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🚀 Building Binary3D Gallery — A Full-Stack NFT Marketplace
I contributed to the full-stack development of Binary3D Gallery, an NFT marketplace designed to deliver a seamless experience for exploring and trading digital assets.
💻 Key Contributions
Frontend Development: Built intuitive, responsive interfaces for smooth digital asset browsing.
Backend Development: Developed backend services to support core marketplace functionality.
Rust API Integration: Integrated Rust-based APIs for wallet creation.
Database Development: Implemented database functionality to support reliable data management.
Performance & Security: Focused on building a responsive, secure, and maintainable platform.
✨ I enjoy tackling complex engineering challenges and turning ideas into reliable, user-friendly applications. Working on Binary3D Gallery allowed me to contribute across the stack while exploring the technologies behind blockchain-powered digital marketplaces.
I'm always excited to connect with developers, founders, and teams building innovative products!
• 6h
I like 3d things it looks really nice
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Built with Framer, it's a customizable starting point for creating a professional coaching or wellness website.
Explore Mindora and bring your next website idea to life.
Kindly check it out the template: https://www.framer.com/marketplace/templates/mindora-life-coach/
Great work
Coming-soon landing page design & development for Peak.
Most coming-soon pages just say "soon". I wanted this one to show the whole product. 🚀 🚀 🚀
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The stage
Instead of screenshots, the middle of the page plays the product: 9 features, 43 scenes, 325 frames. I designed every frame in Figma, then built a Figma-to-code pipeline and a React scene engine to play them. Elements morph from frame to frame while the pointer clicks, drags, types and scrolls. All of it is live HTML and CSS, not video, with its own player and speed control.
The details
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The look carries over from the Peak brand identity I shared a few days ago: Parchment and Carbon, Neue Montreal and its Mono, fine hairlines, and Ember used only as a signal.
Stack: Figma · Next.js · React · Claude Code · Tailwind CSS · WebGL · Supabase · Resend · Vercel · Cloudflare · WordPress
See it live → gopeak.design
CrowdVision AI – Real-Time Crowd Monitoring & Intelligence System
An AI-powered computer vision platform that detects and counts people in real time, monitors crowd density and occupancy, tracks entry and exit movements, and provides actionable crowd intelligence through a live monitoring dashboard.
Overview
CrowdVision AI is an intelligent crowd monitoring and safety analytics platform designed to help organisations monitor public spaces, understand crowd behaviour, and identify potentially unsafe crowd conditions.
The system uses computer vision and machine learning inference to analyse camera feeds and video streams, detect people, estimate occupancy, classify crowd density, and monitor movement across designated zones.
The Problem:
Monitoring crowded environments manually can be difficult, time-consuming, and inefficient. Security personnel may struggle to maintain accurate occupancy counts, monitor multiple zones, and identify unusual crowd conditions quickly.
CrowdVision AI was developed to explore how computer vision and real-time analytics could improve crowd visibility and monitoring.
My Solution:
I developed a computer vision system integrating a pretrained YOLO object-detection model with OpenCV for video processing.
The platform includes:
Real-time people detection and counting.
Crowd occupancy estimation.
Crowd-density classification.
Entry and exit line counting.
Zone-based monitoring.
Incident and anomaly monitoring workflows.
Camera health and tamper monitoring.
Evidence management and reporting.
Telegram alert notifications.
Remote edge camera processing.
Live monitoring dashboard.
Technical Implementation:
Machine Learning & Computer Vision
Python
YOLO / Ultralytics
OpenCV
NumPy
Pandas
Backend:
Python
FastAPI
REST API
Frontend:
React
Vite
JavaScript
Other Technologies
Git
GitHub
Telegram Bot API
JSON/CSV data storage
My Role
Founder & Full-Stack Machine Learning Developer
For entry/exit line counting, I'd test someone pausing on the boundary and stepping back without entering. Do you require a track to move fully between two zones before recording an entry, so small position changes don't become extra visits?