# Glue Coding (glue coding) Methodology

## **1. Definition of Glue Coding**

**Glue coding** is a new way of building software whose core idea is:

> **Almost entirely reuse mature open-source components, and combine them into a complete system with a minimal amount of “glue code.”**

It emphasizes “connecting” rather than “creating,” and is especially efficient in the AI era.

## **2. Background**

Traditional software engineering often requires developers to:

* Design the architecture
* Write the logic themselves
* Manually handle various details
* Repeatedly reinvent the wheel

This leads to high development costs, long cycles, and low success rates.

The current ecosystem has fundamentally changed:

* There are thousands of mature open-source libraries on GitHub
* Frameworks cover various scenarios (Web, AI, distributed systems, model inference…)
* GPT / Grok can help search, analyze, and combine these projects

In this environment, writing code from scratch is no longer the most efficient way.

Thus, “glue coding” becomes a new paradigm.

## **3. Core Principles of Glue Coding**

### **3.1 Don’t write what you don’t have to, and write as little as possible when you must**

Any functionality with a mature existing implementation should not be reinvented.

### **3.2 Copy-and-use whenever possible**

Directly copying and using community-verified code is part of normal engineering practices, not laziness.

### **3.3 Stand on the shoulders of giants, don’t try to become a giant**

Leverage existing frameworks instead of trying to write another “better wheel” yourself.

### **3.4 Do not modify upstream repository code**

All open-source libraries should be kept immutable as much as possible and used as black boxes.

### **3.5 The less custom code the better**

The code you write should only be responsible for:

* Composition
* Invocation
* Encapsulation
* Adaptation

This is the so-called **glue layer**.

## **4. Standard Process of Glue Coding**

### **4.1 Clarify requirements**

Break the system features to be implemented into individual requirement points.

### **4.2 Use GPT/Grok to decompose requirements**

Have AI refine requirements into reusable modules, capability points, and corresponding subtasks.

### **4.3 Search for existing open-source implementations**

Use GPT’s online capabilities (e.g., Grok):

* Search GitHub repositories corresponding to each sub-requirement
* Check whether reusable components exist
* Compare quality, implementation approach, licenses, etc.

### **4.4 Download and organize repositories**

Pull the selected repositories locally and organize them.

### **4.5 Organize according to the architecture**

Place these repositories into the project structure, for example:

```
/services  
/libs  
/third_party  
/glue  
```

And emphasize: **Open-source repositories are third-party dependencies and must not be modified.**

### **4.6 Write the glue layer code**

The roles of the glue code include:

* Encapsulating interfaces
* Unifying inputs and outputs
* Connecting different components
* Implementing minimal business logic

The final system is assembled from multiple mature modules.

## **5. Value of Glue Coding**

### **5.1 Extremely high success rate**

Because community-validated mature code is used.

### **5.2 Very fast development**

A large amount of functionality can be reused directly.

### **5.3 Reduced costs**

Time, maintenance, and learning costs are greatly reduced.

### **5.4 More stable systems**

Depend on mature frameworks rather than individual implementations.

### **5.5 Easy to extend**

Capabilities can be upgraded easily by replacing components.

### **5.6 Highly compatible with AI**

GPT can assist with searching, decomposing, and integrating — a natural enhancer for glue engineering.

## **6. Glue Coding vs Traditional Development**

| Item                         | Traditional Development | Glue Coding           |
| ---------------------------- | ----------------------- | --------------------- |
| How features are implemented | Write yourself          | Reuse open source     |
| Workload                     | Large                   | Much smaller          |
| Success rate                 | Uncertain               | High                  |
| Speed                        | Slow                    | Extremely fast        |
| Error rate                   | Prone to pitfalls       | Uses mature solutions |
| Focus                        | “Invent wheels”         | “Combine wheels”      |

## **7. Typical Application Scenarios for Glue Coding**

* Rapid prototyping
* Small teams building large systems
* AI applications / model inference platforms
* Data processing pipelines
* Internal tool development
* System integration

## **8. Future: Glue Engineering Will Become the New Mainstream Programming Approach**

As AI capabilities continue to strengthen, future developers will no longer need to write large amounts of code themselves, but will instead:

* Find wheels
* Combine wheels
* Intelligently connect components
* Build complex systems at very low cost

Glue coding will become the new standard of software productivity.
