How to vibe codeMeetGeek

Prerequisites

• Node.jsfree

  Required runtime for executing the Next.js frontend and background worker code.

• GitHubfree

  Source code repository and CI/CD deployment pipeline.

• 

  OpenAI API Accountpay-as-you-go

  Provides the Whisper STT and LLM endpoints required for transcription and summarization.

AI coding tools

• Claude Code

  Best-in-class terminal coding agent for scaffolding multi-file full-stack features and fixing complex state bugs.

• Cursor

  Ideal AI-native editor for rapidly reviewing UI component diffs and tweaking Tailwind dashboard layouts.

Build guide

1. 01Project Scaffolding & Database Schema

   Initialize the Next.js project with Tailwind CSS, configure better-auth with SQLite via Turso, and define database models for users, meetings, transcripts, and action items.

   Scaffold a new Next.js project using TypeScript, Tailwind CSS, and App Router. Set up better-auth for email/password authentication backed by a Turso SQLite database. Create Prisma or Drizzle schema models for Users, Meetings (id, title, duration, audioUrl, createdAt, status), Transcripts (id, meetingId, speaker, text, timestampStart, timestampEnd), and ActionItems (id, meetingId, text, assignee, completed). Add basic layout structure with a responsive sidebar dashboard layout using shadcn/ui primitives.

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2. 02Audio Upload & Transcription Pipeline

   Implement file upload functionality for audio/video files, storing binaries in Cloudflare R2 and processing them through OpenAI's Whisper API.

   Implement an audio/video upload endpoint in Next.js that accepts files up to 500MB, uploads them directly to Cloudflare R2 object storage, and triggers a background job using Inngest or a server action. The job must send the audio file to the OpenAI Whisper API to retrieve timestamped segments with speaker labels. Save the resulting transcription items into the Transcripts table linked to the meeting record.

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3. 03LLM Summary & Action Item Extraction

   Build the AI summarization engine using the Vercel AI SDK and custom prompt templates to extract meeting summaries and action items.

   Create a meeting processing workflow using the Vercel AI SDK and Anthropic or OpenAI models. When a transcription is finalized, read all transcript segments and run a structured extraction prompt that returns a JSON object containing: an executive summary, key discussion bullet points, and an array of action items with assignees and due contexts. Save these outputs to the meeting record and display them in a polished summary view on the frontend.

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4. 04Conversational AI Meeting Chat Interface

   Build an interactive chat interface that lets users query past meeting transcripts using vector embeddings or contextual LLM retrieval.

   Build an AI chat page where users can query their meeting database. Use the Vercel AI SDK useChat hook to power a conversational interface. When a user asks a question (e.g., 'What did we decide about pricing?'), fetch relevant transcript snippets from Turso using full-text search or vector embeddings, inject them as context into the system prompt, and stream the cited response back to the user interface in real time.

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5. 05Analytics Dashboard & Post-Meeting Workflows

   Implement conversation analytics (talk-to-listen ratios) and webhook triggers to push action items into external tools like Slack or Notion.

   Build a conversation analytics dashboard calculating talk-to-listen ratios, word counts, and keyword frequencies per speaker from the transcript table. Add a workflow configuration screen allowing users to toggle automated triggers when a meeting finishes—such as formatting a Markdown summary and posting it to a designated Slack webhook URL or Notion database via REST API.

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Cost vs paying for MeetGeek