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A comprehensive guide detailing common pitfalls and anti-patterns encountered when integrating with the Anthropic Claude API. It highlights crucial differences from other models (e.g., OpenAI), such as the mandatory nature of `max_tokens`, correct handling of system prompts, and precise response parsing techniques. Essential for developers debugging subtle API integration issues and ensuring best practices.

This guide teaches developers how to implement robust, event-driven architecture patterns with the Anthropic Claude API. It covers two key methods: Server-Sent Events (SSE) for real-time streaming responses, and the Message Batches API for high-volume, asynchronous bulk processing. Use this when building real-time chat features or processing large datasets offline.

This guide provides comprehensive strategies for optimizing API usage and managing costs associated with the Attio platform. Learn how to audit current usage patterns, implement smart caching tiers (schema, records, queries), and replace inefficient polling mechanisms with webhooks to significantly reduce request volume and stay within budget limits.

This guide provides a set of production-grade TypeScript patterns for robust interaction with the Attio REST API. It covers essential best practices including typed clients, automatic retry mechanisms with exponential backoff, cursor-based pagination iterators, and multi-tenant factory implementation, ensuring reliable and scalable API integration. Use these patterns to handle common API challenges like rate limiting and large datasets efficiently.

This skill provides comprehensive strategies for optimizing API usage with BambooHR. It helps developers reduce unnecessary API calls, preventing rate limiting errors (503s) and ensuring stable system synchronization. Learn critical best practices such as replacing inefficient polling with webhooks, implementing robust data caching, and precisely requesting only the necessary fields.

A comprehensive guide and utility for managing API interactions with BambooHR. It implements robust retry logic (with exponential backoff and jitter) to handle 503 and 429 rate limit errors. It also provides patterns for proactive optimization, such as queue-based throttling, batching data using custom reports, and utilizing incremental sync to maximize API throughput and reliability.

A comprehensive, production-grade reference architecture for integrating with BambooHR HR data. It covers core patterns like real-time employee synchronization, scheduled data pipelines, and event-driven webhook handling. Use this when designing complex HR systems, building robust integrations, or establishing architectural standards for BambooHR-powered applications.

This comprehensive guide details the process of managing BambooHR API changes, addressing breaking changes, and migrating from legacy patterns. It covers critical tasks such as converting XML to JSON, updating deprecated field usages (e.g., 'bestEmail' to 'workEmail'), and implementing robust testing and rollback strategies for seamless integration.

A comprehensive guide detailing three validated architectural patterns (Monolith, Service Layer, Microservice) for integrating with the Canva Connect API. This blueprint helps developers choose the optimal structure—from simple MVPs to complex enterprise systems—by addressing concerns like token management, asynchronous exports, rate limiting, and scaling strategies.

A comprehensive resource detailing common anti-patterns and critical mistakes when integrating with the Canva Connect API. This guide covers essential best practices, including robust token management, handling rate limits, implementing asynchronous workflows, and securing webhooks. Ideal for developers conducting code reviews, onboarding new team members, or auditing existing Canva-related integrations.

Automate the deployment of CAST AI across various multi-cloud Kubernetes clusters, including EKS, GKE, and AKS. This module uses Terraform Infrastructure as Code (IaC) patterns to reliably configure necessary components like IAM roles, node autoscaling policies, and cluster resources, ensuring standardized onboarding across different cloud environments.

A comprehensive guide for debugging complex Claude integration issues beyond basic error handling. It covers advanced patterns such as managing inconsistent outputs (temperature control), validating tool use calls, recovering from streaming connection drops, handling max_tokens truncation, and correctly processing image/vision inputs. Essential for building robust, production-grade AI applications with Anthropic's API.