Llm Supervisor V1 Progress

💡In Progress

Setup

Input

• Store transcript with timestamp in CSV/JSON

Logic + Processing

• Add recent messages as context (simple memory)
• Implement logic for LLM to “respond to a conversation”
• Try out a second persona for contrast

UI

• - Build a simple UI (Streamlit, notebook, or CLI)

  • Display conversation log

  • Add new input field (or simulate with logs)

• Display conversation log

• Add new input field (or simulate with logs)

Presentation and Reflection

• - Write a reflective note (What worked? What next?)
• - Share with your supervisors

☑️ Done

• - Record a short video of the system in action
• Do some research into how to use Whisper for realtime transcription
• Create a basic prompt template with role/context
• Write a 3-sentence scope for your prototype
• Install Ollama or LM Studio locally
• Run a test chat with a small model (e.g., Mistral or Phi)
• Install Whisper or whisper.cpp
• Choose your first LLM persona and write its prompt
• Decide on text or voice input (or both)
• Write a script to transcribe and save input

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