Skill UI

This skill provides a production-grade, containerized integration service for connecting to the Together AI platform. It facilitates high-performance inference, fine-tuning, and model deployment across over 100 open-source models using Together's OpenAI-compatible API. Includes comprehensive Docker setup, environment variable management, health checks, and zero-downtime rolling update strategies for robust, real-time LLM serving.

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.

This guide provides comprehensive instructions for setting up and authenticating the Together AI SDK. It covers installation using both Python and Node.js, configuring the necessary API key, and verifying connectivity to utilize the OpenAI-compatible API for powerful open-source model inference and fine-tuning tasks. Ideal for developers starting with Together AI.

Provides comprehensive guidance for performance tuning, inference, and fine-tuning using the Together AI platform. This skill supports working with Together AI's OpenAI-compatible API, covering model deployment, utilizing various open-source models (like Llama, Mixtral), and implementing best practices for efficient and cost-effective batch inference.

A comprehensive reference architecture for building scalable, cost-effective, and robust AI services. This system manages the full AI lifecycle, including intelligent model routing (for quality/cost trade-offs), response caching (using Redis), asynchronous batch processing, and fine-tuning pipeline management via Together AI's OpenAI-compatible API. Ideal for enterprise applications requiring high availability across multiple open-source models.

This skill provides a powerful mechanism for modifying and extending AWS infrastructure using Pulumi handlers and dedicated declarative components. It allows developers to manage complex cloud setups, including configuring OpenSearch, VPC networking, resource tagging, custom domains, blue-green deployment strategies, and implementing environment-conditional configurations (e.g., production vs. development).

KubeStellar Console is an open-source, multi-cluster Kubernetes dashboard (CNCF project) that integrates AI-powered operations. It utilizes the kc-agent and MCP server to provide advanced tools for managing Kubernetes environments across edge and cloud. Users can perform AI-assisted troubleshooting, detailed performance testing, root cause analysis (RCA), and monitor over 20 CNCF projects.