A pragmatic methodology for building AI workflows — from idea to production.
"If it works, it's good enough."
This repository documents a battle-tested approach to AI workflow design and prototyping, rooted in a simple but powerful philosophy: validate the idea first, optimize later.
The methodology was born from a real-world factory management system — a production traceability tool built entirely on WordPress's post structure — prototyped originally in Scratch. That same thinking now extends to AI-powered automation stacks.
┌─────────────────────────────────────────────────────┐
│ LAYER 1 · THINK & PROTOTYPE │
│ Scratch / TurboWarp │
│ Validate logic and state transitions visually. │
│ Constraints spark creativity. │
├─────────────────────────────────────────────────────┤
│ LAYER 2 · AI PROTOTYPING │
│ Dify │
│ Prototype the AI layer without writing code. │
│ Hand off to non-engineers. Iterate fast. │
├─────────────────────────────────────────────────────┤
│ LAYER 3 · PRODUCTION AUTOMATION │
│ n8n │
│ Connect APIs, webhooks, and databases. │
│ Break limits with JavaScript/Python code nodes. │
└─────────────────────────────────────────────────────┘
The official Scratch environment enforces constraints that paradoxically free your thinking. Once the logic is validated, TurboWarp compiles the project to JavaScript for dramatically faster execution.
| Scratch | TurboWarp | |
|---|---|---|
| Role | Idea validation | Performance testing |
| Speed | Baseline | Up to ~10–18x faster |
| Editor | ✅ | ✅ |
| Extensions | Standard | Extended |
Note: Forkphorus is now in maintenance mode. TurboWarp is the recommended MOD.
Dify sits at the intersection of no-code accessibility and LLM power. It's the right tool when you need to:
- Prototype RAG pipelines and agent workflows quickly
- Hand a working AI app to a non-engineer
- Swap LLM providers without rewriting logic
- Test prompt strategies with a visual flow editor
Think of Dify as the packaging layer — it wraps AI capabilities into something usable before committing to a full engineering implementation.
Where Dify is for AI prototyping, n8n is for connecting systems at scale. Engineers will feel at home because:
- Data flows between nodes as visible, inspectable JSON
- JavaScript/Python code nodes provide an escape hatch for complex logic
- Self-hosting via Docker gives full infrastructure control
- Webhooks and REST APIs are first-class citizens
Vrew by VoyagerX (Korea) represents the current frontier of AI video editing — it treats video as text, enabling transcript-based editing, auto-captions, AI narration, and one-prompt video generation.
Since Vrew does not currently expose a public API, the equivalent pipeline can be assembled from open components:
VREW CAPABILITY → OPEN STACK EQUIVALENT
─────────────────────────────────────────────────
Speech recognition → Whisper (OSS) / AssemblyAI
Auto-captioning → Whisper + custom formatter
AI narration → ElevenLabs / OpenAI TTS
Script generation → Claude / GPT via Dify
AI image generation → Stable Diffusion / DALL·E
Video cutting & joining → FFmpeg
Prototyping flow:
① Scratch / TurboWarp → Validate state machine for video pipeline
② Dify → Prototype AI nodes (script, captions, voice)
③ n8n → Wire APIs into a production-grade workflow
The philosophy of this repository was proven in production before AI tools existed.
A factory traceability system was built by repurposing WordPress's post structure as a production data model:
WordPress Concept → Factory Management Meaning
──────────────────────────────────────────────────
Post ID → Lot / Serial number
Category / Tag → Process stage / Product type
Created / Updated at → Timestamp / Trace history
Custom fields → Arbitrary manufacturing data
Post status → Process state (draft → published → archived)
Prototype path: Scratch → WordPress → Running in production today.
This is the proof of concept for the entire methodology.
- Dify × WordPress REST API integration (natural language queries over factory data)
- Automated video captioning pipeline with Whisper + n8n
- TurboWarp state machine template for workflow prototyping
- Dify RAG pipeline for factory manual Q&A
- n8n workflow: audio → transcript → structured post → WordPress
| Tool | URL | Purpose |
|---|---|---|
| Dify | https://dify.ai | LLM app development platform |
| n8n | https://n8n.io | Workflow automation |
| TurboWarp | https://turbowarp.org | High-performance Scratch MOD |
| Vrew | https://vrew.ai | AI video editing (reference implementation) |
| Whisper | https://openai.com/research/whisper | Open-source speech recognition |
| AssemblyAI | https://www.assemblyai.com | Speech recognition API |
| ElevenLabs | https://elevenlabs.io | AI voice synthesis |
| FFmpeg | https://ffmpeg.org | Video processing |
| Phase | Tool | Goal |
|---|---|---|
| Conceive | Paper / mind | Define what to build |
| Validate | Scratch / TurboWarp | Make it work, fast |
| AI prototype | Dify | Test the AI layer, no code |
| Productionize | n8n | Connect APIs, automate |
| Persist | WordPress / GitHub | Store with battle-tested tools |
This stack deliberately avoids chasing every new framework. The through-line is always the same:
- Validate the logic in the simplest possible environment (Scratch).
- Prototype the AI layer without committing to code (Dify).
- Automate for production with full engineering control (n8n).
- Store with boring, proven technology (WordPress, GitHub).
New tools are evaluated as rungs on this ladder, not replacements for it.
📄 Japanese version: README_jp.md
Generated from a Claude conversation session — April 2026. Resume this project by sharing this README in a new session.