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# Glue Coding Methodology
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## **1. Definition of Glue Coding**
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**Glue coding** is a new type of software construction method, whose core idea is:
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> **Almost entirely reusing mature open-source components, combining them into a complete system with minimal "glue code".**
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It emphasizes "connecting" rather than "creating", and is particularly efficient in the AI era.
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## **2. Background**
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Traditional software engineering often requires developers to:
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* Design architecture
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* Write logic themselves
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* Manually handle various details
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* Reinvent the wheel
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This leads to high development costs, long cycles, and low success rates.
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However, the current ecosystem has fundamentally changed:
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* Thousands of mature open-source libraries on GitHub
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* Frameworks covering various scenarios (Web, AI, distributed, model inference...)
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* GPT / Grok can help search, analyze, and combine these projects
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In this environment, writing code from scratch is no longer the most efficient way.
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Thus, "glue coding" has emerged as a new paradigm.
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## **3. Core Principles of Glue Coding**
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### **3.1 If it can be avoided, don't write it; if it can be minimized, minimize it.**
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Any function with a mature existing implementation should not be reinvented.
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### **3.2 If it can be copied and pasted, copy and paste.**
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Directly copying and using community-tested code is a normal engineering process, not laziness.
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### **3.3 Stand on the shoulders of giants, rather than trying to become a giant.**
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Utilize existing frameworks, rather than trying to write a "better wheel" yourself.
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### **3.4 Do not modify the original repository code.**
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All open-source libraries should remain as immutable as possible, used as black boxes.
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### **3.5 Minimize custom code.**
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The code you write only serves for:
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* Combination
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* Calling
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* Encapsulation
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* Adaptation
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This is the so-called **glue layer**.
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## **4. Standard Process of Glue Coding**
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### **4.1 Clarify Requirements**
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Break down the system's functionalities into individual requirements.
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### **4.2 Use GPT/Grok to Decompose Requirements**
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Let AI refine requirements into reusable modules, capabilities, and corresponding subtasks.
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### **4.3 Search for Existing Open-Source Implementations**
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Utilize GPT's internet capabilities (like Grok):
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* Search for corresponding GitHub repositories based on each sub-requirement
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* Check for reusable components
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* Compare quality, implementation methods, licenses, etc.
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### **4.4 Download and Organize Repositories**
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Pull selected repositories locally and categorize them.
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### **4.5 Organize by Architectural System**
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Place these repositories into the project structure, for example:
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```
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/services
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/libs
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/third_party
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/glue
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```
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And emphasize: **Open-source repositories, as third-party dependencies, must absolutely not be modified.**
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### **4.6 Write the Glue Layer Code**
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The glue code's functions include:
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* Encapsulating interfaces
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* Unifying input and output
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* Connecting different components
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* Implementing minimal business logic
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The final system is composed of multiple mature modules.
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## **5. Value of Glue Coding**
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### **5.1 Extremely High Success Rate**
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Because it uses community-verified mature code.
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### **5.2 Extremely Fast Development Speed**
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A large number of functionalities can be directly reused.
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### **5.3 Reduced Costs**
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Time costs, maintenance costs, and learning costs are significantly reduced.
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### **5.4 More Stable System**
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Relies on mature frameworks rather than individual implementations.
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### **5.5 Easy to Extend**
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Capabilities can be easily upgraded by replacing components.
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### **5.6 Strong Synergy with AI**
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GPT can assist in searching, decomposing, and integrating, serving as a natural enhancer for glue engineering.
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## **6. Glue Coding vs. Traditional Development**
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| Project | Traditional Development | Glue Coding |
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| ----------- | ----------------------- | --------------- |
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| Feature Implementation | Write yourself | Reuse open-source |
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| Workload | Large | Much smaller |
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| Success Rate| Uncertain | High |
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| Speed | Slow | Extremely fast |
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| Error Rate | Prone to pitfalls | Uses mature solutions |
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| Focus | "Inventing wheels" | "Combining wheels" |
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## **7. Typical Application Scenarios for Glue Coding**
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* Rapid prototyping
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* Small teams building large systems
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* AI applications/model inference platforms
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* Data processing pipelines
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* Internal tool development
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* System Integration
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## **8. Future: Glue Engineering Will Become the New Mainstream Programming Method**
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As AI capabilities continue to strengthen, future developers will no longer need to write large amounts of code themselves, but rather:
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* Find wheels
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* Combine wheels
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* Intelligently connect components
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* Build complex systems at extremely low cost
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Glue coding will become the new standard for software productivity.
