Freelancers using React in FaisalabadFreelancers using React in Faisalabad
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Mubeen Ahmad's avatar
Mubeen Ahmad
Faisalabad, Pakistan
Full Stack Developer, Frontend & Backend Engineer
5.0
Rating
3
Followers
Full Stack Developer, Frontend & Backend Engineer
Cover image for AI Consulting Platform in MERN
AI Consulting Platform in MERN Stack | React | Next | Node Developed a highly responsive, enterprise-grade B2B platform for Quantum Sync, a premier IT consulting and technology services firm. Engineered a clean, corporate UI utilizing the MERN stack to showcase their extensive service offerings, industry expertise, and strategic partnerships with tech giants like Microsoft and IBM. Architected a scalable frontend using React to ensure fast loading times and intuitive navigation. Optimized the entire platform for technical SEO, strengthening their digital authority and streamlining corporate client inquiries for enterprise tech solutions.
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Cover image for MERN Stack-based SaaS Inventory Management
MERN Stack-based SaaS Inventory Management System using React.js, Node.js, and MongoDB Build a comprehensive SaaS inventory management solution specifically designed for salvage yards and auto parts businesses. Built on the robust MERN stack, this powerful web-based software maximizes operational efficiency, inventory accuracy, and sales tracking. The platform features an intuitive interface that simplifies complex part tracking, VIN decoding, and custom search functionalities. Integrated advanced invoicing capabilities, allowing users to effortlessly compile multiple parts into single invoices for seamless printing, emailing, or SMS texting, ensuring accurate billing.
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Cover image for Resilient Leader Assessment | React
Resilient Leader Assessment | React | Laravel | Full Stack Development I developed a full-stack web application for a leadership resilience assessment. The platform guides users through a 49-question survey designed to evaluate resilience across four key domains. Key features include: User-friendly assessment experience, Instant results upon completion, and Automated generation of a comprehensive 14-page resilience report. I built the frontend using React to ensure a fast, responsive, and seamless user experience. The backend was powered by Laravel, handling secure data processing, report generation, and user session management.
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Cover image for MERN Stack Development | Pastebin
MERN Stack Development | Pastebin for Code and Text | Full Stack Designed for sharing code and text snippets neatly and efficiently. Users can paste, store, and share text or code online, making it an ideal tool for developers and collaborators. The website ensures quick and easy distribution of content, enabling seamless sharing with friends or teams.
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React(8)
Sameer Sajid's avatar
Sameer Sajid
Faisalabad, Pakistan
Lead Dev | Turning Complex AI Ideas into High-End Products
32
Followers
Lead Dev | Turning Complex AI Ideas into High-End Products
Cover image for SoulDeeds — Full-Stack Community &
SoulDeeds — Full-Stack Community & Connection Platform Project Overview SoulDeeds is a high-performance, full-stack web application designed to facilitate meaningful and purposeful connections within a community. As a Lead Developer, I architected and built the entire system focusing on scalability, real-time interactions, and a premium user experience. The Challenge The goal was to create a secure, high-traffic platform that balances a modern UI with complex backend logic for user verification and community matchmaking. Core Contributions & Features Full-Stack Architecture: Developed a robust end-to-end system using Next.js and React for the frontend, and Node.js for the backend. Database Management: Integrated MongoDB to handle complex user data, relationships, and dynamic content efficiently. RESTful API Development: Built secure and scalable REST APIs to manage authentication, profile verification, and data flow between the frontend and server. UI/UX Specialist Approach: Implemented a sophisticated, responsive design using CSS3 and modern styling libraries, ensuring a seamless experience across all devices. Community Features: Engineered logic for verified profiles and "halal connections," prioritizing user safety and community integrity. Tech Stack Frontend: Next.js, React.js. Backend: Node.js, Express. Database: MongoDB. APIs: REST API Architecture. Styling: Advanced CSS3, Responsive Design.
