How to vibe codetl;dv
The AI Notetaker built for Team Collaboration
tldv.io ↗AI Meeting Assistant & Conversational Intelligence
The verdict: can you vibe code tl;dv?
Build a personal meeting search wrapper instead of a full clone; capturing live multi-platform video streams and orchestrating STT at scale requires heavy distributed engineering.
Attempting to build a full-scale clone of tl;dv for personal use is a serious undertaking. While putting a Next.js wrapper around a transcript summarizer is trivial, capturing audio/video streams from Zoom, Google Meet, and Microsoft Teams without bot restrictions, handling diarization, and managing vector embeddings across hours of conversations will consume dozens of frustrating hours. Paying $18/month for the real product is vastly more rational unless you are doing this purely to learn media pipelines.
Estimated effort: 3-4 months of part-time development
What you can't replicate
- The massive active user base and proprietary cross-company meeting insights
- Native zero-friction hook implementations across thousands of enterprise apps
- The institutional reliability of SOC 2 and GDPR compliance infrastructure
Founded
2020
Raised
$4.5M
Team
~60
Cheapest paid tier
$0
What tl;dv does
An AI-powered meeting assistant and conversational intelligence platform that records, transcribes, and summarizes virtual meetings across Zoom, Google Meet, and Microsoft Teams.
Core features
- Automated meeting recording and HD video/audio capture
- Speech-to-text transcription with speaker diarization in 30+ languages
- LLM-powered post-meeting summaries and custom prompt templates
- Cross-meeting semantic search and thematic intelligence
- CRM and project management auto-sync
- Model Context Protocol (MCP) server integration for developer tools
The business
Pricing
- Free Forever$0
- Pro$18/mo
Funding
$4.5M from K Fund, Seedcamp, Mustard Seed Maze, Shilling Capital Partners, another.vc
Pay vs build, cumulative
No break-even inside 24 months at these numbers.
The hard parts of vibe coding tl;dv
- Building reliable cross-platform screen and audio capture without intrusive bots
- Orchestrating low-latency speaker diarization and translation pipelines at scale
- Managing massive token context windows and vector RAG for months of meeting transcripts
- Maintaining thousands of fragile third-party integrations (HubSpot, Salesforce, Slack)
How to vibecode tl;dv
Prerequisites
Node.jsFree
Required runtime for backend services and AI orchestration scripts.
GitHubFree
Version control and CI/CD pipelines.
AI coding tools
Recommended stack
| Frontend | Next.js |
|---|---|
| Backend | Node.js |
| Database | Neon |
| Auth | better-auth |
| Payments | Stripe |
| Other | Vercel AI SDK, OpenAI API, Anthropic API |
Build guide
01Project Scaffolding and Database Schema
Initialize the Next.js project with TypeScript, configure Tailwind CSS, set up better-auth, and define Neon Postgres tables for users, meetings, transcripts, and embeddings.
Initialize a new Next.js 16 project with TypeScript and Tailwind CSS. Configure better-auth with email/password authentication connected to a Neon Postgres database using Drizzle ORM. Create database schemas for users, meetings (id, title, duration, createdAt), transcripts (id, meetingId, speaker, text, timestampStart, timestampEnd), and summaries (id, meetingId, content, actionItems). Ensure environment variables are structured correctly in a .env.example file. Implement a clean, responsive dashboard layout shell with a sidebar navigation for meetings, search, and settings.02Audio Ingestion and Transcription Pipeline
Build an audio upload endpoint and integrate OpenAI Whisper API to process uploaded meeting recordings, extracting timestamps and speaker text.
Build a robust audio upload API route in Next.js that accepts MP3, WAV, or M4A meeting recording files up to 500MB. Integrate the OpenAI Whisper API to transcribe the uploaded audio file with precise timestamps and word-level timing. Save the generated transcript chunks into the Neon database linked to the meeting record. Add a frontend upload modal with progress indicators and error handling for failed uploads or unsupported file formats.03AI Summarization and Action Item Extraction
Use the Vercel AI SDK and Anthropic API to generate structured meeting notes, summaries, and action items immediately after transcription.
Implement a server action using the Vercel AI SDK and Anthropic API to process completed transcripts. When a transcript is ready, send it to Claude with a system prompt designed to extract an executive summary, key discussion points, and a bulleted list of action items with assignees. Store the structured output in the summaries table and display it cleanly on the meeting detail view page with options to copy or export markdown.04Cross-Meeting Semantic Search and RAG
Set up pgvector in Neon to index meeting transcripts, enabling natural language search across all historical conversations.
Configure pgvector extension in the Neon Postgres database. Write a background job or server action that generates vector embeddings for meeting transcript chunks using OpenAI's embedding model. Implement a global semantic search interface in the Next.js frontend where users can query their entire meeting history in natural language, retrieving relevant meeting snippets with contextual citations and direct links to timestamps.05MCP Server Implementation and Final Polish
Expose a lightweight Model Context Protocol (MCP) server endpoint to stream meeting summaries directly into developer tools like Claude Code.
Build a Model Context Protocol (MCP) server module using Node.js that exposes endpoints to query and retrieve meeting summaries and action items on demand. Secure the MCP endpoints with API keys. Add comprehensive error handling, input validation across all routes, and polish the dashboard UI for seamless daily usage.
Cost vs paying for tl;dv
What will you build it with?
Starting total with Claude Code$0 one-time
Starting costs (one-time)
- Domain name$12 one-time
- OpenAI / Anthropic API starter credits$20 one-time
Total~$32 one-time
Ongoing costs (monthly)
- Vercel Hobby/Pro hosting$0-20/mo
- OpenAI Whisper & Anthropic API usage~$10/mo
Total~$10-30/mo
Paying for tl;dv
$18/mo (Pro Plan)
Your time to build
60-80 hours
AI tool credits
$20 (Claude Code / Cursor)
Break-even
Never (paying $18/mo is cheaper than 80 hours of dev time)
Vibe code tl;dv: FAQ
- Can you vibe code tl;dv yourself?
- Serious undertaking — 35/100 vibecodeable. Build a personal meeting search wrapper instead of a full clone; capturing live multi-platform video streams and orchestrating STT at scale requires heavy distributed engineering.
- How long does it take to vibe code tl;dv?
- 3-4 months of part-time development — roughly 60-80 hours of hands-on time with an AI coding agent.
- How do you build your own tl;dv?
- Scoped to personal use: Next.js on the front, Node.js behind it, Neon for data. Follow the 5-step build guide on this page — each step has a paste-ready prompt for an AI coding agent.
- How do you code your own tl;dv without being an expert?
- Use an AI coding tool (Claude Code or Cursor) and work in small steps: scaffold, data model, core screens, then deploy. Realistic effort: 3-4 months of part-time development. The prompts on this page are written so the AI does the heavy lifting.
- How much does it cost to vibe code tl;dv instead of paying?
- About ~$32 one-time to start and ~$10-30/mo to run, versus $18/mo (Pro Plan) for tl;dv. Break-even: Never (paying $18/mo is cheaper than 80 hours of dev time).
- What stack should you use to vibe code tl;dv?
- Next.js; Node.js; Neon; plus Vercel AI SDK, OpenAI API, Anthropic API.