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# 🧬 Glue Coding
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> **The Holy Grail and Silver Bullet of Software Engineering — finally here.**
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---
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## 🚀 Disruptive Manifesto
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**Glue Coding is not a technology; it's a revolution.**
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It might perfectly solve the three fatal flaws of Vibe Coding:
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| Pain Points of Traditional Vibe Coding | Solutions of Glue Coding |
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| :----------------------------------- | :----------------------- |
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| 🎭 **AI Hallucinations** - Generating non-existent APIs, incorrect logic | ✅ **Zero Hallucinations** - Only uses verified mature code |
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| 🧩 **Complexity Explosion** - The larger the project, the more out of control | ✅ **Zero Complexity** - Every module is a time-tested wheel |
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| 🎓 **High Barrier to Entry** - Requires deep programming skills to master AI | ✅ **Barrier Disappears** - You only need to describe "how to connect" |
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---
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## 💡 Core Concept
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```
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Traditional Programming: Humans write code
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Vibe Coding: AI writes code, humans review code
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Glue Coding: AI connects code, humans review connections
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```
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### Paradigm Shift
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**A fundamental shift from "generation" to "connection":**
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- ❌ No longer letting AI generate code from scratch (the source of hallucinations)
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- ❌ No longer reinventing the wheel (the source of complexity)
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- ❌ No longer requiring you to understand every line of code (the source of high barrier)
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- ✅ Only reusing mature, production-verified open-source projects
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- ✅ AI's sole responsibility: understanding your intent and connecting modules
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- ✅ Your sole responsibility: clearly describing "what is the input, what is the desired output"
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---
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## 🏗️ Architectural Philosophy
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```
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┌─────────────────────────────────────────────────────────┐
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│ Your Business Requirements │
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└─────────────────────────────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────┐
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│ AI Glue Layer │
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│ │
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│ "I understand what you want to do, let me connect these blocks" │
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│ │
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└─────────────────────────────────────────────────────────┘
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│
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┌────────────────┼────────────────┐
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▼ ▼ ▼
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┌─────────────┐ ┌─────────────┐ ┌─────────────┐
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│ Mature Module A │ │ Mature Module B │ │ Mature Module C │
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│ (100k+ ⭐) │ │ (Production Verified) │ │ (Official SDK) │
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└─────────────┘ └─────────────┘ └─────────────┘
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```
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**Entity**: Mature open-source projects, official SDKs, time-tested libraries
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**Link**: AI-generated glue code, responsible for data flow and interface adaptation
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**Function**: Your described business goals
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---
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## 🎯 Why is this a Silver Bullet?
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### 1. Hallucination Issue → Completely Disappears
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AI no longer needs to "invent" anything. It only needs to:
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- Read Module A's documentation
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- Read Module B's documentation
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- Write the data transformation from A to B
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**This is what AI excels at, and where it is least likely to make mistakes.**
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### 2. Complexity Issue → Transferred to the Community
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Behind each module are:
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- Thousands of Issue discussions
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- Wisdom of hundreds of contributors
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- Years of refinement in production environments
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**You are not managing complexity; you are standing on the shoulders of giants.**
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### 3. Barrier to Entry Issue → Minimized
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You don't need to understand:
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- Underlying implementation principles
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- Details of best practices
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- Handling of edge cases
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You just need to speak plainly:
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> "I want to take Telegram messages, process them with GPT, and save them to PostgreSQL"
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**AI will help you find the most suitable wheels and then glue them together.**
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---
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## 📋 Practical Process
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```
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1. Clarify Goal
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└─→ "I want to implement XXX functionality"
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2. Find Wheels
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└─→ "Are there mature libraries/projects that have done something similar?"
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└─→ Let AI help you search, evaluate, and recommend
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3. Understand Interfaces
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└─→ Feed official documentation to AI
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└─→ AI summarizes: what is the input, what is the output
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4. Describe Connection
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└─→ "Output of A should become input of B"
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└─→ AI generates glue code
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5. Verify Operation
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└─→ Runs successfully → Done
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└─→ Errors → Throw errors to AI, continue gluing
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```
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---
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## 🔥 Classic Case Study
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### Case Study: Polymarket Data Analysis Bot
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**Requirement**: Get real-time Polymarket data, analyze it, and push to Telegram
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**Traditional Approach**: Write scraper from scratch, write analysis logic, write Bot → 3000 lines of code, 2 weeks
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**Glue Approach**:
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```
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Wheel 1: polymarket-py (Official SDK)
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Wheel 2: pandas (Data Analysis)
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Wheel 3: python-telegram-bot (Message Push)
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Glue code: 50 lines
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Development time: 2 hours
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```
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---
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## 📚 Further Reading
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- [Glue Development Prompt](../../prompts/coding_prompts/glue-development.md)
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- [Project Practice: polymarket-dev](../Project%20Practical%20Experience/polymarket-dev/)
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---
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## 🎖️ Summary
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> **If you can copy, don't write; if you can connect, don't build; if you can reuse, don't originate.**
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Glue Coding is the ultimate evolutionary form of Vibe Coding.
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It's not laziness; it's the **highest embodiment of engineering wisdom**—
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Leveraging the least amount of original code to drive the greatest productivity.
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**This is the silver bullet software engineering has been waiting 50 years for.**
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---
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*"The best code is no code at all. The second best is glue code."*
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