vibe-coding-cn/documents/gluecoding.md

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.