How to vibe codeScreenApp
AI-powered meeting assistant and video-processing platform
screenapp.io ↗SaaS
The verdict: can you vibe code ScreenApp?
Build a personal subset web clone with basic uploads and Whisper transcription, but keep paying for the production app due to the brutal complexity of live meeting bots and cross-platform native recording.
Replicating ScreenApp's core value for personal use means building a web app where you upload video/audio files, run them through Whisper for transcription, and query them with an LLM. However, building reliable headless browser meeting bots that join Zoom, Teams, and Google Meet without crashing or triggering anti-bot firewalls is a massive engineering trap. A solo developer will spend weeks fighting WebRTC streams and headless browser instability alone.
Estimated effort: 4-6 weeks of part-time work
What you can't replicate
- The 7.9M user network effect
- Reliable enterprise-grade headless meeting bots for every major conferencing platform
- Cross-platform native OS recorders for Mac, Windows, iOS, and Android
Founded
2023
Raised
—
Team
13-17
Cheapest paid tier
$19/mo
What ScreenApp does
ScreenApp records, transcribes, summarizes, and analyzes audio and video content with AI-generated notes, chat across historical files, and meeting bots.
Core features
- Screen and audio recording across web and desktop
- Automated meeting bots for Zoom, Teams, and Google Meet
- Speech-to-text transcription in 100+ languages
- AI summarization, chapters, and action item extraction
- Second brain chat across historical recordings via RAG
- Video compression, conversion, and clip-making tools
The business
Pricing
- FreeFree
- Growth$19/mo
- Business$34/mo
- Enterprise$199/mo
Funding
Unknown / bootstrapped
Pay vs build, cumulative
Break-even at month 4 — after that, every month is money kept.
The hard parts of vibe coding ScreenApp
- Headless browser orchestration and anti-bot bypass for live meeting bots
- Robust video processing, chunking, and storage pipelines at scale
- Cross-platform client footprint across web, extensions, mobile, and desktop
- Low-latency vector search and LLM context management across large video files
How to vibecode ScreenApp
Prerequisites
Node.jsfree
Runtime environment for TypeScript and full-stack development.
GitHubfree
Version control and deployment pipeline integration.
OpenAI API Keypay-per-use (~$10/mo)
Required for Whisper transcription and GPT summarization features.
AI coding tools
Recommended stack
| Frontend | Next.js |
|---|---|
| Backend | Next.js API Routes |
| Database | Neon |
| Auth | better-auth |
| Payments | none |
| Other | Cloudflare R2, OpenAI API (Whisper), Vercel AI SDK, Tailwind CSS |
Hosting & infrastructure
| Vercel | Hosting the Next.js frontend and serverless API functions. | $0/mo (Hobby) |
| Cloudflare R2 | Storing recorded audio and video files with zero egress fees. | $0-5/mo |
Build guide
01Project Scaffolding & Database Setup
Initialize the Next.js project with Tailwind CSS, configure Neon Postgres for metadata storage, and set up better-auth for personal access.
Scaffold a new Next.js project using Next.js, Tailwind CSS, and TypeScript. Set up a Neon Postgres connection using Drizzle ORM with tables for 'users', 'recordings' (id, user_id, title, duration, file_url, transcript_text, summary, created_at), and 'chats'. Integrate better-auth for secure email/password authentication. Ensure proper environment variable configuration and modular folder structures.02Media Upload & Storage Pipeline
Implement browser-based media recording or file upload, piping files directly to Cloudflare R2 object storage.
Build a dashboard page in Next.js featuring a file upload dropzone and a browser MediaRecorder component to capture microphone/screen audio. Implement a secure presigned URL generation endpoint that uploads media files directly to Cloudflare R2 object storage. Save the file metadata (filename, storage key, file size) into the Neon database upon successful upload.03Transcription Pipeline with OpenAI Whisper
Create a background processing step that takes uploaded media, sends it to the OpenAI Whisper API, and saves the resulting timestamped transcript.
Create a server action or API route that triggers when a recording is successfully uploaded. Fetch the media file from Cloudflare R2, send it to the OpenAI Whisper API for speech-to-text transcription, and store the resulting full transcript text in the database. Update the recording status from 'processing' to 'ready' and display the progress state in the UI.04AI Summarization & Action Items
Leverage the Vercel AI SDK and LLMs to parse transcripts into executive summaries, key decisions, and action items.
Implement an AI summarization feature using the Vercel AI SDK and Anthropic or OpenAI models. When a user views a completed transcript, provide a 'Generate Summary' button that analyzes the transcript text and outputs structured JSON containing an executive summary, key decisions, chapters with timestamps, and action items. Save these insights to the database and render them neatly in the UI.05Second Brain RAG Chat Interface
Build a conversational interface allowing users to query across their historical transcripts using vector embeddings or full-text context.
Build a chat interface panel component ('Ask AI') using the Vercel AI SDK useChat hook. Implement a retrieval-augmented generation (RAG) backend query that searches the user's transcript database using pgvector or full-text matching to inject relevant recording context into the prompt, allowing users to ask natural language questions across all their recorded meetings.06Dashboard Polish & Export Tools
Add search functionality, filtering across transcripts, and a clean PDF/text export feature for meeting notes.
Design a comprehensive user dashboard featuring search across all transcripts, tag filters, sorting by date, and a detail view with video playback synchronized to transcript text highlights. Add an export button that generates a clean markdown or PDF document containing the summary, action items, and full transcript.
Cost vs paying for ScreenApp
What will you build it with?
Starting total with Claude Code$0 one-time
Starting costs (one-time)
- Domain Name$12/yr
Total~$12 one-time
Ongoing costs (monthly)
- Cloudflare R2 Storage & Vercel$0-5/mo
- OpenAI Whisper & LLM API Usage~$10/mo
Total~$15/mo
Paying for ScreenApp
$19/mo
Your time to build
35-50 hours
AI tool credits
$20/mo (Claude Pro / Cursor)
Break-even
Personal learning project (not built for financial ROI)
Vibe code ScreenApp: FAQ
- Can you vibe code ScreenApp yourself?
- Serious undertaking — 45/100 vibecodeable. Build a personal subset web clone with basic uploads and Whisper transcription, but keep paying for the production app due to the brutal complexity of live meeting bots and cross-platform native recording.
- How long does it take to vibe code ScreenApp?
- 4-6 weeks of part-time work — roughly 35-50 hours of hands-on time with an AI coding agent.
- How do you build your own ScreenApp?
- Scoped to personal use: Next.js on the front, Next.js API Routes behind it, Neon for data. Follow the 6-step build guide on this page — each step has a paste-ready prompt for an AI coding agent.
- How do you code your own ScreenApp 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: 4-6 weeks of part-time work. The prompts on this page are written so the AI does the heavy lifting.
- How much does it cost to vibe code ScreenApp instead of paying?
- About ~$12 one-time to start and ~$15/mo to run, versus $19/mo for ScreenApp. Break-even: Personal learning project (not built for financial ROI).
- What stack should you use to vibe code ScreenApp?
- Next.js; Next.js API Routes; Neon; plus Cloudflare R2, OpenAI API (Whisper), Vercel AI SDK, Tailwind CSS.