How to vibe codeCircleback

Prerequisites

• Node.jsfree

  Runtime environment for the Next.js web application and build tools.

• GitHubfree

  Source code control and deployment pipeline integration.

• 

  OpenAI AccountPay-as-you-go (~$0.006/min)

  Required for API access to Whisper for speech-to-text transcription.

AI coding tools

• Claude Code

  Agentic coding tool to scaffold the full-stack Next.js application, database schema, and LLM processing pipelines.

• Cursor

  AI code editor for iterative UI design of the meeting dashboard and transcript viewer.

Build guide

1. 01Scaffold Next.js Dashboard and Database Schema

   Initialize the Next.js application with Tailwind CSS and configure Turso with better-auth for personal single-user authentication. Create database tables for meetings, transcripts, summaries, and action items.

   Create a new Next.js project with TypeScript and Tailwind CSS. Configure better-auth with Turso as the database driver using SQLite syntax. Set up database migrations for a 'meetings' table (id, title, audio_url, created_at, duration), a 'transcripts' table (id, meeting_id, speaker, text, timestamp), a 'summaries' table (id, meeting_id, content), and an 'action_items' table (id, meeting_id, task, assignee, status). Build a clean, modern dark-mode dashboard UI inspired by professional productivity apps with a sidebar for navigation and a main content area.

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2. 02Build Audio Upload and Whisper Transcription Pipeline

   Implement an audio file upload mechanism in the web app and integrate the OpenAI Whisper API to process audio files, storing timestamps and raw transcripts into Turso.

   Add an audio file upload component to the Next.js app supporting MP3, WAV, and M4A formats. Create a server-side action that receives the uploaded audio file, streams it to the OpenAI Whisper API with timestamp granularity enabled, and parses the returned JSON response containing speaker segments and text. Save these transcript chunks directly to the 'transcripts' table linked to a newly created meeting record. Handle large files by chunking if necessary or handling serverless timeout limits.

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3. 03Implement LLM Summarization and Action Item Extraction

   Create an automated background routine using the Anthropic API to analyze raw transcripts, synthesize structured meeting notes, and extract actionable items.

   Write a server-side utility that triggers after a transcript is successfully saved. This utility should fetch all transcript lines for a meeting, construct a structured prompt for the Anthropic API (Claude 3.5 Sonnet), and ask it to output a JSON object containing: 1) an executive summary, 2) key discussion points, and 3) a list of extracted action items with suggested assignees. Store the resulting structured notes into the 'summaries' table and individual action items into the 'action_items' table. Display these cleanly formatted sections on the meeting detail page.

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4. 04Build Conversational Search (RAG) Across Meetings

   Implement a semantic search engine allowing users to query past meetings, leveraging text embeddings stored in Turso or vector extensions.

   Implement a conversational search page where users can ask questions about any past meeting. When a meeting summary or transcript is created, generate text embeddings using an OpenAI embedding model and store them in a vector column or separate table in Turso. When a user submits a query, compute its embedding, perform a vector similarity search to retrieve relevant context chunks, and pass them along with the user query to the Anthropic API to generate a precise, cited answer summarizing past conversations.

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5. 05Develop Electron Companion App for Local Audio Capture

   Build a lightweight Electron wrapper or standalone script for desktop system audio capture to feed local recordings straight to the web application.

   Create a minimal Electron desktop companion app with a system tray icon and a simple control window with 'Start Recording' and 'Stop Recording' buttons. Using desktopCapturer or native audio capture APIs, capture system audio and microphone streams locally into a single audio buffer. Once recording stops, encode the buffer into an MP3 file and automatically upload it via an authenticated API endpoint to the Next.js web application backend for processing.

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Cost vs paying for Circleback