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RealtimeAssistant

A production-grade live assistant for technical interviews, meetings, and screen-aware workflows. It listens, transcribes, streams answers, captures context, and can run fully local with Qwen and Whisper.cpp when privacy matters.

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Local + Cloud
Qwen, Whisper.cpp, Apple Speech, GPT Realtime-2
Live Context
Mic, system audio, screenshots, recordings
Practice Mode
Karaoke readback with highlighted responses
Project: React Native interview | Session: Interview
LiveDisplay 1ScreenshotRecordMicSystem

Please explain Angular lifecycle hooks.

Whisper.cpp input - transcription 1.0s

Angular lifecycle hooks are methods called at key points in a component lifecycle: initialize, check, project content, render view, and clean up.

Local Qwen - first token 1.4s - 19.4 tok/s

Angular lifecycle hooks help you explain setup, change detection, view updates, and cleanup.

Karaoke readback - Apple Speech
RealtimeAssistant by Wonek app iconRealtime stackQwen GGUFWhisper.cppGPT Realtime-2
01

Live interview answers

Capture the question from microphone or system audio, then stream a concise answer from GPT Realtime-2 or local Qwen.

02

Local-first transcription

Use Whisper.cpp, Whisper CLI, Apple Speech, or Qwen ASR depending on whether speed, privacy, or accuracy matters most.

03

Screen-aware context

Attach screenshots, detected moments, and visible app context so answers can react to the actual work in front of you.

04

Karaoke readback

Read the latest model response aloud while the app highlights your progress and tracks improvised wording.

Model routing

Choose the right engine for the moment

RealtimeAssistant is built for the messy reality of live speech: short questions, long answers, missed edge words, code terms, accents, background noise, and privacy-sensitive interviews.

Local Qwen GGUF answers with token streaming

Whisper.cpp ASR with VAD chunking and Apple Speech draft support

GPT Realtime-2 path for low-latency cloud sessions

Credit-aware usage tracking for paid production accounts

Workflow

From speech to useful answer without losing context

The app separates capture, ASR, answer generation, and readback so every session can be tuned for latency, accuracy, cost, or local privacy.

  1. 1

    Listen to microphone, system audio, or both selected channels

  2. 2

    Transcribe with the selected ASR path and preserve partial progress

  3. 3

    Combine ASR candidates when Whisper or Apple Speech misses an edge word

  4. 4

    Stream the answer into the session timeline with timing and model labels

Production controls

Built for real usage, not a demo script

Local models are excluded from cloud usage billing, GPT Realtime sessions can be measured with credits, and model labels plus timing metadata make every response traceable.

Private local mode

Run interviews with local Qwen and Whisper.cpp

Use local ASR and local answers when you need the Mac to handle sensitive prompts without sending the session to cloud models.

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Workflow Guides

Playbooks for live transcription, local Qwen & interview prep

Configure system audio capture, compare Whisper.cpp with Apple Speech, size local GGUF models, and practice spoken delivery.

Browse all 9 guides →
RealtimeAssistant by Wonek

Turn live questions into calm, useful answers

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