clone-discovery

name	clone-discovery
description	Discovery phase -- compare 20+ competing services, build score matrix, select best clone target

Clone Discovery

When to Use

• Starting a clone project from scratch -- need to pick the right target
• Have a product category but no specific product chosen
• Need structured comparison of GenAI market segment

Process

1. Define category -- narrow down the product space (e.g. "AI presentation builders", "AI writing assistants")
2. Web search -- find 20+ products via Product Hunt, G2, AlternativeTo, GitHub Awesome lists, Reddit threads
3. Build comparison table -- for each product capture:
   • Name, URL, pricing (free tier / paid / enterprise)
   • Core features (bullet list)
   • Tech stack (frontend, backend, AI model)
   • Open-source availability (full OSS / partial / closed)
   • User base & traction (downloads, stars, reviews)
4. Score matrix -- rate each product 1-5 on:
   • Openness -- how much code/architecture is visible
   • Complexity -- effort to replicate (lower = better for cloning)
   • Market fit -- demand and willingness to pay
   • Differentiation potential -- room to improve over original
5. Select target -- pick the product with highest composite score
6. Write rationale -- 3-5 sentences explaining why this target was chosen

Output

• Comparison table (markdown) with 20+ entries
• Score matrix with weighted scores
• Recommended target + rationale document
• List of runner-up alternatives

Example

Category: "AI slide generators". Search yields Gamma.app, Beautiful.ai, Tome, SlidesAI, Decktopus, etc. Score matrix shows Gamma.app wins on market fit (4.8M users) + moderate complexity + partial API visibility. Recommendation: clone Gamma.app with focus on open-source LLM backend.