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Browser Copilot

Use existing or create your own AI helpers to make your web application tasks easier.
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Browser Copilot
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Introduction

Browser Copilot is an open-source Chrome extension that empowers users to integrate AI assistants into their web browsing experience. It provides a framework for using existing or custom AI assistants to enhance productivity and streamline tasks across various web applications. This tool is designed to be flexible, allowing users to easily incorporate third-party copilots or build their own, all within the familiar environment of Google Chrome.

Feature

Integrate Third-Party Copilots

Users can quickly add and utilize existing AI assistants by simply providing the URL where the copilot is hosted. This feature allows for immediate access to a wide range of AI-powered tools and services.

Build Custom Copilots

Browser Copilot offers a robust framework and user interface for developers to create and integrate their own AI assistants directly into Google Chrome. This feature empowers users to tailor AI solutions to their specific needs and preferences.

Open-Source Community

The extension fosters a vibrant open-source community where users can share and benefit from copilots developed by experts and enthusiasts. This collaborative environment encourages innovation and knowledge sharing.

Free to Use

Browser Copilot is available as a free Chrome extension, ensuring accessibility for all users without any cost barriers.

Flexible AI Integration

The extension supports integration with any AI assistant that has a URL or API, providing users with a wide range of options to enhance their browsing experience.

FAQ

How do I use Browser Copilot?

To use Browser Copilot, simply add the extension to your Chrome browser and follow the provided instructions to integrate your desired AI assistant.

Can I use Browser Copilot with any AI assistant?

Yes, Browser Copilot is designed to work with any AI assistant that has a URL or API, offering great flexibility in choosing and integrating AI tools.

What are some tips for getting the most out of Browser Copilot?

To maximize the benefits of Browser Copilot:

  1. Explore available copilots and integrate those that best suit your needs.
  2. When building your own copilot, adhere to the guidelines and best practices provided by the extension.
  3. Engage with the open-source community to discover new copilots and share your own creations.

Latest Traffic Insights

  • Monthly Visits

    193.90 M

  • Bounce Rate

    56.27%

  • Pages Per Visit

    2.71

  • Time on Site(s)

    115.91

  • Global Rank

    -

  • Country Rank

    -

Recent Visits

Traffic Sources

  • Social Media:
    0.48%
  • Paid Referrals:
    0.55%
  • Email:
    0.15%
  • Referrals:
    12.81%
  • Search Engines:
    16.21%
  • Direct:
    69.81%
More Data

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Reduce your cloud compute costs by 3-5 times with the best cloud GPU rentals. Vast.ai's simple search interface allows for fair comparison of GPU rentals from all providers.

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Brilliant summaries in a single click. Well written AI summaries in both text and audio. Summarize articles in any language.

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Midjourney SREF Codes Library for Style Reference

What are SREF Codes?

SREF codes are special text strings used in Midjourney to quickly and easily apply specific artistic styles to your image generations. 

How to Use SREF Codes:

Simply add an SREF code to your Midjourney prompt. For example:

`/imagine a majestic dragon, SREF:cyberpunk`

SREF Code Library:

This library contains a collection of SREF codes categorized by style. 

* Art Styles:
    * `photorealistic`: Creates images with high levels of realism.
    * `impressionistic`:  Captures the essence of a scene with loose brushstrokes and vibrant colors.
    * `cubist`:  Breaks down objects into geometric shapes.
    * `surrealist`:  Creates dreamlike and fantastical images.
    * `abstract`:  Focuses on form, color, and composition rather than representational imagery.

* Visual Effects:
    * `cinematic`:  Gives images a cinematic look and feel.
    * `HDR`:  Creates images with high dynamic range, resulting in greater detail and contrast.
    * `neon`:  Adds vibrant neon lights to the image.
    * `pixel art`:  Generates images in a retro pixel art style.

* Other Styles:
    * `anime`:  Creates images in the style of Japanese animation.
    * `manga`:  Similar to anime, but with a more comic book aesthetic.
    * `sketch`:  Generates images that resemble pencil sketches.
    * `watercolor`:  Creates images with the look and feel of watercolor paintings.

Note: This is just a small sample of the many SREF codes available. 

Explore and Experiment:

The best way to learn about SREF codes is to experiment with them! Try combining different codes to create unique and interesting styles.
View Detail

Midjourney SREF Codes Library for Style Reference What are SREF Codes? SREF codes are special text strings used in Midjourney to quickly and easily apply specific artistic styles to your image generations. How to Use SREF Codes: Simply add an SREF code to your Midjourney prompt. For example: `/imagine a majestic dragon, SREF:cyberpunk` SREF Code Library: This library contains a collection of SREF codes categorized by style. * Art Styles: * `photorealistic`: Creates images with high levels of realism. * `impressionistic`: Captures the essence of a scene with loose brushstrokes and vibrant colors. * `cubist`: Breaks down objects into geometric shapes. * `surrealist`: Creates dreamlike and fantastical images. * `abstract`: Focuses on form, color, and composition rather than representational imagery. * Visual Effects: * `cinematic`: Gives images a cinematic look and feel. * `HDR`: Creates images with high dynamic range, resulting in greater detail and contrast. * `neon`: Adds vibrant neon lights to the image. * `pixel art`: Generates images in a retro pixel art style. * Other Styles: * `anime`: Creates images in the style of Japanese animation. * `manga`: Similar to anime, but with a more comic book aesthetic. * `sketch`: Generates images that resemble pencil sketches. * `watercolor`: Creates images with the look and feel of watercolor paintings. Note: This is just a small sample of the many SREF codes available. Explore and Experiment: The best way to learn about SREF codes is to experiment with them! Try combining different codes to create unique and interesting styles.

