How to vibe codeSwell AI
Repurpose Audio or Video Content for Every Marketing Channel
swellai.com ↗Web app
The verdict: can you vibe code Swell AI?
You can build a solid personal-use clone of Swell AI for processing your own podcast episodes, but dealing with long-running media transcription queues and RAG chatbot pipelines will test your debugging patience.
Swell AI is a classic wrapper-plus-pipelines SaaS. The UI is straightforward dashboard CRUD, but the engineering meat is entirely asynchronous: accepting a massive podcast audio file, sending it to a transcription API, splitting the output, feeding chunks to LLMs with strict prompting templates, and building a vector search index for the episode chat. An AI coding agent will scaffold the Next.js frontend and basic database schema in an afternoon, but getting background job queues and the transcript clipping state management right requires careful iteration.
Estimated effort: 2-3 weeks part-time
What you can't replicate
- The exact user base and organic agency integrations
- Proprietary fine-tuned brand voice models accumulated over thousands of production runs
Founded
2023
Raised
—
Team
Very small team
Cheapest paid tier
$0/mo
What Swell AI does
AI-powered content repurposing platform that ingests long-form audio or video files and automatically generates transcripts, show notes, blog posts, newsletters, social assets, video clips, and RAG-powered episode chatbots.
Core features
- Media ingestion via direct upload, YouTube links, and RSS feeds
- Automatic speech-to-text transcription with speaker diarization
- Interactive transcript viewer with clip selection and highlight timestamps
- AI text generation engine for show notes, blog posts, newsletters, and social copy
- Vector embeddings and RAG pipeline for conversational episode chat bots ('Swell Chat')
- Project dashboard to manage multi-episode catalogs and show branding
- Webhook and REST API system for automated content pipelines
The business
Pricing
- Hobby$0/mo
- Studio$29/mo
- Agency$49/mo
- Pay-As-You-Go$8.99
Funding
Unknown / bootstrapped
Pay vs build, cumulative
Break-even at month 1 — after that, every month is money kept.
The hard parts of vibe coding Swell AI
- Handling large multi-gigabyte media uploads and managing background worker queues without timing out
- Synchronizing audio player playback states with precise timestamp highlights in a custom interactive text editor
- Chunking long transcripts, generating vector embeddings, and building a responsive RAG retrieval pipeline for episode chats
- Orchestrating multi-step LLM pipelines that format raw transcripts into structured templates (LinkedIn threads, long articles, show notes)
How to vibecode Swell AI
Prerequisites
Node.jsfree
Required runtime for Next.js development and package management
GitHubfree
Source control and deployment pipeline integration
OpenAI API Keypay-as-you-go
Powers Whisper transcription and GPT-4o text generation pipelines
AI coding tools
Recommended stack
| Frontend | Next.js with Tailwind CSS and shadcn/ui |
|---|---|
| Backend | Next.js App Router API routes with Inngest for background jobs |
| Database | Supabase (PostgreSQL with pgvector for transcript embeddings) |
| Auth | better-auth |
| Payments | none |
| Other | Vercel AI SDK, OpenAI API (Whisper + GPT), UploadThing |
Build guide
01Project Scaffolding & Database Schema
Initialize the Next.js project with Tailwind CSS, shadcn/ui, better-auth, and Supabase client bindings including pgvector support.
Create a new Next.js project with App Router, TypeScript, and Tailwind CSS. Configure better-auth for local single-user or multi-user authentication using a Supabase PostgreSQL database. Write the database migration file using SQL to create tables for `podcasts`, `episodes` (with columns for title, audio_url, raw_transcript, duration, status), and `transcript_chunks` with a `vector(1536)` column for embeddings. Set up Drizzle ORM or Supabase JS client configuration in `lib/supabase.ts`. Ensure all environment variables are documented in `.env.example`. Test the database connection with a simple health check route.02Media Ingestion & Transcription Pipeline
Build file upload handling via UploadThing and a background transcription workflow using OpenAI Whisper API.
Implement a media upload component using UploadThing for handling audio and video files up to 500MB. When an upload completes, trigger an API route that creates an episode record with status `processing`. Call the OpenAI Whisper API (`v1/audio/transcriptions`) with timestamp granularities to retrieve verbatim speaker-labeled and timestamped transcript segments. Store the JSON transcript inside the `episodes` table and update the status to `completed`. Handle errors robustly with fallback retry states and display processing progress indicators in the UI.03Interactive Transcript Editor & Clipping
Create an interactive transcript dashboard where users can view timestamps, search text, and highlight clips.
