Basics of production grade development and best practices. Fetching data from backend via APIs and displaying content on Frontend.
-
Updated
Jan 10, 2025 - JavaScript
Basics of production grade development and best practices. Fetching data from backend via APIs and displaying content on Frontend.
Novel unified representation to solve all the sub-tasks of argumentation mining
Data & AI Engineer | Azure · Databricks · PySpark · LLM Agents | Ex-Mercedes-Benz MO360 Platform
A secure and scalable Full Stack Nextjs Admin built with Laravel + Nextjs and powered by Passport authentication. Designed to manage users, orders, and admin operations through clean RESTful endpoints
Portfolio v5: Primary implementation of the V26 High-Fidelity Rig. Featuring Houdini-powered atmospheric lighting, decoupled token-based architecture, and Singleton-driven performance logic.
tampilan antarmuka verifikasi
study-trackは日々の勉強を記録し、共有できるwebアプリケーションです
Production-ready, ultra-low-latency Runtime Threat Detection & Response (RTDR) engine. Uses Spring Boot, Redis sliding windows, and a multi-variable risk scoring matrix to intercept application-layer anomalies and stream active mitigation commands down to remote host daemons via SSE pipelines.
A full-stack Design-to-Delivery (D2D) Industrial Process Management System. Features a React frontend and Flask backend to streamline manufacturing, logistics, and proposal workflows.
A multilingual GIS dashboard for exploring buildings on a Cesium 3D map.
FULL-STACK APPLICATION DESIGN AND DEVELOPMENT: Introduction to design and development of full-stack web applications. Topics include asynchronous programming; non-relational data stores; use of APIs; serverless (cloud-based) applications; decoupled client/server architectures; performance; testing; packaging; and deployment.
Lightweight mediator for JavaScript and TypeScript that simplifies event-driven architecture, CQRS, and decoupled application design.
TransactAlert — Asynchronous Fraud Alert Service | Java, Spring Boot, JMS ● Decoupled transaction ingestion from fraud analysis using a REST API and ActiveMQ Artemis message queue, so fraud checks run independently of the customer-facing request path instead of blocking it
An AI-driven gift recommendation platform utilizing a decoupled architecture with a Next.js (TypeScript) frontend, a FastAPI (Python) backend, and a PostgreSQL database.
To associate your repository with the decoupled-architectures topic, visit your repo's landing page and select "manage topics."