time on phone weakens Doomo; studying and completing small real-world quests help him recover."
3. Channels: inferred - From technology stack (React Native, Android usage data) and project context.
4. Customer Relationships: inferred - From "turned screen habits and learning progress into an emotional companion" statement.
5. Revenue Streams: not evidenced - No mention of monetization or pricing models.
6. Key Resources: evidenced - Based on "We used Codex and GPT-5.6 with React Native, Android usage data, an offline behavior engine, and spaced-repetition study cards."
7. Key Activities: evidenced - Based on same statement as key resources.
8. Key Partnerships: inferred - From "Integrating google classroom for more personalized feel and realtime conversation."
9. Cost Structure: not evidenced - No information about operational or development costs.

Questions that would convert inferred blocks to evidenced:

1. Channels: What specific platforms or distribution methods will Doomo use?
2. Customer Relationships: How does the emotional companion model work in practice?
3. Revenue Streams: What monetization strategies are planned for Doomo?
4. Key Partnerships: Which specific educational platforms or services will Doomo integrate with?
5. Cost Structure: What are the primary cost drivers in developing and maintaining Doomo?