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🧬 Glue Coding

The holy grail and silver bullet of software engineering – it's finally here.

🚀 Disruptive Manifesto

Glue Coding is not a technology, but a revolution.

It might perfectly solve the three fatal flaws of Vibe Coding:

Pain Points of Traditional Vibe Coding	Glue Coding's Solution
🎭 AI Hallucinations - Generating non-existent APIs, incorrect logic	✅ Zero Hallucinations - Only using validated, mature code
🧩 Complexity Explosion - The larger the project, the more out of control it becomes	✅ Zero Complexity - Every module is a time-tested wheel
🎓 High Barrier to Entry - Requires deep programming skills to master AI	✅ Barrier Disappears - You only need to describe "how to connect"

💡 Core Concept

Traditional Programming: Humans write code
Vibe Coding: AI writes code, humans review code
Glue Coding: AI connects code, humans review connections

Paradigm Shift

A fundamental shift from "generation" to "connection":

❌ No longer letting AI generate code from scratch (the source of hallucinations)
❌ No longer reinventing the wheel (the source of complexity)
❌ No longer requiring you to understand every line of code (the source of high barriers)
✅ Only reusing mature, production-validated open-source projects
✅ AI's sole responsibility: understanding your intent and connecting modules
✅ Your sole responsibility: clearly describing "what is the input, what is the desired output"

🏗️ Architectural Philosophy

┌─────────────────────────────────────────────────────────┐
│                   Your Business Needs                   │
└─────────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│                   AI Glue Layer                        │
│                                                         │
│   "I understand what you want to do, let me connect these blocks" │
│                                                         │
└─────────────────────────────────────────────────────────┘
                           │
          ┌────────────────┼────────────────┐
          ▼                ▼                ▼
   ┌─────────────┐  ┌─────────────┐  ┌─────────────┐
   │ Mature Module A │  │ Mature Module B │  │ Mature Module C │
   │  (100K+ ⭐)  │  │  (Production Validated)  │  │  (Official SDK)  │
   └─────────────┘  └─────────────┘  └─────────────┘

Entity: Mature open-source projects, official SDKs, time-tested libraries Link: AI-generated glue code, responsible for data flow and interface adaptation Function: Your described business objective

🎯 Why is this the Silver Bullet?

1. Hallucination Problem → Completely Disappears

AI no longer needs to "invent" anything. It only needs to:

Read Module A's documentation
Read Module B's documentation
Write the data transformation from A to B

This is what AI excels at, and what is least prone to errors.

2. Complexity Problem → Transferred to the Community

Behind each module are:

Thousands of Issue discussions
Hundreds of contributors' wisdom
Years of production environment refinement

You are not managing complexity, you are standing on the shoulders of giants.

3. Barrier to Entry Problem → Reduced to a Minimum

You don't need to understand:

Underlying implementation principles
Best practice details
Edge case handling

You just need to speak human language:

"I want to process Telegram messages with GPT and save them to PostgreSQL"

AI will help you find the most suitable wheels and then glue them together.

📋 Practical Workflow

1. Clarify Goal
   └─→ "I want to implement XXX function"

2. Find Wheels
   └─→ "Are there any mature libraries/projects that have done something similar?"
   └─→ Let AI help you search, evaluate, and recommend

3. Understand Interfaces
   └─→ Feed the official documentation to AI
   └─→ AI summarizes: what is the input, what is the output

4. Describe Connection
   └─→ "The output of A should become the input of B"
   └─→ AI generates glue code

5. Verify Run
   └─→ Runs successfully → Done
   └─→ Error → Give the error to AI, continue gluing

🔥 Classic Case Study

Case: Polymarket Data Analysis Bot

Requirement: Real-time acquisition of Polymarket data, analysis, and pushing to Telegram

Traditional Approach: Write a crawler from scratch, write analysis logic, write a Bot → 3000 lines of code, 2 weeks

Glue Approach:

Wheel 1: polymarket-py (Official SDK)
Wheel 2: pandas (Data Analysis)
Wheel 3: python-telegram-bot (Message Push)

Glue Code: 50 lines
Development Time: 2 hours

📚 Further Reading

语言层要素 - 8 Levels of Understanding 100% Code
胶水开发提示词
项目实战：polymarket-dev

🎖️ Summary

If you can copy, don't write; if you can connect, don't build; if you can reuse, don't originate.

