Deadline AI

Deadline AI is a planning tool that turns a task, a deadline, and daily available time into a realistic day-by-day plan.

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Inspiration

I often have long-term learning goals or projects, but I’m not very good at planning over the long term. As a result, I frequently fall behind schedule and miss my target deadlines. That got me thinking: What if AI could help me create and manage a plan? Inspired by that idea, I built this project.

What it does

Deadline AI helps users turn a task, a deadline, and their available daily time into a realistic execution plan. Users describe what they need to finish, choose a deadline, and enter how many hours they can work each day. The app then generates a day-by-day plan, evaluates whether the task is on track, at risk, or unrealistic, and gives scope advice to help users finish on time. It also supports progress tracking, completed-step styling, automatic movement of completed day blocks below unfinished work, plan regeneration, stop-generation control, delete confirmation, and task export/import.

How we built it

We built Deadline AI as a Next.js and TypeScript web app. The planning flow uses OpenRouter with DeepSeek to generate structured AI plans from the user’s task, deadline, and daily capacity. The frontend handles task creation, plan display, progress tracking, export/import, and local persistence. Tasks are stored in the browser with localStorage, so users can try the app without creating an account. The app is deployed on Vercel, with a live demo and source code available for review.

Challenges we ran into

One challenge was making the AI output useful instead of just generating a generic checklist. We needed the model to consider time capacity, deadline pressure, feasibility, and scope reduction. Another challenge was making the app feel reliable during generation. We added a stop button, retry behavior, safer error states, and validation around generated plans. We also had to think carefully about local data safety. For example, imported tasks are added as new local copies instead of overwriting existing tasks, which avoids accidental data loss.

Accomplishments that we're proud of

We are proud that Deadline AI is more than a simple to-do list. It gives users a feasibility judgment, explains risk, recommends scope changes, and turns the result into trackable daily work. We are also proud of the complete MVP loop: users can create a plan, track progress, regenerate if needed, export tasks, import them later, and keep working without an account. The app is simple to use, but it still handles practical details like stopping generation, delete confirmation, completed-step visuals, and local-only task privacy.

What we learned

We learned that AI planning works best when the product gives the model clear constraints. A deadline alone is not enough; daily available time is what makes the plan realistic. We also learned that small interaction details matter. Features like progress updates, completed task styling, import safety, and stop-generation controls make the tool feel much more usable. Finally, we learned that building an AI app is not just about calling a model. The surrounding product design, validation, state management, and failure handling are what make the AI output trustworthy.

What's next for Deadline AI

Next, Deadline AI could support user accounts, cloud sync, calendar integration, reminders, and recurring planning check-ins. We would also like to add more personalization, such as preferred working hours, task difficulty, energy level, and automatic plan adjustment when the user falls behind. Longer term, Deadline AI could become a full deadline management assistant that helps users plan, revise, and finish complex projects across multiple days or weeks.

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