A productivity journal app built with Next.js, Tailwind CSS, and Supabase. This app helps users track their daily journal entries, set and manage goals, and gain insights into their productivity. Powered by OpenAI API for advanced analysis and insights.
Chrome Extention for ADHD Block Distracting
Extension for Google Chrome that helps ADHD people to remember important things by displaying them on new tabs
synapz is a research prototype exploring how large language models can adapt teaching content to different cognitive styles. built over a 48-hour sprint with a strict $50 api budget, this project implements a scientific framework to test whether adaptive teaching produces measurably better results than static approaches.
🌎 API and management portal to assist parents of children with ADHD.
Scripts related to "Associations between attention-deficit hyperactivity disorder (ADHD) symptom remission and white matter microstructure: A longitudinal analysis"
About 🗂️ A revolutionary flashcard app for macOS , that will have rich text editor , multiple interface improvement, material design anki importing etc
Notion Database Notifications
This Flutter app uses the Pomodoro Technique for ADHD productivity. Its minimal interface enhances focus with timers and breaks for effective work management. Version 1.0.0 is live now!
🌟 FocuSpace is a focus-enhancing AI powered web app designed for neurodivergent individuals and anyone seeking a personalized, distraction-free workspace.
A ML/DL pipeline for classification of ADHD children and healthy controls using EEG signals from a public dataset.
A work in progress Telegram bot to help people with Executive Function Disorder (ADD, ADHD, and CPTSD) with certain tasks that are more difficult than they need to be.
Repository and Source Control for Pillcount.Org's Free Beck's Anxiety & Depression Inventory Website
Neovim Plugin plugin to seamlessly integrate Bluesky to bring social updates directly into your editor.
Aplicativo para da ferramentas a pessoas com TDAH para melhor se organizarem.
This project used machine learning to classify ADHD based on EEG data. We preprocessed the EEG signals, extracted various features, and used LDA for dimensionality reduction. A voting ensemble of classifiers achieved 72% accuracy in distinguishing between ADHD and control groups.
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