Glue Coding is the ultimate evolution of Vibe Coding.

It's not laziness, but the highest manifestation of engineering wisdom –

Using the least amount of original code to leverage the greatest productivity.

This is the silver bullet software engineering has been waiting for for 50 years.

"The best code is no code at all. The second best is glue code."

Glue Coding Methodology

1. Definition of Glue Coding

Glue coding is a new software construction method, whose core idea is:

Almost entirely reusing mature open-source components, combining them into a complete system with a minimal amount of "glue code".

It emphasizes "connection" rather than "creation", especially efficient in the AI era.

2. Background

Traditional software engineering often requires developers to:

Design architecture
Write logic themselves
Manually handle various details
Reinvent the wheel

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

However, the current ecosystem has fundamentally changed:

Thousands of mature open-source libraries on GitHub
Frameworks covering various scenarios (Web, AI, Distributed, 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" has emerged as a new paradigm.

3. Core Principles of Glue Coding

3.1 Don't write what can be avoided, write as little as possible

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

3.2 Copy-paste whenever possible

Directly copying and using community-validated code is a normal engineering process, not laziness.

3.3 Stand on the shoulders of giants, don't try to be a giant

Utilize existing frameworks instead of trying to write a "better wheel" yourself.

3.4 Do not modify original repository code

All open-source libraries should ideally remain immutable, used as black boxes.

3.5 The less custom code, the better

Your written code only serves to:

Combine
Call
Encapsulate
Adapt

Which is the so-called glue layer.

4. Standard Workflow of Glue Coding

4.1 Clarify Requirements

Break down the system's functionality into individual requirements.

4.2 Use GPT/Grok to Deconstruct Requirements

Let AI refine requirements into reusable modules, capabilities, and corresponding subtasks.

4.3 Search for Existing Open-Source Implementations

Leverage GPT's internet capabilities (e.g., Grok):

Search for corresponding GitHub repositories for each sub-requirement.
Check for reusable components.
Compare quality, implementation methods, licenses, etc.

4.4 Download and Organize Repositories

Pull the selected repositories locally and organize them by category.

4.5 Organize According to Architectural System

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 never be modified.

4.6 Write Glue Layer Code

The role of glue code includes:

Encapsulating interfaces
Unifying input/output
Connecting different components
Implementing minimal business logic

The final system is composed of multiple mature modules.

5. Value of Glue Coding

5.1 Extremely High Success Rate

Because it uses community-validated mature code.

5.2 Extremely Fast Development Speed

A large amount of functionality can be directly reused.

5.3 Reduced Costs

Time cost, maintenance cost, and learning cost are all significantly reduced.

5.4 More Stable System

Relies on mature frameworks rather than individual implementations.

5.5 Easy to Extend

Capabilities can be easily upgraded by replacing components.

5.6 Strong Synergy with AI

GPT can assist in searching, deconstructing, and integrating, making it a natural enhancer for glue engineering.

6. Glue Coding vs. Traditional Development

Project	Traditional Development	Glue Coding
Feature Implementation	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	"Building wheels"	"Combining wheels"

7. Typical Application Scenarios for Glue Coding

Rapid prototype development
Small teams building large systems
AI application/model inference platforms
Data processing pipelines
Internal tool development
System Integration

8. Future: Glue Engineering will become the new mainstream programming method

As AI capabilities continue to strengthen, future developers will no longer need to write a lot of code themselves, but rather:

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

Glue coding will become the new standard for software productivity.

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