QuietGlass vs GitHub
Side-by-side comparison of features, pros & cons, pricing, and community votes (2026).
🏆 QuietGlass leads with 82 upvotes
Screen privacy for your Mac, with or without AirPods
QuietGlass is a free, open-source Mac application designed to enhance screen privacy effortlessly. It offers users the ability to blur or hide specific areas of their screen, protecting sensitive information from prying eyes. Unique features include integration with AirPods for look-away privacy and camera-based alerts that detect when someone else is in view, prompting you to take action or automatically blurring the screen. Built with native Swift code and optimized for macOS 14+, QuietGlass provides smooth performance and seamless privacy controls, including focus mode for active window clarity and owner recognition to ensure only you can disable protections. Its compatibility with notch controls makes it well-suited for modern MacBooks, offering a comprehensive privacy solution for professionals, remote workers, and anyone concerned about screen security.
Pros
- Open-source and free, offering transparency and community support
- Advanced privacy features like camera-based face detection and owner recognition
- Native Swift implementation ensures smooth performance on macOS 14+
- Supports specific areas and window focus modes for tailored privacy
- Integrates with AirPods for look-away privacy when needed
Cons
- Limited to macOS 14+ and Mac hardware, reducing compatibility with older systems
- Features may require some configuration for optimal use
- Currently lacks extensive user documentation or community resources
Best for
- • Securing sensitive work data during video calls or screen sharing
- • Protecting confidential information when working in public or shared spaces
- • Automatically blurring private windows or text during screen recordings
- • Using AirPods to discreetly look away from the screen during meetings
Pricing: Pricing not verified
Stop losing data science context. Build knowledge graphs.
KMDS (Knowledge Management & Data Science) revolutionizes how data scientists and developers manage complex workflows by transforming fragmented notebooks and data pipelines into comprehensive, structured knowledge graphs. By leveraging local large language models (LLMs), it enables users to scan repositories, create searchable archives of experimental histories, and visually audit data engineering artifacts—all within local environments, ensuring data privacy and security. This tool is ideal for teams and individual professionals aiming to preserve context, improve collaboration, and streamline their data science lifecycle. Its unique approach of converting scattered data assets into interconnected knowledge graphs makes tracking, understanding, and reusing data workflows more efficient than ever.
Pros
- Transforms unstructured notebooks into organized, searchable knowledge graphs
- Runs entirely locally, ensuring data privacy and security
- Leverages local LLMs for advanced scanning and chat capabilities
- Visualizes data workflows and engineering artifacts for easy auditing
- Enhances collaboration by maintaining context across projects
Cons
- May have a learning curve for users unfamiliar with knowledge graphs
- Dependent on local LLM performance, which can vary based on hardware
- Limited information on pricing and ongoing support options
Best for
- • Converting scattered notebooks into structured, searchable knowledge bases
- • Auditing and visualizing complex data pipelines
- • Documenting experimental histories for reproducibility
- • Collaborative data science projects requiring context preservation
Pricing: Likely follows a freemium model with core features available for free, and premium features or higher usage tiers available at a monthly cost. Exact pricing details are not publicly specified.