Site blocker Chrome extension to reduce digital distractions
A Chrome extension that blocks distracting websites to help users stay focused. The extension was built by a developer who struggled with distractions during college, starting as a rough class project and later rebuilt from scratch. No LLM usage is mentioned.
Related
Content Chef: browser extension to block AI-generated YouTube videos
Content Chef is a browser extension that lets users filter out AI-generated or otherwise unwanted content from their YouTube feed. It provides customizable filters that can be toggled on or off, giving users control over what they see. The extension addresses the problem of low-quality or unwanted AI content cluttering YouTube recommendations.
Chrome extension turns Netflix into language learning tool
A Chrome extension that overlays language learning features on Netflix, allowing users to learn English, French, or Spanish while watching shows. It uses LLMs to provide translations, explanations, or interactive subtitles, making passive viewing into active learning.
ADHD habit app that unlocks habits gradually to prevent overwhelm
BetterADHDay is a habit tracker designed for people with ADHD. It uses a gradual unlocking mechanism: users start with one habit and earn more slots by maintaining streaks, preventing the overwhelm that leads to app abandonment. The app is built without LLM features, but the creator also built a separate tool that uses LLMs to generate structured engineering prompts.
LLM-powered learning tool that streams generated lessons to avoid user abandonment
A learning tool that generates personalized lessons from a user's profile using an LLM. The key insight was that streaming the lesson output (instead of showing a blank spinner during 50-second generation) dramatically improved user retention. Solves the problem of users assuming the app is broken when generation takes time.
YouTube watch history analyzer for parents
A tool that analyzes a child's YouTube watch history and generates a 'brainrot risk report' for parents. It uses LLMs to identify patterns like repeat channels, addictive loops, late-night spikes, and content quality, helping parents understand what content shapes their child's feed without relying on screen time alone.