sharanbabu

* This val creates a text editing tool using the Cerebras Llama 70B model. * It includes a command input field with speech-to-text functionality, a collapsible additional context area, * and a rich text editor. The user's command, additional context, and current text are sent to the Cerebras API, * which returns the modified text to be displayed in the editor. * We use React for the UI, the Web Speech API for speech recognition, and the Cerebras API for text processing.

* This val creates a modern, stylish knowledge explorer using the Cerebras LLM API. * It allows users to enter a topic or select from suggestions, displays information in a centered card, * and enables exploration of related topics or deeper dives using arrow keys or buttons.

* This val creates a text summarization comparison tool using the Cerebras LLM API. * It provides a text area with default text, a summarize button, and displays two different summarization results: * 1. Direct summarization * 2. Extractive summarization followed by cohesive rewriting * * The server handles API calls to Cerebras, while the client manages the UI and user interactions.

* This code creates a search engine prototype with autocomplete functionality using the Cerebras LLM API. * It uses React for the frontend and the Cerebras API for generating autocomplete suggestions. * The suggestions are cached in the browser to reduce API calls. * It implements a two-step LLM process: first to get initial suggestions, then to filter them for sensibility and ethics. * If the second LLM call fails, it displays "Failed to fetch" instead of showing results.

// This chatbot app will use a simple React frontend to display messages and allow user input.