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Using OpenAI's GPT to determine if YouTube videos have been watched or ignored, based on your instructions.

Chat with Web-LLM Models in the Browser
You can interact with Web-LLM models directly in your web browser without requiring any additional setup or installation. This allows you to easily experiment with and explore the capabilities of these models.
Getting Started
To get started, simply open a web browser and navigate to the Web-LLM model's website or platform. Once you're on the website, you can usually find a chat interface or text input field where you can enter your prompts or questions.
How it Works
When you enter a prompt or question, the Web-LLM model processes your input and generates a response. This response is then displayed in the chat interface or output field, allowing you to read and interact with the model's output.
Benefits
The benefits of chatting with Web-LLM models in the browser include:
* Convenience: No need to install any software or set up any development environments.
* Accessibility: Anyone with a web browser can interact with the model, regardless of their technical expertise.
* Ease of use: The chat interface provides a user-friendly way to interact with the model, making it easy to experiment and explore its capabilities.
Use Cases
Some potential use cases for chatting with Web-LLM models in the browser include:
* Research and development: Quickly experiment with different models and prompts to explore their capabilities and limitations.
* Education and learning: Use the chat interface to teach students about AI and language models, or to provide interactive learning experiences.
* Creative writing and ideation: Use the model as a tool to generate ideas, write stories, or create poetry.
Overall, chatting with Web-LLM models in the browser provides a convenient and accessible way to interact with these powerful language models, and can be a valuable tool for a wide range of applications.

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Cykel AI Browser Extension

Exthalpy is a serverless retrieval platform. It powers the future of AI by building retrieval-first AI applications and models. These applications and models are less dependent on computation-heavy training processes.