Skill UI

This comprehensive guide details the necessary steps and breaking changes when migrating from Cohere API v1 to v2. It covers critical updates for SDKs (TypeScript/Python), including changes in chat message formats, role naming conventions, and required parameters for key endpoints like Embed, Rerank, and Tool Use, ensuring smooth API adoption.

This comprehensive guide demonstrates how to implement robust, real-time streaming UIs using Cohere's `chatStream` method. It details handling various event types, including content deltas, citation information for RAG, and structured tool call streams. A practical example is provided for creating a Server-Sent Events (SSE) endpoint, making it ideal for front-end integration with advanced AI features.

This guide provides step-by-step instructions to configure access to the CoreWeave Kubernetes Service (CKS). It covers downloading the kubeconfig file, setting API tokens, and verifying GPU node access using standard `kubectl` commands. This is essential for deploying AI/ML workloads on bare-metal GPU infrastructure.

This comprehensive guide teaches you how to integrate and use the ElevenLabs Text-to-Speech (TTS) API. It provides multiple code examples (TypeScript, Python, cURL) for generating high-quality speech audio from text. Learn advanced features such as detailed voice parameter control, choosing optimal models (e.g., streaming vs. standard), and implementing low-latency streaming for real-time applications.

This comprehensive guide details how to install and properly configure the ElevenLabs SDK for both Node.js and Python environments. It covers setting up the required API keys using environment variables, initializing the client connection, and verifying the setup by listing available voices. Use this skill when starting a new project that requires advanced text-to-speech (TTS) or voice synthesis capabilities.

This guide addresses critical LangGraph streaming pitfalls, including state overdraw, proxy buffering issues (Nginx, Cloud Run), and excessive event noise. It provides a production-grade pattern for implementing robust SSE/WebSocket endpoints using FastAPI. Learn how to correctly select the `stream_mode` (messages, updates, values) and implement server-side event filtering to reliably build live-token chat UIs and perform comprehensive state visualization.

A comprehensive guide and implementation pattern for handling common errors when working with the Runway AI platform. This skill provides Python SDK examples for tasks like image-to-video generation, along with structured solutions for API issues such as 'Unauthorized' (401) or 'Insufficient credits' (402), ensuring smooth and reliable creative AI workflows.

This guide provides detailed, step-by-step instructions for installing and authenticating the Runway ML SDK. It covers prerequisites for both Python and Node.js environments, including setting up API keys and verifying the connection. This setup is essential for developers who plan to perform high-fidelity AI video generation, image editing, or advanced creative content workflows using the Runway platform.

This skill provides the implementation pattern and comprehensive guide for integrating the Runway ML API. It demonstrates using the Python SDK to perform advanced creative tasks, such as image-to-video generation. Furthermore, it includes crucial error handling instructions for managing common issues like API rate limits, unauthorized access (401), and credit exhaustion.

This workflow guides users through the complete lifecycle of customizing large language models (LLMs). It covers uploading training data, initiating fine-tuning jobs, monitoring training progress, managing model versions, and finally deploying the optimized model to a dedicated, autoscaling inference endpoint using the Together AI API. Ideal for production-level customization.

A comprehensive guide to optimizing operating costs when utilizing Together AI's OpenAI-compatible API. This resource details strategies for cost reduction across various AI tasks, including inference, fine-tuning, and model deployment. Learn how to select appropriate model sizes, implement caching, leverage batch inference, and utilize Turbo variants to maximize efficiency and minimize spending.

This guide provides a comprehensive tutorial on running various AI inference tasks using the Together AI API, which is compatible with the OpenAI standard. It demonstrates key functionalities including chat completions, streaming responses, image generation, and creating embeddings. This is ideal for developers who need to test open-source models, compare performance across different LLMs (like Llama or Mixtral), or integrate powerful generative AI features into their applications using Python or Node.js.