Skill UI

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.

This comprehensive checklist guides the secure and robust deployment of AssemblyAI transcription services to a live production environment. It covers critical pre-deployment checks, health monitoring implementations, webhook-based processing setup, and gradual rollout strategies, ensuring high availability and reliability for mission-critical speech-to-text functions.

This skill provides comprehensive solutions for managing rate limits and throttling when interacting with the AssemblyAI Speech-to-Text API. It implements advanced patterns like exponential backoff with jitter, queue-based concurrency control, and resilient streaming retry logic. Use it when building high-throughput, reliable applications that process large volumes of audio data.

This guide provides production-ready architectural patterns for integrating the AssemblyAI SDK in TypeScript and Python. It details best practices including implementing Singleton clients, robust service wrappers, advanced error handling (like exponential backoff retries), and multi-tenant client management. Use this when building highly scalable and reliable speech-to-text applications.

Provides comprehensive security best practices for integrating AssemblyAI into production applications. This guide covers secure API key management (using environment variables), generating temporary tokens for browser streams, implementing PII redaction, managing data retention, and enabling content safety checks for compliance and security.

A comprehensive guide for developers on upgrading the assemblyai npm package and migrating between major SDK versions (e.g., v3 to v4). This resource details handling breaking changes, updating API calls, and transitioning between different transcription methods, including synchronous, asynchronous, and streaming setups, ensuring smooth integration with the latest features and models.