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This debug bundle provides comprehensive guidance for integrating the Wispr Flow voice-to-text API. It covers best practices for various integration methods, such as low-latency WebSocket streaming and standard REST API calls. It is essential for developers building sophisticated voice-powered applications, dictation tools, or any system requiring robust speech recognition and error handling.

This technical guide provides comprehensive integration details for the Wispr Flow voice-to-text API. It equips developers with the knowledge to build sophisticated, voice-powered applications. Learn how to utilize WebSocket streaming for low latency or REST endpoints for simple transcription, ensuring accurate context awareness for code and development jargon across multiple platforms.

This guide provides comprehensive instructions for integrating the Wispr Flow API for advanced voice-to-text transcription. It details both WebSocket streaming (recommended for low latency) and REST API endpoints, covering authentication setup, API key configuration, and accessing short-lived client access tokens. Ideal for building real-time, voice-powered applications, such as code editors or dictation tools.

This guide provides detailed instructions for setting up a local development loop using the Wispr Flow voice-to-text API. It covers connecting via WebSocket (recommended for low latency) or REST API, handling authentication, and optimizing audio input. Ideal for building voice-powered applications, dictation tools, or integrating sophisticated voice command features into existing SaaS products.

This comprehensive guide provides developers with best practices and detailed instructions for integrating the Wispr Flow voice-to-text API. It covers critical topics such as managing rate limits, choosing between WebSocket streaming and REST endpoints, handling common error codes (e.g., 401 Unauthorized), and optimizing context awareness, particularly for programming code and developer jargon. Essential for building reliable, low-latency voice-powered applications.

This guide provides the reference architecture and best practices for integrating Wispr Flow's voice-to-text API. It details how to utilize both WebSocket (for low-latency streaming) and REST APIs, covering essential concepts like audio format requirements, context awareness (especially for programming code), and comprehensive error handling strategies.

This pattern provides comprehensive guidance for integrating the Wispr Flow voice-to-text API. It covers best practices for both low-latency WebSocket streaming and simpler REST API usage. Key topics include authentication, recommended audio formats, robust error handling, and context awareness (e.g., understanding code or CLI commands), making it ideal for building advanced voice-powered applications.

A comprehensive guide for migrating and integrating with the Wispr Flow voice-to-text API. This skill details best practices for both WebSocket (low-latency streaming) and REST API methods. It covers authentication, optimal audio formatting (16kHz mono PCM), context awareness for specialized jargon (e.g., code), and robust error handling across multiple platforms (Mac, iOS, browser).

A comprehensive guide for integrating the Wispr Flow voice-to-text API using webhooks. This skill covers best practices for real-time speech recognition, including low-latency WebSocket streaming and robust REST API calls. It is essential for developers building voice-powered applications, dictation tools, or any system requiring advanced audio processing and context-aware transcription.

This skill provides high-performance, real-time object detection utilizing the Google Coral Edge TPU accelerator. It processes live camera frames to identify up to 80 COCO classes (e.g., person, car, dog). Optimized for native macOS and Linux deployment, it achieves extremely low latency (e.g., ~4ms at 320x320), making it ideal for embedded vision systems, surveillance, and low-latency monitoring applications.

This skill provides real-time object detection capabilities by leveraging the Google Coral Edge TPU accelerator. It operates natively within Windows Subsystem for Linux (WSL), allowing high-speed, low-latency inference on live camera frames. By utilizing hardware acceleration, it achieves impressive performance (e.g., ~4ms inference at 320x320), making it ideal for edge computing projects requiring rapid visual analysis and resource efficiency.

Use this skill to deeply inspect and audit the entire process of local AI coding-agent sessions. It tracks critical operational metrics such as token consumption, cost spikes, tool failures, latency anomalies, and overall health scores. Ideal for debugging unreliable code runs, establishing CI/CD health gates, or comparing different attempts to ensure semantic accuracy.