¶ Summary
A secure, multi-tenant personal task planner application. The application must not only manage tasks but also optimize long-term scheduling through a simulation algorithm that ensures balanced variety, respecting time and proportion constraints.
The key objective is to develop and deploy a solution that is scalable, secure, and capable of generating optimized task schedules, while supporting multiple users in an isolated, cloud-hosted environment.
The architecture is designed using a secure, multi-tenant framework with robust user authentication and data isolation. A Monte Carlo simulation algorithm is implemented to evaluate task permutations and optimize schedules for variety and proportional balance over time. Deployment leverages cloud-native technologies — such as container orchestration and managed databases — to ensure scalability and minimal maintenance overhead.
The resulting application delivers secure, personalized task planning with intelligent long-term scheduling. Users benefit from optimized, balanced task distributions, and the cloud-based setup ensures accessibility, reliability, and easy scaling as user demand grows.
¶ Balanced Planner web-application tech-stack
- a support for creating a long-term plan with bits of long tasks scheduled with high variety but within the given contraints of time and proportions. Tasks may be: reading long books, studying a long course, doing regular workout. Proportions may be set as: 30% culture, 30% sports, 30% career, 10% other.
- Full-stack development
- Demo Video
- tech stack:
- Nginx + letsencrypt
- Webix.js
- Flask (python)
- Mongodb
- Docker
- AWS Fargate (servless container mgr)
- GitHub source code
- Webflow (state diagram)
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