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Cover image for Project: WeVersity — E-Learning Ecosystem WeVersity
Project: WeVersity — E-Learning Ecosystem WeVersity is a high-performance "Learn to Earn" platform bridging the gap between education and modern tech. Core Features: Dual-Portal: Custom dashboards for Instructors & Students with secure RBAC. Live Classes (Web): Real-time interactive sessions with experts. TikTok-Style (App): Educational shorts on Google Play Store for fast learning. Course Library: 200+ Free Courses optimized for all devices. Technical Stack: Frontend: Next.js & React.js. Backend: Supabase & PostgreSQL. Infrastructure: VPS with Vercel CI/CD. From database architecture to a LIVE Play Store app, WeVersity showcases my full-stack expertise.
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Cover image for Purpose & Requirements: The goal
Purpose & Requirements: The goal of this project was to craft a luxury shopping experience that leverages user browsing history and behavior to provide personalized, real-time recommendations. To meet high-end user requirements, I integrated an editorial-style layout, infinite scrolling fashion grids, and an AI concierge. This ensures the shopping journey is far from ordinary, transforming it into a truly "premium digital experience". Key Benefits: High Conversion: AI-powered recommendations significantly boost sales potential and user engagement. Ultra-Fast Performance: Built with React 19 and Vite, ensuring blazing-fast loading speeds for a seamless experience. Modern Aesthetics: The editorial design elevates the perceived value of luxury brands and enhances brand identity.
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Cover image for A high-performance, Full-Stack React application built to sh...
A high-performance, Full-Stack React application built to showcase my development agency's services and portfolio. This project demonstrates my ability to build scalable, AI-integrated web solutions with a focus on premium user experience. Key Features: Seamless page transitions, AI-powered content generation (Gemini API), and a secure backend for data management. Tech Stack: React 19, TypeScript, Tailwind CSS, Framer Motion, and Supabase. Performance: Optimized for speed and responsiveness across all devices using modern CSS architecture. Skills & Tools Section mein ye select karen: Is project ke liye jab Skills aur Tools ka option aaye, to ye lazmi add karna: Skills: Full-Stack Development, Web Animation, AI Integration. Tools: React, TypeScript, Supabase, Framer Motion, Tailwind CSS.
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React(4)
Owais Raza's avatar
Owais Raza
Faisalabad, Pakistan
Full Stack Developer | Agentic Systems | MVP & SaaS Platform
New to Contra
Full Stack Developer | Agentic Systems | MVP & SaaS Platform
Cover image for Just launched a powerful SaaS
Just launched a powerful SaaS project: One-Click SEO Audit Marketplace Built a complete platform that helps users analyze and improve their website SEO in seconds. From technical audits to keyword insights and performance tracking, everything is streamlined into a simple, user-friendly experience. The platform scans websites, identifies SEO issues, and provides clear, actionable recommendations to boost rankings and traffic Key highlights: • Instant SEO audits with detailed reports • Multi-site management dashboard • Credit-based service marketplace • Scalable SaaS architecture Designed for agencies, marketers, and business owners who want fast, data-driven SEO decisions without complexity. Check it out: https://oneclickseo.ai/
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Cover image for CTRBoss, Programmatic CTR Optimisation &
CTRBoss, Programmatic CTR Optimisation & Local SEO GeoGrid Rank Tracking SaaS Built an enterprise-grade CTR optimisation and local SEO rank tracking platform for digital marketing agencies combining stealth browser automation, geo-targeted proxy routing, smart traffic scheduling, and geographic GeoGrid rank visualisation into a single fully automated system. Running CTR campaigns at scale is one of the most technically demanding challenges in SEO. Every bot session has to appear indistinguishable from a real human user across browser fingerprint, user agent, proxy IP, search behaviour, and on-site activity. Off-the-shelf automation tools get detected and blocked immediately. Agencies needed a platform that could run hundreds of realistic sessions per day across multiple client campaigns, target specific geographies, and visualise local SEO performance all without manual intervention. CTRBoss is built on Django, Flask microservices, React, Next.js, and PostgreSQL. The core bot engine runs Undetected ChromeDriver with selenium-stealth, removes webdriver flags via Chrome DevTools Protocol, sets device-specific user agents, and routes every session through geo-targeted