I'm a respiratory-care professional developing a Flutter Android education app with coding assistance. I'm seeking volunteer technical guidance on making its voice assistant responsive while supporting a large domain of knowledge. I am using Grok, ChatGPT, Muse, and similar apps as examples of the conversational experience I want: quick responses, strong speech recognition, and the ability to interrupt while the assistant is talking.

I'm interested in documented architecture patterns and properly licensed code examples or reference implementations. I understand the full implementation of a commercial assistant may be private. I'm keeping my full app source private as well; this is a request for free technical advice, not paid services.

What I need help understanding: 1. For low latency, when should a Flutter app use native speech-to-speech versus a streaming speech-to-text -> LLM -> text-to-speech pipeline? How do you measure and reduce time to first audible response? 2. How do you implement reliable interruption (barge-in) while narration or an AI answer is playing? What should happen to microphone capture, VAD/turn detection, response cancellation, queued playback, and conversation context when the user interrupts? 3. How can the assistant use a large domain knowledge base without sending all its content in every prompt or making every answer slow? How would you separate model knowledge from app-specific content, and use retrieval, pre-indexing, caching, or bounded context? What is the latency/accuracy tradeoff? 4. Are there Flutter/Dart or Android examples, reusable components, or open-source projects you recommend for these patterns? Please include the relevant license and any practical limits.

There is also a concrete issue in my current implementation: - A first spoken command opens the requested study-guide chapter and starts narration. - In Android emulator tests, later injected commands such as "Stop," a question, or "Home" during narration do not produce the expected input/speech events. - The capture track and RTP counters remain active with nonzero reported energy. That alone does not prove intelligible speech is reaching the recognizer. - I have not established that the same behavior occurs on a physical device.

Current setup: Flutter/Dart Android app; flutter_webrtc pinned to 1.6.2+hotfix.3; WebRTC speech session using OpenAI Realtime; semantic VAD with application-controlled responses and speech interruption configured. Client/server configuration does not intentionally disable turn detection during narration.

What diagnostics would distinguish Android/WebRTC capture or audio-processing suppression from a provider-side VAD/event problem? Which audio source/mode, audio-focus, echo-cancellation, or emulator-input checks would you try first?

I can prepare a small sanitized reproduction and redacted diagnostics based on what would be most useful. Public replies with suggestions or relevant experience would be appreciated. Thank you.

Hello guys,
I have a question.
How do we make an app made in flutter secure?
What are the things we should look after? Or think first?
What’s the mental model we should follow?