Can I vibecode Grain?

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Build a deliberately narrow personal substitute for Grain, focusing heavily on generating shareable video clips and actionable meeting intelligence.
Use exactly this stack: Python 3.12 + FastAPI + whisper.cpp + SQLite, with a React frontend leveraging Tailwind CSS.
Primary job: Build a private meeting clips + intelligence pipeline. Accept a video/audio file (MP4, WAV, MP3), transcribe it locally with speaker identification, extract structured notes (decisions, action items, owners, due dates), and allow the user to create and export short highlight clips.
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. Use an LLM API (key in .env) for the intelligence layer to filter out filler language and tangents.
Implement a "Clipping Tool" in the UI: users can highlight text in the transcript, and the app automatically trims the corresponding video/audio segment to create a "shareable moment."
Build a "Shared Reality" dashboard where users can view these highlight clips alongside the structured meeting outcomes.
Include clear empty, loading, validation, success, and failure states. Add robust import and export capabilities (exporting clips as MP4s and notes as Markdown).
Use accessible keyboard navigation, labels, focus states, and sensible contrast. Validate untrusted input and never log secrets.
Deliberately exclude these paid-product advantages: live multi-speaker accuracy tracking, enterprise CRM integrations (Salesforce/HubSpot), team workspaces, and cross-call analytics.
Do not fake integrations, network effects, proprietary data, model quality, compliance, or security claims.
Where an external API is optional, keep the app useful without it and explain the degraded mode.
Write focused unit tests for the data model, transcript parsing, and video clipping logic (mocking ffmpeg). Add one end-to-end smoke test.
Create a comprehensive 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.