residential proxies from Geonode and Proxy Empire with country, state, and city-level granularity. For organic search campaigns, the bot types the keyword into Google, locates the client's website across organic results, Knowledge Panel, and paginated SERPs, clicks through, and spends session time on-site with multi-page browsing and gradual scrolling to mimic real human behaviour. Google Maps campaigns use a three-tier detection system covering the Knowledge Panel, the local 3-pack, and full Maps results with pagination. The Django smart scheduler runs every minute implementing time-of-day traffic ramping so sessions are light overnight and peak during business hours, enforcing per-keyword interval rules and daily search limits, pre-verifying proxies before each run, and auto-restarting the Flask bot server on consecutive errors. Up to 12 parallel keyword campaigns run simultaneously across multiple clients. The GeoGrid rank tracker queries DataForSEO's Google Maps SERP API across a coordinate grid producing a visual map showing exactly where a Google Business Profile ranks across its target area and where competitors dominate. Three payment gateways Stripe, PayPal, and Braintree with a credit-based billing system made the platform commercially ready at launch. Tech Stack: Django · Flask · React · Next.js · PostgreSQL · Celery · Redis · Selenium · Undetected ChromeDriver · Playwright · Geonode · Proxy Empire · DataForSEO · Stripe · PayPal · Braintree · ApexCharts
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Cover image for Keyword Finder, AI SEO Keyword
Keyword Finder, AI SEO Keyword Research & SERP Intelligence SaaS Built a distributed SEO research platform that expands a single seed keyword into thousands of opportunities combining Google Ads volume data, live SERP analysis, NLP competitor scoring, domain authority metrics, and AI content brief generation into one unified workflow with no CSV exports or tool-switching. SEO agencies were stitching together four or five separate tools a keyword research tool, a SERP analyser, a domain authority checker, an NLP scorer, and a rank tracker and manually reconciling their exports in spreadsheets. Scaling that workflow across multiple clients and thousands of keywords every month was unsustainable. Keyword Finder is built on Django 5, FastAPI, Flask microservices, React 18, and GPT-4o. The platform supports 4 keyword generation modes — Seed, URL, Competitor, and Bulk. In Seed mode it builds prefix, suffix, and alpha combination lists, fires them to Google Autocomplete in parallel with rotating proxies, then runs a second deep expansion round before fetching volume, CPC, and competition data from the Google Ads API via OAuth2. SERP analysis jobs are distributed across a dedicated Flask scraping microservice fleet with round-robin load balancing and a four-tier fallback chain direct, Bright Data, scraping.narf.ai (http://scraping.narf.ai), and CrawlBase — ensuring near-100% page retrieval success regardless of target site protection. Each competitor URL is processed through a full NLP pipeline using YAKE and NLTK, with domain authority pulled from Moz and Majestic via DomDetailer and cached locally to avoid redundant API calls. The Ranking Recipe lets agencies define custom Easy, Medium, Hard, and Easiest thresholds across 12 metrics turning keyword prioritisation from guesswork into a data-driven decision. AI content briefs are generated using GPT-4o and Cohere with full keyword context, cutting brief writing time from hours to minutes. Real-time job progress is delivered via Django Channels WebSockets, with Celery and Redis managing the async scraping queue. Supports 200+ Google country domains for multi-market research. Tech Stack: Django · FastAPI · Flask · React · GPT-4o · LangChain · Celery · Redis · PostgreSQL · Google Ads API · Bright Data · YAKE · NLTK · Moz · Majestic · Material UI · Redux
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Cover image for WriteWave, Enterprise AI Content Automation
WriteWave, Enterprise AI Content Automation SaaS for Agencies Built an end-to-end AI content automation platform for digital marketing agencies covering everything from SERP competitor research and NLP keyword extraction through AI article generation, image creation, schema injection, and auto-publishing to 10+ CMS platforms, all with zero manual steps between brief and live URL. Agencies managing content for multiple clients were stuck repeating the same manual workflow for every article SERP research, keyword extraction, writing, sourcing images, adding schema, scheduling, and publishing across dozens of clients. Scaling to hundreds of articles per month was operationally impossible without full automation. WriteWave is a multi-tenant agency SaaS built on Django 5, Celery, React 18, and GPT-4o. The platform offers 8 distinct article generation modes including a one-click engine