# Books and Podcasts Platform - Planning Session
**Date:** June 12th, 2025
**Planning Tool:** Vibe Coder Stack Planner MCP Server
## Project Vision
**Project Name:** books and podcasts.com
**Problem Statement:**
When listening to podcasts (like Dwarkesh, World of DAS, 80,000 Hours) in the car, hosts and guests frequently mention interesting books, but listeners forget them by the time they get home and want to use their Audible credits.
**Solution:**
Build a website/app that processes unstructured podcast data to identify book mentions, then structures this information so users can:
- See what books were mentioned in specific episodes
- See which episodes mentioned a particular book
- Get Amazon purchase links for the books
- Filter and browse the data in different ways
## Target Users
**Primary Users:** Avid podcast listeners who frequently discover books through podcasts
- Intellectually curious people who listen to educational/interview-style podcasts
- Knowledge workers, entrepreneurs, academics, lifelong learners
- Listen to shows like: Dwarkesh, 80,000 Hours, World of DAS, Tim Ferriss, Joe Rogan (interesting guests), Lex Fridman
**Usage Patterns:**
- Check the site when they remember hearing about a book but can't recall details
- Browse for new reading material
- Use on mobile right after finishing podcast episodes
- Use on desktop when ready to purchase books
- Power users check regularly to see trending books across favorite podcasts
- Occasional but high-intent usage (ready to buy when they visit)
## Core Features & Data Requirements
### User Features:
1. Search for books by podcast name, episode, or book title
2. Browse trending books across podcasts or within specific shows
3. View detailed book pages showing which episodes mentioned them and context
4. Click through to purchase on Amazon/Audible with affiliate links
5. Save books to personal reading lists/wishlists
6. Get notifications when favorite podcasts mention new books
7. Filter by categories/topics (business, philosophy, science, etc.)
8. See "books like this" recommendations based on podcast overlap
### Data Requirements:
1. Podcast transcripts and audio files from major podcasts
2. Book metadata (titles, authors, descriptions, ISBNs, cover images)
3. Episode metadata (dates, descriptions, guest info)
4. Book mention extraction data (timestamps, context quotes)
5. Amazon/Audible product links and pricing
6. User accounts and saved book lists
7. Analytics on book popularity and trending data
8. Podcast RSS feeds for ongoing data collection
**Key Technical Challenge:** Accurately extracting book mentions from unstructured transcript data and matching them to actual book records.
## Scale & Timeline
- **Expected Scale:** thousands_plus users
- **Timeline:** month_or_two (1-2 months for initial version)
- **Budget:** reasonable ($50-200/month)
- **Technical Comfort:** enjoy_technical
## Recommended Tech Stack
### Frontend
- **React Native with Expo** (cross-platform mobile support)
### Backend
- **Next.js API routes** with **PlanetScale MySQL database**
### Hosting & Infrastructure
- **Vercel Pro** with premium add-ons as needed
- **Domain:** Namecheap or Google Domains (~$12/year)
### Why This Stack?
- Scales automatically as user base grows
- Well-supported with good documentation
- Fits technical comfort level and timeline
- Cost-effective for expected scale
### Estimated Costs
- **Monthly:** $50-150/month with room to scale
- **Setup Complexity:** Medium - coding required but well-supported
## Next Steps
1. Get detailed deployment instructions
2. Set up project structure
3. Implement AI/ML components for book mention extraction
4. Build core features iteratively
5. Test with real podcast data
## Technical Notes
The core challenge of extracting book mentions from unstructured podcast data will likely require:
- Natural Language Processing (NLP) models
- Named Entity Recognition (NER) for book titles and authors
- Audio transcription services (if working directly with audio)
- Book database APIs for metadata enrichment
- Machine learning models for improving extraction accuracy over time
