Full-stack · AI integration
Demo / staging
Food records, personal and group preferences, and recommendations brought together in a multi-client product.
My contribution Handled backend and web development, AI integration, infrastructure, and delivery throughout the team project.
Spring Boot PostgreSQL React Python AWS One public boundary One public boundary Web + Android Spring Boot PostgreSQL Private AI services Recommendation Cooking Chat Backend owns Permissions · Rules · Validation One public boundary
Web + Android
One shared public API
↓ Spring Boot
Permissions · Rules · Validation
↓ Conceptual system illustration Enterprise workflows
Working prototype
A construction project workspace for tasks, progress reports, risks, team membership, and documents.
My contribution Built the Spring Boot backend and React management interface, including project access rules, Redis caching, and an AWS deployment workflow.
Java Spring Boot PostgreSQL Redis React AWS ECS A shared project context A shared project context Project Tasks Reports Risks Documents Ownership + membership Access and management are separate decisions A shared project context
Project
Ownership + membership
↓ Tasks · Reports · Risks · Documents
Access and management are separate decisions
Conceptual system illustration Local-first · Data integrity
Windows beta
An offline desktop ledger for multi-currency cash, investments, and net worth, with traceable changes and explicit data-quality checks.
My contribution Developed the React desktop interface and Rust application core, including decimal rules, import reconciliation, and encrypted backup and recovery.
Rust Tauri SQLite React TypeScript One authority for financial rules One authority for financial rules React UI Rust core SQLite Preview Validate Commit Decimal rules Traceable changes One authority for financial rules
React UI
Preview
↓ Rust core
Validate · Decimal rules
↓ SQLite
Commit · Traceable changes
Conceptual system illustration ML · Anomaly detection
ML experiment
Graph and hypergraph anomaly-detection experiments on server monitoring time series, from data preparation to model comparison.
My contribution Implemented data preparation, graph construction, autoencoder models, anomaly scoring, and batch experiments against several baselines.
Python PyTorch NumPy scikit-learn Relationships, then reconstruction Relationships, then reconstruction Signal windows Hypergraph Autoencoder Anomaly score Experimental pipeline · schematic only Relationships, then reconstruction
Signal windows
Server Machine Dataset
↓ Hypergraph
Build relationships
↓ Autoencoder
Reconstruct signals
↓ Anomaly score
Experimental pipeline · schematic only
Conceptual system illustration