that scrapes top SERP competitors, extracts NLP keywords via YAKE and NLTK, builds an optimised outline, generates full HTML content, creates AI images per section, injects internal links from sitemap crawls, adds DoFollow and NoFollow external links, embeds YouTube videos, and scores every article from 0 to 100 against NLP targets. A reading-level humanisation system adjusts vocabulary complexity from Grade 5 through college graduate. The standout feature is an Agentic AI Journalist powered by VAPI an AI phone agent that autonomously calls real human experts, conducts a structured interview, transcribes the conversation, and generates a complete publishable article from the transcript. Agencies get genuinely expert-sourced content at scale without journalist hours. Business Profile articles pull live Google reviews, Maps photos, and inject JSON-LD LocalBusiness schema automatically. All 10+ publishing integrations — WordPress, Shopify, LinkedIn, Wix, Squarespace, Medium, and more — adapt HTML per platform so no manual reformatting is ever needed. Real-time article generation progress is delivered via Django Channels WebSockets, with Celery and Redis handling async workloads at agency scale. Tech Stack: Django · React · GPT-4o · LangChain · Celery · Redis · PostgreSQL · VAPI · OpenAI · NLTK · Material UI · TipTap · Redux
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Jaleed Ahmad's avatar
Jaleed Ahmad
Faisalabad, Pakistan
AI/ML Engineer integrating DevOps & Cloud.
New to Contra
AI/ML Engineer integrating DevOps & Cloud.
Cover image for AuraBeat: Context-Aware Music Curator Project Overview A
AuraBeat: Context-Aware Music Curator Project Overview A full-stack, AI-driven application that generates highly personalized music soundscapes by blending real-time environmental data with deep user sentiment analysis. Built to seamlessly integrate advanced LLM decision-making with frontend aesthetics, the platform curates audio-visual experiences that map exactly to the user's current mood and local weather conditions. Core Technologies & Frameworks React 18, Tailwind CSS, Framer Motion Node.js, Express.js Google Gemini AI YouTube Data API v3 Open-Meteo & Nominatim APIs Key Features & Engineering Highlights Context-Aware LLM Pipeline: Engineered an AI pipeline using Google Gemini to perform sentiment analysis on user input and merge it with real-time weather analytics for highly accurate, contextual song recommendations. Dynamic UI & Color Theory: Built an ultra-responsive frontend featuring a dynamically shifting UI color palette that programmatically updates based on the AI's aesthetic evaluation of the recommended track. Multi-API Orchestration: Seamlessly integrated multiple external APIs (YouTube, Open-Meteo, Nominatim) within an Express.js backend to stream official video content and fetch localized environmental data. Decoupled Architecture: Designed a robust client-server architecture separating the Vite/React frontend from the Node.js backend, ensuring secure API key management and scalable request handling.
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Cover image for Blizzup Agentic Bike Dealership Project Overview A
Blizzup Agentic Bike Dealership Project Overview A sophisticated, full-stack AI-driven dealership platform that leverages Agentic AI to provide deep technical comparisons, transparent mathematical scoring, and automated inventory management. Built to fulfill a "Fullstack + AI Developer" assessment, the system uses a ReAct (Reason + Act) loop, allowing the AI to autonomously fetch real-world data and execute complex backend functions. (Note: The live AI agent functionality is currently disabled.) Core Technologies & Frameworks React, Tailwind CSS Node.js, Express Google Gemini AI (@google/generative-ai) MongoDB Atlas / Mongoose Pollinations.ai (http://Pollinations.ai) (Image Generation) Key Features & Engineering Highlights Agentic AI & Function Calling: Engineered a state-machine ReAct loop where the AI autonomously decides when to trigger real-time backend functions to retrieve inventory data, moving beyond traditional static prompting. Explainable Scoring Engine: Developed a dynamic scoring system that evaluates bikes across five strict metrics (Price, Fuel Average, Engine Power, Value, Features), featuring "Thinking Accordions" that expose the AI's internal mathematical reasoning to the user. Automated Bulk Ingestion: Built an intelligent pipeline allowing admins to ingest multiple vehicles by name; the AI automatically fetches exact technical specifications and generates high-fidelity photographic prompts. Dynamic Image Generation & Self-Healing: Integrated Pollinations.ai (http://Pollinations.ai) to dynamically generate accurate vehicle images based on AI-classified categories (e.g., Mountain Bike vs. Superbike), alongside an automated admin utility to repair and refresh low-quality database thumbnails.
