Can I vibecode MeetRecord?

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Build the closest honest consolation tool inspired by MeetRecord (Outdoo), focusing on a personal revenue intelligence and sales coaching system; do not claim to replace its structural enterprise moat.
Use exactly this stack: Python 3.12 + FastAPI + whisper.cpp + SQLite, with a React/Tailwind frontend.
Primary job: Build a private sales meeting pipeline that combines "Deal Intelligence" (transcribing audio, extracting customer pain points/objections/next steps) with an "AI Sales Roleplay" module for preparation.
Start from an empty folder and create the complete working project. Make the default mode single-user and private.
Store user data locally in SQLite. Do not add analytics, telemetry, ads, or third-party accounts.
Put every secret and external credential in .env and provide .env.example. Provide an LLM integration for the roleplay and summarization features.
Build a "Performance Dashboard" that automatically scores ingested meeting transcripts based on predefined top-performer behaviors (e.g., talk/listen ratio, objection handling, asking discovery questions).
Build an "AI Sales Roleplay" module: allow the user to define an "AI Buyer Persona" (pain points, objections), and have an interactive text-based or audio chat to practice pitches before a call.
Implement the smallest polished interface that completes the core loop end to end: import audio, view transcript, view call score/coaching feedback, and run a prep roleplay.
Include clear empty, loading, validation, success, and failure states. Add Markdown export so the user is not trapped in the app.
Use accessible keyboard navigation, labels, focus states, and sensible contrast. Validate untrusted input and never log secrets.
Deliberately exclude these paid-product advantages: live meeting-bot auto-join, complex enterprise LMS (Learning Management System) integrations, real-time cross-call team analytics, and live CRM bidirectional syncing.
Do not fake integrations, network effects, proprietary data, model quality, compliance, or security claims.
Where an external API (like an LLM for the roleplay bot) is optional, keep the app useful without it and explain the degraded mode.
Write focused unit tests for the transcript scoring logic, data model, and roleplay persona generator. Add one end-to-end smoke test.
Create a README with setup, permissions, architecture, data location, backup, and limitations.
Add scripts for install, development, test, build, and a production-style local run.
Run the tests and build before finishing, then fix errors rather than merely describing them.