Trajectory: A More Realistic Way to Plan Your Goals

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.

What I Built

Trajectory is a work-in-progress project exploring a simple question: What if planning your day started with your goals, not just a list of tasks?

Students juggle courses, personal projects, assignments, and hobbies. Independent learners try to build new skills while managing existing responsibilities. It's easy to create an ambitious to-do list, but much harder to decide what deserves attention first and how much time to give it.

Trajectory is my attempt to make that process more realistic.

The initial version focuses on two core features:

• Goal-to-Plan: Describe a bigger goal and what you want to achieve. AI helps turn it into a structured plan with actionable steps and milestones.
• Daily To-do Planner: List the tasks you want to work on, assign each a deadline (2 days, 4 days, 1 week, or no deadline), and provide your available time. AI suggests a priority order, reasonable time allocations, and why each task matters.

The idea is to keep tasks and progress available across days while generating a fresh daily plan based on current priorities and unfinished work. Rather than treating every day as a clean slate, Trajectory aims to help users make steady progress toward their goals.

The longer-term vision includes more adaptive planning, progress-based forecasting, personalized career guidance, and a creative-practice journal where users can save sketches and reflect on their artistic development. These are future possibilities, not claims about the current implementation.

Demo

Project demo: https://trajectory-git-main-mystic-force.vercel.app/

The project is currently in development. The demo will be updated as the core features become functional and ready to showcase.

Code

GitHub repository: https://github.com/anweshasingh-dev/trajectory

How I Built It

The planned technology stack includes:

• Python and Flask for the backend.
• SQLite for persistent storage of goals, tasks, and progress.
• HTML, CSS, and JavaScript for the user interface.
• An open-weight instruction model, with a suitable inference setup to be selected and verified, for interpreting natural-language goals and proposing structured plans.
• Python application logic to validate inputs, enforce planning constraints, and manage task progress.

An important design principle is to keep AI suggestions separate from application-controlled logic. The model can help interpret goals, break work into actionable steps, and explain recommended priorities. The application should remain responsible for validating data and preserving user-approved changes.

The initial focus is deliberately small: generate a goal-based plan, prioritize daily to-dos, and retain progress between sessions. More advanced scheduling and forecasting can be explored once this foundation works reliably.

The final implementation, model choice, and verification results will be documented as development progresses.

Why Does Open Innovation Matter?

Personal planning involves information that can be sensitive, including goals, schedules, unfinished work, and learning habits.

Open-weight models and local inference offer an opportunity to experiment with AI-assisted planning without making every interaction dependent on a closed, externally hosted API.

For Trajectory, this approach could support:

1. More control: Developers can experiment with compatible models and adapt the system to their needs.
2. Privacy-conscious design: Local inference can keep prompts on the user's machine, provided the application and its other data flows are designed accordingly.
3. Accessible experimentation: Students and independent developers can explore AI-powered products without making a paid proprietary API the foundation of every interaction.

I want the AI to serve a clear purpose rather than simply add a chatbot to another to-do list. The aim is to explore how open models can help turn goals into actionable work while transparent application logic keeps the planning process grounded and manageable.

The open-weight model and inference setup are still being evaluated, so their final implementation and limitations will be reported honestly.

What's Next?

The immediate goal is to complete and test the two core features: generating goal-based plans and creating prioritized daily to-do plans with persistent progress.

From there, I want to learn what works in real planning scenarios before expanding the project. Trajectory is a small first step toward a more personal planning system that connects everyday actions with longer-term ambitions.