Inspiration
What it does
How we built it
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for DeadlineFlow AI
Inspiration
As students, we noticed that people in college don't usually forget tasks because they don't care about them. The real problem is that college life is full of competing priorities — assignments, exams, events, competitions, hackathons, club activities, and personal work.
Students often start working on one thing while something more important is approaching its deadline. They still have to manually create tasks, decide what is urgent, plan their time, and remember every deadline.
That's why we built DeadlineFlow AI — to bridge the gap between knowing about a task and actually getting it done.
Our goal is simple: turn scattered information into clear actions and make sure important work doesn't disappear after being saved.
What it does
DeadlineFlow AI is an AI-powered productivity assistant that helps users capture, organize, prioritize, schedule, and complete their work.
Users can type or paste instructions, create tasks normally, or upload screenshots containing assignments, deadlines, events, competitions, or other important information.
AI analyzes that information and converts it into structured tasks with relevant deadlines and priorities.
The DeadlineFlow workflow
Capture → Understand → Prioritize → Plan → Remind → Complete
✨ Key Features
Screenshot-to-Task — Converts information inside screenshots into actionable tasks.
Deadline Risk Detection — Identifies tasks that may not be completed before their deadlines.
Next Best Action — Recommends what the user should focus on right now.
Smart Reminders — Follows up on unfinished tasks as their deadlines approach.
AI Scheduling — Organizes tasks based on deadlines, priorities, estimated effort, and available time.
Focus Mode — Helps users concentrate on one important task at a time.
Productivity Insights — Gives users a clearer understanding of their progress and workload.
Instead of simply showing another long to-do list, DeadlineFlow tries to answer one question:
"What should I do right now, and why?"
How we built it
DeadlineFlow AI is built as a modern full-stack web application using:
- Next.js
- React
- TypeScript
- Tailwind CSS
- ShadCN UI
- Google Gemini
We use Google Gemini for AI-powered understanding, including interpreting natural-language tasks and extracting actionable information from screenshots.
For decisions where predictable behavior is important, we use deterministic application logic rather than relying completely on AI. This includes:
- deadline-risk calculations
- task ranking
- scheduling decisions
- reminder states
- completion tracking
The application uses persistent storage and authentication so that tasks and schedules remain available across sessions while user data stays separated.
We also focused heavily on the user experience. The interface includes smooth motion, interactive visual elements, responsive layouts, and a focused productivity dashboard.
The dashboard is designed around one central question:
What should I do right now?
DeadlineFlow AI builds upon the open-source DeyWeaver project. We extended and transformed its productivity foundation by developing our own screenshot-to-task workflow, deadline-risk system, next-action recommendations, reminder experience, product design, and DeadlineFlow-specific user journey.
Challenges we ran into
Turning messy information into reliable tasks
One of our biggest challenges was converting unstructured information into something the application could reliably use.
A screenshot can contain multiple dates, unrelated text, incomplete instructions, and several possible actions.
Simply asking an AI model for an answer wasn't enough.
We needed structured extraction and a review process that allows users to verify important information before saving it.
Deciding what actually matters
The task with the closest deadline isn't always the task that should be completed first.
Our prioritization system therefore needed to consider:
- urgency
- deadline
- estimated effort
- priority
- available time
- completion status
Building useful reminders
Reminders also presented an interesting challenge.
Too few reminders can result in forgotten work, while too many notifications quickly become annoying.
We designed reminders around task status and deadline proximity, so completed work no longer generates unnecessary reminders.
Making AI part of the product
We didn't want DeadlineFlow to simply be another productivity application with a chatbot added to it.
Our goal was to make AI part of the actual workflow — from understanding information to helping users decide what they should do next.
Accomplishments that we're proud of
We're particularly proud that DeadlineFlow AI connects several disconnected productivity steps into one continuous workflow.
Traditionally, a student might have to:
Find information → Understand it → Create a task → Set a deadline → Prioritize it → Schedule it → Remember it → Complete it
DeadlineFlow reduces that friction by transforming incoming information directly into an actionable plan.
We're especially proud of the Deadline Risk and Next Best Action features.
Instead of presenting users with a long list of tasks and expecting them to figure everything out themselves, DeadlineFlow helps them make an immediate decision.
Most importantly:
DeadlineFlow isn't just about organizing your work. It's about helping you get it done.
What we learned
Building DeadlineFlow AI taught us that productivity problems are often information problems first.
AI is extremely useful for understanding messy, human information, but an AI model isn't necessary for every decision.
We learned to combine:
AI for interpretation + deterministic logic for reliability
We also learned that building around the user's next decision can be more valuable than simply adding as many features as possible.
A productivity tool becomes much more useful when it can answer:
"What needs my attention right now, and why?"
What's next for DeadlineFlow AI
Our next goal is to make DeadlineFlow automatically capture tasks from more of the places where work originates.
Future improvements include:
- 📅 Calendar integrations
- 📧 Email task extraction
- 🎓 Learning-platform integrations
- 💬 Communication-platform integrations
- 🔔 Smarter cross-platform notifications
- 👥 Collaborative tasks
- 🧠 Improved schedule optimization
- 📱 Native mobile support
We also want DeadlineFlow to learn from user behavior over time.
For example, if a user consistently takes longer than expected to complete programming assignments, DeadlineFlow could learn from that behavior and create more realistic schedules and earlier deadline-risk warnings.
Our long-term vision is for DeadlineFlow AI to become a personal execution layer between information and action.
Capture it. Plan it. Remember it. Finish it.
Built With
- css
- framer-motion
- geminiapi
- genkit
- goggle-gemini
- next.js
- node.js
- postgresql
- react
- react-three-fiber
- shadcn/ui
- supabase
- supabase-auth
- tailwind
- three.js
- typescript
- vercel
- zod
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