Skill UI

This technical guide provides patterns for handling rate limits, throttling, and concurrency issues when scraping data using Bright Data services. It demonstrates implementing concurrency control using queuing, applying exponential backoff for transient errors (like 429 or 503), and managing specific API trigger rate limits (e.g., 20/min, 60/hr). Essential for building robust, large-scale web scrapers.

This guide provides production-ready patterns for integrating with Bright Data proxy services. It covers critical aspects like setting up a proxy client, implementing robust retry logic for transient errors (e.g., 502, 503), managing sticky sessions for consistent IPs, and parsing complex HTML responses. Essential for building reliable, high-scale web scrapers using TypeScript and Python.

This comprehensive guide provides developers with step-by-step instructions for migrating between different Bright Data services and API versions. It covers complex transitions, such as upgrading from raw proxies to Web Unlocker, moving from Puppeteer to Scraping Browser, and updating datasets API endpoints (v2 to v3). Includes prerequisites, code examples, and rollback procedures for seamless integration.

Provides comprehensive strategies for handling external API rate limits (HTTP 429 errors). This skill implements advanced patterns including exponential backoff with jitter, request queuing, and header monitoring. It is crucial for building reliable, high-throughput integrations with rate-limited services like ClickUp.

This comprehensive workflow guides the implementation of a complete Retrieval-Augmented Generation (RAG) pipeline using Cohere's core services. It demonstrates the full process: embedding documents, searching for relevant context, reranking results for precision, and finally generating a grounded answer with precise citations using the Chat API. Ideal for building knowledge-intensive, enterprise-grade Q&A systems.

This comprehensive guide provides a deep dive into migrating AI applications from major LLM providers (like OpenAI or Anthropic) to the Cohere platform. It covers critical technical steps, including API call adaptation, re-vectorizing embeddings, migrating tool definitions, and implementing the Adapter Pattern for seamless, gradual re-platforming of existing AI services.

This guide details advanced patterns for managing API rate limits, particularly for external AI services like Cohere. It covers implementing exponential backoff with jitter for handling transient 429 errors, utilizing concurrency-limited request queues, and building proactive rate limit trackers. These techniques ensure highly reliable and optimized API throughput in production systems.

This guide details the process of deploying production-grade inference services on CoreWeave using KServe. It covers setting up autoscaling, GPU scheduling, and implementing scale-to-zero functionality for efficient ML model serving endpoints. Ideal for MLOps engineers deploying large language models (LLMs) or other complex AI models in a production environment.

This skill provides comprehensive guidance for deploying and managing GPU-accelerated AI inference services on CoreWeave Kubernetes (CKS). It covers best practices including containerization using NVIDIA CUDA base images, configuring specific GPU resource limits (A100/H100), setting up robust health checks, and executing controlled rolling updates. Ideal for managing multi-model inference and scaling demanding AI workloads in a cloud environment.

This guide demonstrates how to deploy GPU workloads on the CoreWeave platform using Kubernetes (kubectl). It covers deploying vLLM inference services for large language model (LLM) testing and running batch CUDA jobs for benchmarking. This is ideal for users running their first GPU workloads, testing inference pipelines, or verifying cluster access on bare-metal GPU nodes (A100, H100).

This guide provides a deep dive into migrating machine learning workloads, including inference services and training pipelines, from major hyperscalers (AWS, GCP, Azure) to the CoreWeave GPU cloud. It covers cost comparisons, containerization, Kubernetes YAML adaptation, and a phased deployment strategy, helping users ensure a smooth, cost-effective, and optimized transition.

A comprehensive checklist for ensuring GPU workloads, such as inference services or model training pipelines, are fully prepared for production deployment on CoreWeave. It covers critical MLOps and DevOps aspects including autoscaling, resource limits, security policies, persistent storage validation, monitoring setup, and rollback procedures.