50 Popular Apps Reverse-Engineered into Claude-Readable Design Specs: Key Patterns for UI Cloning
u/meliwat spent weeks reverse-engineering 50 popular apps into structured markdown design specs and fed them to Claude to rebuild the UIs. The difference between near-perfect clones and drifting outputs came down to a few concrete practices observed at volume.
What Made Claude Nail UI Clones
- Exact values, not ranges.
#1A1A1Aworks. "dark gray" produces five different grays across five screens. - State coverage up front. Listing every state (empty, loading, error, filled) stopped Claude from inventing its own.
- Spacing as a scale, not per-element pixels. A 4/8/16/24 spacing system produced more consistent layouts than annotating every gap.
- Navigation as a graph. Explicit screen-to-screen transitions killed the "where does this button go" guessing.
What Didn't Help
Longer prose. Past a point, more words made the output worse, not better. Keep specs concise.
Available Resources
The entire collection is open-sourced on GitHub at github.com/Meliwat/awesome-ios-design-md. Each app ships with 3 spec depths: quick reference, standard build, or full pixel-level clone. All markdown, MIT license, no dependencies.
Community Question
The author asks: if you've done UI cloning with Claude, what patterns have you found that are missing from this list? Which apps are worth adding next?
📖 Read the full source: r/ClaudeAI
👀 See Also
Reddit user measures MCP token overhead: 67K tokens consumed before any question
A developer measured their MCP server token overhead at 67,000 tokens consumed before typing a single question, with Playwright MCP using 13,600 tokens and GitHub MCP using 18,000 tokens idle. They replaced MCP with skills and CLI tools for lower context costs.
GGUF Model Merging Script and Workflow for Qwen3.5-35B Variants
A Reddit user shared a Python script for merging GGUF model files with minimal loss, specifically combining HauhauCS's Qwen3.5-35B-A3B-Uncensored model with samuelcardillo's Claude-4.6-Opus-Reasoning-Distilled version. The script runs on Google Colab Free Tier and includes quantization support via llama-quantize.
Jan Adds One-Click OpenClaw Installation with Jan-v3-Base Model Integration
Jan now supports one-click installation of OpenClaw with direct integration to the Jan-v3-base model, keeping all operations local and private on your computer.
Local Memory System for AI Coding Tools Extracts 2,600+ Facts from Conversation Logs
A developer built a local memory layer that ingests conversation logs from Claude Code, Factory.ai, and Codex CLI, extracts structured facts using a local LLM, and auto-injects context into new sessions. After months of use, it has indexed 13,000+ messages and extracted 2,600+ facts.