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Cover image for AI Image Forensics Web Application Project
AI Image Forensics Web Application Project Overview A professional-grade, multimodal web application designed to detect and analyze AI-generated visual content. By combining traditional image processing techniques with advanced Large Vision-Language Models (VLMs), the platform provides comprehensive, automated analyses of image authenticity, digital signatures, and physical integrity. Core Technologies & Frameworks Next.js (React) Python, FastAPI Google Gemini Vision API Docker, Google Cloud Run Key Features & Engineering Highlights 8-Stage Forensic Pipeline: Engineered a highly robust image analysis pipeline that sequentially executes metadata scanning, quadrant tiling, Error Level Analysis (ELA), and Canny Edge detection. Multimodal AI Analysis: Integrated Google Gemini Vision as an automated "forensic analyst" to evaluate visual inputs for physical inconsistencies, blending errors, and AI generation artifacts. Decoupled Microservices Architecture: Built a high-performance Next.js frontend paired with a highly scalable Python/FastAPI backend to efficiently process complex mathematical image transformations. Cloud-Native Deployment: Established automated workflows for containerizing the application with Docker and deploying the backend services to Google Cloud Run for scalable compute. Data Visualization UI: Developed an intuitive, user-friendly dashboard to clearly present complex forensic findings, edge maps, and AI confidence scores to end-users.
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Cover image for AI Study Notes Agent Project Overview A
AI Study Notes Agent Project Overview A cloud-native, multi-user AI application designed to transform how users interact with and synthesize study materials. Built with a focus on end-to-end engineering and MLOps, this platform leverages advanced Retrieval-Augmented Generation (RAG) to deliver context-aware document analysis and automated study aids. Core Technologies & Frameworks Python, Streamlit Google Gemini 2.5 Flash LangChain, ChromaDB Supabase (PostgreSQL) gTTS (Google Text-to-Speech) Key Features & Engineering Highlights Intelligent Document Analysis: Engineered a robust RAG pipeline utilizing ChromaDB and LangChain to process documents and generate highly accurate, citation-backed insights. 1-Click Anki Flashcard Generator: Developed an automated pipeline that extracts key concepts from text and instantly formats them into Anki-ready flashcards for spaced-repetition learning. Audio Podcast Conversion: Integrated gTTS to automatically convert study notes and summaries into audio formats, allowing users to consume educational content on the go. Scalable Cloud Architecture: Designed a multi-user environment backed by Supabase PostgreSQL, utilizing UUIDs for secure, persistent data storage and user isolation. LLM Output Formatting: Implemented strict prompt engineering and formatting harnesses to prevent LLM output drift, ensuring 100% reliable and seamless database insertions.
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Khadija Hameed's avatar
Khadija Hameed
Faisalabad, Pakistan
Professional Front End developer
Professional Front End developer
Cover image for High-Converting Landing Page
High-Converting Landing Page
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Cover image for Portfolio Website
Portfolio Website
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Cover image for E-commerce Site Project
E-commerce Site Project
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Yousaf Sabir's avatar
Yousaf Sabir
Faisalabad, Pakistan
Fullstack Typescript And Python Engineer 🚀
Fullstack Typescript And Python Engineer 🚀
Cover image for Bellscribe.com (Web3 Inscription Service)
Bellscribe.com (Web3 Inscription Service)
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Cover image for Pixel-Perfect Multi-Page Portfolio Landing Page
Pixel-Perfect Multi-Page Portfolio Landing Page
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Cover image for Web Scraping With Python Fastapi And Selenium
Web Scraping With Python Fastapi And Selenium
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