OpenAI 2026 hackathon

Personal OS: ADHD Smart Planning List

A working alpha that helps people with ADHD-related planning and task-initiation friction turn projects into smaller, clearer next actions.

Solo project by ying lin · 0 likes · 0 comments

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 #5,902 place in the like-ranked listing is a tie-break inside that group, not a ranking.

Projects (log scale)

1
10
100
1k
10k
05,592
11,758
2285
3–4132
5–975
10+14

Likes on Devpost. ▲ marks this project's group.

Show the figures
LikesProjectsShare of archive
05,59271.2%
11,75822.4%
22853.6%
3–41321.7%
5–9751.0%
10+140.2%
Devpost like counts for all 7,856 archived projects, captured when this archive was built.

Executive Summary

The company appears to be a solo project (1 person) building an iOS app for people with ADHD-related planning friction. The product is described as a working alpha that helps users break projects into smaller actions and see what to do next. It uses Swift, SwiftUI, and SQLite, and incorporates AI tools like Codex and GPT-5.6 during development.

What changed: The author reports building a native iOS app for ADHD planning, using AI-assisted development tools during a hackathon. The tool is described as an alpha with incomplete features, focusing on local task/project/stage workflow before expanding AI features.

Single most important open question: Is there evidence of user validation or adoption beyond the solo developer's own use? The description states no revenue, customers, or traction data are available.

Back to contents

What The Product Actually Is

The description states that Personal OS is a working alpha for planning projects, stages, tasks, and smaller task steps. It helps users keep work visible, break a task into manageable actions, and see what to do next. It is built as a native iOS app using Swift, SwiftUI, and SQLite.

Evidence:

  • "Personal OS is a working alpha for planning projects, stages, tasks, and smaller task steps"
  • "It helps users keep work visible, break a task into manageable actions, and see what to do next"
  • "Built a native iOS app with Swift, SwiftUI, and SQLite"

Inference: The tool appears to be a task management application focused on breaking down larger goals into actionable steps. It is not described as a marketplace, SaaS platform, or developer tool.

Back to contents

Positioning & Claim Evolution

The description states that the product helps people with ADHD-related planning and task-initiation friction turn projects into smaller, clearer next actions. The inspiration behind it was to provide a calmer way to handle overwhelming goals, rather than adding more tasks.

Evidence:

  • "People with ADHD-related planning and task-initiation friction often do not need more tasks. They need a calmer way to turn an overwhelming goal into one clear next action."
  • "A planning tool for ADHD-related friction needs clear hierarchy, low cognitive load, and honest product states instead of pretending unfinished automation is complete."

Inference: The positioning is focused on solving a specific problem (ADHD-related planning friction) with a minimalist approach that emphasizes clarity and low cognitive load. It does not claim to be a general-purpose productivity tool.

Back to contents

Target Customer & ICP

The description states the app is for people with ADHD-related planning and task-initiation friction. The author also mentions that they are validating the experience with users, but no specific customer segments or personas are defined.

Evidence:

  • "People with ADHD-related planning and task-initiation friction"
  • "We will validate the experience with users"

Inference: The target customer is likely individuals with ADHD who struggle with initiating tasks or managing complex projects. No explicit ICP (Ideal Customer Profile) is described beyond this.

Back to contents

Business Model & Pricing Evidence

The description does not provide any information on pricing, monetization, or business model. It is unclear if the app will be sold, offered for free, or have a freemium model.

Evidence: None provided.

Inference: No business model or pricing evidence is available in the self-reported description.

Back to contents

Technical & Delivery Signals

The product is built as a native iOS app using Swift, SwiftUI, and SQLite. The development was accelerated with Codex and GPT-5.6 during OpenAI Build Week. The author notes that the product is intentionally still half-built, focusing on core workflow before AI features.

Evidence:

  • "Built a native iOS app with Swift, SwiftUI, and SQLite"
  • "Codex and GPT-5.6 accelerated implementation, testing, and iteration"
  • "The product is intentionally still half-built. We focused on a reliable local task, project, stage, and step workflow before expanding the AI-assisted features"

Inference: The technical stack suggests a native iOS app with local data storage. The use of AI tools during development indicates an early-stage, iterative approach.

Back to contents

Traction & Maturity Signals

The description states that this is a working alpha, and that the product is intentionally still half-built. There is no mention of users, customers, revenue, or adoption beyond the developer's own validation.

Evidence:

  • "This is a working alpha"
  • "The product is intentionally still half-built"
  • "We will validate the experience with users"

Inference: No traction or maturity data are provided. The project is described as early-stage and under development.

Back to contents

Competitive Context

No information is provided about competitors or market positioning beyond the general idea of planning tools for ADHD users.

Evidence: None provided.

Inference: No competitive analysis or context is available in the self-reported description.

Back to contents

Key Risks & Red Flags

  • The project is described as a solo effort with no team, which may limit scalability.
  • It's an alpha and not fully built, indicating early-stage development.
  • No revenue, customers, or traction data are provided.
  • The lack of pricing or monetization strategy raises questions about long-term viability.

Evidence:

  • "Team size: 1"
  • "This is a working alpha"
  • "No revenue, customer or traction data is available"

Inference: The solo developer model and early-stage product raise concerns about execution, scalability, and commercial viability.

Back to contents

Diligence Questions To Ask The Founders

  1. What specific user feedback have you received so far?
  2. How do you plan to monetize the app beyond the current alpha?
  3. Are there any plans for team expansion or partnerships?
  4. What are your timelines for completing remaining features?
  5. Have you identified a clear path to user adoption or market fit?

Back to contents

Investment/Partnership Verdict

The description states that this is a solo project, built as an alpha during a hackathon. There is no evidence of revenue, customers, or traction. The product is described as incomplete and under development.

Evidence:

  • "Team size: 1"
  • "This is a working alpha"
  • "No revenue, customer or traction data is available"

Inference: This project is in an early stage with no commercial evidence. It may be suitable for early-stage investment or partnership if the founder plans to validate and scale the concept, but lacks current proof of traction or business model viability.

Back to contents

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.