Tech tales & AI Adventures

When we set out to build RecycloAI , one of the biggest challenges - and most exciting opportunities - was developing a machine learning model capable of identifying waste types from user-submitted images. Here’s a look into how we approached this challenge.     Problem We’re Solving Improper waste disposal is a massive contributor to environmental degradation. Our goal was to create a system that could: Accurately classify waste types like plastics, metals, organic, e-waste, etc. Offer disposal or recycling instructions based on the result Work fast enough to give real-time or near-instant feedback        The Model Behind RecycloAI We used a custom-trained YOLOv8 classification model , fine-tuned using a labeled dataset of waste images. The model was trained to recognize various categories, including: plastic, paper, glass and more. We trained the model using PyTorch and Ultralytics' YOLOv8 framework in Google Colab. ...

 Smarter Recycling Starts Here:     Introducing RecycloAI A large portion of waste is not recycled at all . Bin contamination, confusion about materials, and lack of real-time guidance are just a few of the challenges that undermine effective recycling. As a result, millions of tons of potentially recyclable materials end up in landfills and oceans each year. That’s where RecycloAI comes in—an AI-powered solution that helps users identify and sort waste in seconds, boosting recycling accuracy and supporting a more circular economy.     The Problem "Waste is piling up—and we’re sorting it wrong." Every year, vast amounts of recyclable materials are lost due to improper sorting. Individuals want to recycle but often don’t know how (e.g., “Is this plastic #5 or #7?” ). Organizations face growing pressure to meet sustainability targets and avoid costly contamination fees. The result? A global recycling system overwhelmed by confusion and inefficiency...