Midjourney SREF Codes Library for Style Reference What are SREF Codes? SREF codes are special text strings used in Midjourney to quickly and easily apply specific artistic styles to your image generations. How to Use SREF Codes: Simply add an SREF code to your Midjourney prompt. For example: `/imagine a majestic dragon, SREF:cyberpunk` SREF Code Library: This library contains a collection of SREF codes categorized by style. * Art Styles: * `photorealistic`: Creates images with high levels of realism. * `impressionistic`: Captures the essence of a scene with loose brushstrokes and vibrant colors. * `cubist`: Breaks down objects into geometric shapes. * `surrealist`: Creates dreamlike and fantastical images. * `abstract`: Focuses on form, color, and composition rather than representational imagery. * Visual Effects: * `cinematic`: Gives images a cinematic look and feel. * `HDR`: Creates images with high dynamic range, resulting in greater detail and contrast. * `neon`: Adds vibrant neon lights to the image. * `pixel art`: Generates images in a retro pixel art style. * Other Styles: * `anime`: Creates images in the style of Japanese animation. * `manga`: Similar to anime, but with a more comic book aesthetic. * `sketch`: Generates images that resemble pencil sketches. * `watercolor`: Creates images with the look and feel of watercolor paintings. Note: This is just a small sample of the many SREF codes available. Explore and Experiment: The best way to learn about SREF codes is to experiment with them! Try combining different codes to create unique and interesting styles.

Explore a list of 177 unique Midjourney sref codes and style references to find your perfect Midjourney sref code effect.

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Stability Matrix CivitAI Integration 

This document outlines the integration of Stability Matrix into Civitai. 

What is Stability Matrix?

Stability Matrix is a powerful tool for evaluating and comparing different AI models. It provides a comprehensive set of metrics to assess model performance across various tasks, including text generation, image generation, and code generation.

Benefits of Integrating Stability Matrix into Civitai:

* Enhanced Model Discovery: Users can easily identify high-performing models based on their desired tasks and preferences.
* Improved Model Selection: The detailed metrics provided by Stability Matrix allow for informed decision-making when choosing a model for a specific project.
* Community-Driven Evaluation: By leveraging the collective wisdom of the Civitai community, Stability Matrix fosters a transparent and collaborative approach to model evaluation.
* Accelerated Model Development: Developers can use Stability Matrix to track the progress of their models and identify areas for improvement.

Implementation Details:

* Stability Matrix scores will be displayed alongside model descriptions on Civitai.
* Users will be able to filter models based on their Stability Matrix scores.
* The integration will initially focus on text generation and image generation models.

Future Enhancements:

* Expand support for additional model types, such as code generation models.
* Implement interactive visualizations of Stability Matrix data.
* Allow users to contribute their own model evaluations to the platform.
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Stability Matrix CivitAI Integration This document outlines the integration of Stability Matrix into Civitai. What is Stability Matrix? Stability Matrix is a powerful tool for evaluating and comparing different AI models. It provides a comprehensive set of metrics to assess model performance across various tasks, including text generation, image generation, and code generation. Benefits of Integrating Stability Matrix into Civitai: * Enhanced Model Discovery: Users can easily identify high-performing models based on their desired tasks and preferences. * Improved Model Selection: The detailed metrics provided by Stability Matrix allow for informed decision-making when choosing a model for a specific project. * Community-Driven Evaluation: By leveraging the collective wisdom of the Civitai community, Stability Matrix fosters a transparent and collaborative approach to model evaluation. * Accelerated Model Development: Developers can use Stability Matrix to track the progress of their models and identify areas for improvement. Implementation Details: * Stability Matrix scores will be displayed alongside model descriptions on Civitai. * Users will be able to filter models based on their Stability Matrix scores. * The integration will initially focus on text generation and image generation models. Future Enhancements: * Expand support for additional model types, such as code generation models. * Implement interactive visualizations of Stability Matrix data. * Allow users to contribute their own model evaluations to the platform.

Stability Matrix CivitAI Integration This document outlines the integration of Stability Matrix into Civitai. What is Stability Matrix? Stability Matrix is a powerful tool for evaluating and comparing different AI models. It provides a comprehensive set of metrics to assess model performance across various tasks, including text generation, image generation, and code generation. Benefits of Integrating Stability Matrix into Civitai: * Enhanced Model Discovery: Users can easily identify high-performing models based on their desired tasks and preferences. * Improved Model Selection: The detailed metrics provided by Stability Matrix allow for informed decision-making when choosing a model for a specific project. * Community-Driven Evaluation: By leveraging the collective wisdom of the Civitai community, Stability Matrix fosters a transparent and collaborative approach to model evaluation. * Accelerated Model Development: Developers can use Stability Matrix to track the progress of their models and identify areas for improvement. Implementation Details: * Stability Matrix scores will be displayed alongside model descriptions on Civitai. * Users will be able to filter models based on their Stability Matrix scores. * The integration will initially focus on text generation and image generation models. Future Enhancements: * Expand support for additional model types, such as code generation models. * Implement interactive visualizations of Stability Matrix data. * Allow users to contribute their own model evaluations to the platform.

This extension adds a button to the CivitAI interface that allows you to download a given model with Stability Matrix.

193.90 M