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Ranking Platform for padel players 🔥, they can register their matches, and be ranked with an ELO ranking system. It is a secondary product from SportChain
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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BG Remover is a high-precision, production-ready AI background removal studio and bulk editing suite built with Next.js 16, React 19, TypeScript, and Tailwind CSS.
Unlike traditional cloud-based background removers that mandate subscriptions, inject watermarks, or upload user photos to remote servers, BG Remover runs 100% locally in your web browser via WebAssembly (WASM).
It delivers sub-second (~85ms average) AI subject isolation, 4K HD transparent PNG exports, SVG vector outline traces, and a full-featured studio editor — completely free and 100% private.
LIVE:- https://bg-remover-one-blush.vercel.app/
Doing the removal in the browser with no uploads is a solid selling point. How fast is it on large files?
A little Three.js experiment — a torn-edge reveal built with a custom shader and Sobel edge detection.
I wanted the transition to feel more organic and physical than a typical image reveal.
Custom shader + Sobel edge for a torn reveal is the kind of WebGL detail people feel even if they can't name it.
Unused concept for a teacher feedback loop in an LMS.
The challenge was straightforward on paper: make it easier for teachers to give meaningful feedback across multiple classes, often with 100+ students.
But the real friction happened before they even started.
From our findings, teachers often felt overwhelmed by the amount of work waiting for them, so they defaulted to bulk feedback using templates.
It was faster, but it created another problem: students received feedback that felt vague, repetitive, and disconnected from their actual work.
For this concept, I explored two ways to help teachers get started without forcing them into one workflow:
— Focus manually on flagged assignments that need the most attention
— Use AI-assisted grouping to cluster assignments with similar patterns, so teachers can review them in context and respond more efficiently
The idea wasn’t to automate the feedback itself, but to reduce the friction around deciding where to start.
Still an unused concept, but I liked the direction.
What do you think?
Super clean work!