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Performance Optimization and Resource Benchmarking
[The Problem] Standard data processing practices often load entire datasets into memory, leading to severe server overhead, unpredictable crashes, and inflated cloud infrastructure costs when handling large enterprise files.
[The Solution] By implementing memory-safe streaming pipelines, resource consumption is reduced to a constant, minimal baseline. This drastically lowers hosting bills and allows the system to process infinite data volumes without requiring hardware upgrades.
Cost reduction | Infrastructure stability | Unrestricted scalability
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Footer designs for my latest framer templates.
Created with intentionality, each design representing true brand identity.
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Introducing Job Board Hub, a full-stack application built with the MERN stack to explore remote job opportunities across different areas such as technology, design, marketing, and more.
The main idea behind the app is to help users find job opportunities and quickly understand whether a position fits their profile.
One of the main features is AI Job Summary, an artificial intelligence integration that generates summaries of long job descriptions. Many job posts often include lengthy text, mixed requirements, and repetitive information, so the goal was to improve the user experience and make reading faster and clearer.
Beyond the visible feature, this project also allowed me to work on important backend decisions:
- AI integration through an AI Gateway
- The possibility to switch or combine models in the future
- Rate limiting to prevent abuse and control token usage
- Streaming responses to improve the perception of speed
- Clean backend architecture within a MERN app
- Organization of remote jobs by categories
The focus was not just to “add AI,” but to integrate it in a more thoughtful way: considering costs, user experience, scalability, and system control.
Job Board Hub is a simple app in concept, but it helped me demonstrate how an AI feature can provide real value when supported by good backend decisions.
I’m very happy with this project because it combines product, backend, and artificial intelligence into a useful and easy-to-understand experience.
#MERN #FullStackDevelopment #ArtificialIntelligence #BackendDevelopment #React #NodeJS #MongoDB #ExpressJS #AIIntegration #WebDevelopment
Zappy: Building a Real-Time Workflow Layer for WhatsApp-Based Businesses
Zappy is a mobile first shared WhatsApp inbox for small businesses, designed to turn chaotic customer messaging into structured team workflows.
The core problem is not messaging, but coordination. As small teams grow, conversations become fragmented, messages are missed, and there is no clear ownership or system of work.
I designed Zappy as a real time, multi tenant communication system where every business operates in an isolated workspace, and every conversation is structured, trackable, and assignable across team members.
The system is built with a contract first architecture using OpenAPI and shared types, ensuring the backend and mobile app stay fully in sync as the product scales.
Real time updates are handled through Socket.IO, with fallback polling for reliability across environments.
On top of this communication layer, AI is embedded directly into workflow moments, not as a separate tool. It assists with reply suggestions, conversation summaries, tone rewriting, sentiment detection, and off hours responses, all aimed at reducing cognitive load and response time for small teams.
Zappy is ultimately a workflow layer for WhatsApp based businesses, focused on structure, speed, and clarity in customer communication.
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