Skill UI

This guide provides techniques to significantly boost performance when integrating with the Hex API. It covers essential optimization strategies such as implementing LRU caching for project lists, running multiple independent jobs in parallel, and adopting adaptive polling intervals to manage API latency and resource usage effectively. Use this when facing slow data retrieval or requiring high-throughput data processing.

This comprehensive guide provides advanced strategies for optimizing interactions with the Klaviyo API. It covers implementing response caching (LRU Cache), efficient cursor-based pagination, request batching using DataLoader, and controlling API concurrency. Use this when dealing with slow API responses, large datasets, or high-throughput integration requirements.

This comprehensive guide presents three validated architectural blueprints for integrating Adobe services (Firefly, PDF, etc.). It helps developers decide between simple direct SDK integration (suitable for MVPs), native Adobe App Builder (serverless functions), or robust, high-throughput dedicated microservices, based on team size, complexity, and required scalability.

A comprehensive guide and implementation pattern for handling rate limits and quotas across various Adobe APIs (Firefly, PDF Services, Photoshop, etc.). This includes implementing exponential backoff, honoring `Retry-After` headers, and proactive quota tracking to ensure stable, high-throughput batch operations and reliable integration.

A comprehensive, production-ready reference architecture for building robust, scalable transcription services using AssemblyAI. This guide demonstrates best practices for layered system design, covering API endpoints, service logic, client management, and infrastructure integration (webhooks, database). It is ideal for designing new high-throughput speech-to-text applications.

This comprehensive guide outlines advanced strategies for optimizing interactions with the Attio REST API. Techniques covered include implementing LRU caching for schemas, using batch queries to avoid N+1 problems, efficient pagination (including streaming), and optimizing connection pooling via keep-alive. It helps developers maximize API throughput, minimize latency, and reliably process large volumes of data.

Provides structured methods and code examples to optimize Abridge's clinical AI integration for high-volume, enterprise deployments. Focuses on reducing critical latencies across the entire workflow, including audio streaming throughput, note generation pipelines, and secure FHIR push operations, ensuring scalability for large health systems.

This guide demonstrates best practices for implementing robust API rate limiting, backoff strategies, and session throttling patterns. It provides solutions for handling common API quota issues, such as 429 errors, and managing high volumes of concurrent encounters. It is essential for building stable, high-throughput systems in SaaS and healthcare environments.

Implements robust rate limiting, backoff logic, and quota management for the Anthropic Claude API. It handles 429 errors and optimizes request throughput by managing Requests Per Minute (RPM), Input Tokens Per Minute (ITPM), and Output Tokens Per Minute (OTPM). This skill provides advanced strategies including SDK retries, custom header awareness, queue-based throttling, and batch processing for stable, high-throughput production applications.

A comprehensive guide on managing rate limits when interacting with the Apify Platform API. It details best practices for building robust, high-throughput automation workflows, covering built-in retries, request queuing using p-queue, batching large payloads, and staggering concurrent operations to prevent 429 errors and maintain system stability.

This guide documents the practical throughput limits and throttling patterns for automating Apple Notes operations (Create, Read, Search, Delete) via scripts like osascript. It provides technical solutions, including TypeScript throttling queues and Bash batch processing examples, to prevent common timeouts (-1712), sync lag, or data loss when performing high-volume, reliable automation on macOS.

This guide provides advanced techniques to optimize AssemblyAI's transcription performance for large-scale applications. Learn how to maximize throughput and minimize latency by implementing caching strategies (Redis/LRU), utilizing webhooks instead of polling, selecting the optimal speech model based on needs, and parallelizing batch processing workloads.