Secure Full-Stack E-Commerce Platform with Angular and Spring Boot

Secure Full-Stack E-Commerce Platform with Angular and Spring BootSecure Full-Stack E-Commerce Platform with Angular and Spring Boot

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DayakarReddy Gorita

• Oct 3

Designed and developed a secure, enterprise-level full stack e-commerce application supporting multiple product categories including books, coffee mugs, mouse pads, luggage tags, and lifestyle accessories. The platform enables seamless product browsing, secure authentication, and online payment processing with production-grade backend architecture.

Key Contributions & Features:

Developed RESTful APIs using Spring Boot for product management, order processing, and customer operations. Built a responsive frontend using Angular, implementing dynamic product listing, search functionality, and pagination. Integrated Stripe Payment Gateway to securely process credit card transactions. Implemented robust application security using JWT authentication, OAuth2, and OpenID Connect with Okta. Configured SSL/TLS encryption to ensure secure communication between client and server. Applied API-level security using Spring Security configuration. Designed relational database schema using MySQL for products, orders, and customer data. Implemented login/logout workflows with secure session handling. Managed environment-based configurations for Development, QA, and Production deployments. Optimized user experience with server-side pagination and search capabilities.

Backend Development Software Development Angular Java Full Stack Development Spring Boot

Drake Ballew

• Oct 2

Know someone who needs a haircut? 🙋 Send them to TrimsByTracy.com, where they will find the salon of Tracy Turnblad, a solo hairstylist in Baltimore who needed an expert to tame her booking flow the way she tames manes.

I had a lot of fun working on this Proof of Concept, from ideating design system inspiration to working through the nooks and crannies of a hairstylist's booking flow, to figuring out how to succinctly communicate the value added in an overview video. I also continue to learn each time I build something new with Lovable. This time I was focused on reducing the amount of time spent testing, which is what my "Process" video is about. I hope you'll take a look at that since I personally think what I shared there is very valuable and should be standard to all builders' workflow and possibly considered for productization by Lovable. Regardless, I hope you enjoy taking a look around TrimsByTracy! All built with care in Lovable. - Drake https://trimsbytracy.com/ https://trims-by-tracy.lovable.app

Completed work

Software Development UX Design Visual Design Adobe Premiere Pro Figma lovablechallenge Lovable lovablepartners

Rashida Abdul-rashid

• Oct 4

The delight of booking, Mr. Pinky made me smile.

Sadia Abdullah

• Oct 1

Problem: Apps need a secure, organized backend to handle users, orders, products and AI features.

Solution: Built a centralized API Gateway (BFF) in n8n Cloud. One webhook handles validation, API key security and rate limiting, then routes to sub-workflows: Users (register/login), Orders (stock check, totals, status), Products (search), and AI (OpenAI chat, recommendations, review summaries). Supabase is the database.

Tools: n8n, Supabase, OpenAI API, Webhooks

Self-initiated demo project.

AI Automation Supabase N8N OpenAI

Kapil Chauhan

• Oct 1

I'd test authorization separately for each sub-workflow, especially Orders. Does the gateway pass a verified user identity through, or does the Orders workflow check that the caller can access each order?

Iman Imran

• Oct 1

Complete stack RAG driven document intelligence assistant developed using FastAPI and React. Features multi-format ingestion support (PDF, DOCX, and scanned images through EasyOCR), with FAISS vector indexing, and 6 LLM driven modes operating on Groq’s Llama 3.3 70B model. Dockerized application via GitHub actions CI pipeline for automatic linting and build validation with sub-2 second response time in local tests. FAISS is chosen over Pinecone for quick in-memory computation without relying on cloud infrastructure, while Groq is selected over other options for quicker and cheaper inference than GPT-4 alternatives.