Grayhound
Grayhound: PC optimization tool for removing bloatware
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Minwoo Kim

Grayhound - Grayhound: PC optimization tool for removing bloatware

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Grayhound is an open-source PC optimization tool for removing bloatware. It leverages a LLM with RAG to build an always up-to-date threat list by analyzing the latest discussions from IT communities, ensuring the most relevant protection.

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Minwoo Kim
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Hi everyone 🙂, I’m a junior developer and wanted to share a project I’ve been working on. I'm hoping to get some feedback and ideas from the wider IT community. Project Name: Grayhound Why Grayhound? I noticed that many bloatware cleaners rely on static blocklists, which often miss region-specific or newly popular grayware (especially outside the US). As a personal learning project, I wanted to create something more adaptable. Grayhound is a tool that updates its own definitions by analyzing discussions from IT communities (like forums and Reddit). This allows it to detect software that might only be considered "bloatware" in specific countries like the US, Korea, China, Japan, India. What does it do? -💡 AI-Driven: Uses Retrieval-Augmented Generation (RAG) with LLMs to scan tech forums and build a dynamic, transparent threat database. - 📖 Open & Transparent: 100% open-source (MIT license). It explains why a program is labeled as bloatware, not just showing a list. - ✅ User in Control: Scans your PC, but every suggestion is reviewable before you take any action. - 🖥️ Platform: Currently works for Windows 11 only (Tauri frontend + Python backend). Why am I sharing this? - I’d be incredibly grateful for any feedback or ideas! - Is this community-driven detection approach useful? - Are there similar tools I should learn from? Any thoughts on handling edge cases or making it more robust? If you'd like to test it out or contribute, the code is open and PRs/issues are always welcome. GitHub: https://github.com/RenardtheWolf... Note: This tool requires admin rights for some actions. Please back up your data and review all detected items before cleaning. Thank you for reading!