| name | twinmind-performance-tuning |
| description | Optimize TwinMind transcription accuracy and speed with Ear-3 model configuration, audio quality tuning, and caching strategies.
Use when implementing performance tuning,
or managing TwinMind meeting AI operations.
Trigger with phrases like "twinmind performance tuning", "twinmind performance tuning".
|
| allowed-tools | Read, Write, Edit, Bash(npm:*), Bash(curl:*), Grep |
| version | 1.13.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| tags | ["saas","twinmind","performance"] |
| compatibility | Designed for Claude Code, also compatible with Codex and OpenClaw |
TwinMind Performance Tuning
Overview
Optimize TwinMind transcription accuracy and speed with Ear-3 model configuration, audio quality tuning, and caching strategies. TwinMind uses the Ear-3 speech model (5.26% WER, 3.8% DER) for transcription, with GPT-4, Claude, and Gemini for AI summarization.
Prerequisites
- TwinMind account (Free, Pro $10/mo, or Enterprise)
- Chrome extension installed and authenticated
- Understanding of TwinMind workflow
Instructions
Step 1: Setup
TwinMind operates as a Chrome extension and mobile app with optional API access for Pro/Enterprise users.
const config = {
apiKey: process.env.TWINMIND_API_KEY,
model: "ear-3",
aiModels: ["gpt-4", "claude", "gemini"],
};
Step 2: Implementation
const twinmind = {
transcriptionModel: "ear-3",
languages: ["en", "es", "ko", "ja", "fr"],
features: ["transcription", "summary", "action-items"],
privacyMode: "on-device",
};
async function verify() {
const health = ();
.(, health.());
}