Build a dashboard page in Next.js at `/episodes/[id]` featuring an HTML5 audio player on the left and a scrollable, synchronized transcript viewer on the right. Render transcript segments with speaker labels and timestamps. Clicking any transcript line must seek the audio player to that exact second. Add text selection functionality that lets users highlight a range of transcript text, click 'Create Clip', and save the snippet to a `clips` table linked to the episode. Include search filtering across transcript text.04AI Content Generation Engine
Implement automated generation of show notes, blog posts, newsletters, and social media posts using Vercel AI SDK and OpenAI.
Create a content generation panel within the episode view that includes a template selector (Show Notes, Blog Article, LinkedIn Post, Twitter Thread). Using the Vercel AI SDK (`ai` package) and OpenAI models, write server actions that take the episode's raw transcript, apply specific prompt engineering templates based on selected formats, and stream the generated markdown back to the UI. Provide an interface to edit the generated copy and export or copy it to the clipboard.05RAG-Powered Episode Chat ('Swell Chat')
Build a conversational vector search chatbot that answers questions based on individual episode content.
Implement the 'Swell Chat' feature for individual episodes. Upon episode transcription completion, chunk the transcript text, generate embeddings via OpenAI `text-embedding-3-small`, and store them in the `transcript_chunks` table linked to the episode ID. Build a chat interface component using `useChat` from the Vercel AI SDK. In the chat API route, take the user query, embed it, perform a vector similarity search (`cosine distance`) against the episode's chunks using Supabase pgvector, construct a prompt with the retrieved context, and stream the conversational answer back to the user interface.06Dashboard Polish & RSS Import Support
Add multi-show project management and podcast RSS feed ingestion support.
Build a multi-project dashboard where users can group episodes by podcast show. Implement an RSS feed parser utility using `rss-parser` that accepts a podcast RSS URL, lists recent episodes, and allows bulk importing of audio enclosure URLs into the database queue for automated transcription and content generation. Polish the overall Tailwind UI with consistent dark/light mode styling, loading skeletons, and notification toasts.
Cost vs paying for Swell AI
What will you build it with?
Starting total with Claude Code$0 one-time
Starting costs (one-time)
- Domain name (optional)$12 one-time
- OpenAI API starting credits$10 one-time
Total~$22 one-time
Ongoing costs (monthly)
- Supabase DB & Storage$0/mo
- Vercel Hosting$0/mo
- OpenAI Whisper & GPT API usage (per uploaded hour)~$0.15/hr
Total~$3-10/mo depending on media volume
Paying for Swell AI
$29/mo (Studio Plan)
Your time to build
25-35 hours
AI tool credits
$20 (Claude Pro)
Break-even
Pays for itself immediately if processing over 4 hours of audio per month compared to paid tiers, though built primarily for learning and personal customization.
Vibe code Swell AI: FAQ
- Can you vibe code Swell AI yourself?
- Solid side project — 62/100 vibecodeable. You can build a solid personal-use clone of Swell AI for processing your own podcast episodes, but dealing with long-running media transcription queues and RAG chatbot pipelines will test your debugging patience.
- How long does it take to vibe code Swell AI?
- 2-3 weeks part-time — roughly 25-35 hours of hands-on time with an AI coding agent.
- How do you build your own Swell AI?
- Scoped to personal use: Next.js with Tailwind CSS and shadcn/ui on the front, Next.js App Router API routes with Inngest for background jobs behind it, Supabase (PostgreSQL with pgvector for transcript embeddings) 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 Swell AI 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: 2-3 weeks part-time. The prompts on this page are written so the AI does the heavy lifting.
- How much does it cost to vibe code Swell AI instead of paying?
- About ~$22 one-time to start and ~$3-10/mo depending on media volume to run, versus $29/mo (Studio Plan) for Swell AI. Break-even: Pays for itself immediately if processing over 4 hours of audio per month compared to paid tiers, though built primarily for learning and personal customization..
- What stack should you use to vibe code Swell AI?
- Next.js with Tailwind CSS and shadcn/ui; Next.js App Router API routes with Inngest for background jobs; Supabase (PostgreSQL with pgvector for transcript embeddings); plus Vercel AI SDK, OpenAI API (Whisper + GPT), UploadThing.