The holy grail and silver bullet of software engineering - finally here.
Glue Coding is not a technology, but a revolution.
It might perfectly solve the three fatal flaws of Vibe Coding:
| Traditional Vibe Coding Pain Points | Glue Coding Solution |
|---|---|
| 🎭 AI Hallucinations - Generates non-existent APIs, incorrect logic | ✅ Zero Hallucinations - Uses only validated, mature code |
| 🧩 Complexity Explosion - The larger the project, the more out of control | ✅ Zero Complexity - Each module is a battle-tested wheel |
| 🎓 High Barrier - Requires deep programming skills to master AI | ✅ No Barrier - You only need to describe "how to connect" |
Traditional Programming: Humans write code
Vibe Coding: AI writes code, humans review code
Glue Coding: AI connects code, humans review connections
A fundamental shift from "generation" to "connection":
❌ No longer requiring you to understand every line of code (source of high barrier)
✅ Only reusing mature, production-validated open-source projects
✅ AI's sole responsibility: understand your intent, connect modules
✅ Your sole responsibility: clearly describe "what is the input, what is the desired output"
┌─────────────────────────────────────────────────────────┐
│ 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, battle-tested libraries Link: AI-generated glue code, responsible for data flow and interface adaptation Function: Your described business goal
AI no longer needs to "invent" anything. It only needs to:
This is what AI excels at, and what is least prone to errors.
Behind each module are:
You are not managing complexity; you are standing on the shoulders of giants.
You don't need to understand:
You only need to speak plain language:
"I want to take messages from Telegram, process them with GPT, and store them in PostgreSQL"
AI will help you find the most suitable wheels and glue them together.
1. Define the Goal
└─→ "I want to implement XXX functionality"
2. Find the Wheels
└─→ "Are there any mature libraries/projects that have done something similar?"
└─→ Let AI help you search, evaluate, and recommend
3. Understand the Interfaces
└─→ Feed the official documentation to AI
└─→ AI summarizes: what is the input, what is the output
4. Describe the Connection
└─→ "The output of A should become the input of B"
└─→ AI generates glue code
5. Validate and Run
└─→ Runs successfully → Done
└─→ Errors → Give the errors to AI, continue gluing
Requirement: Real-time acquisition of Polymarket data, analysis, and push to Telegram
Traditional Approach: Write a crawler, analysis logic, and bot from scratch → 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
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; it's the highest embodiment of engineering wisdom —
Leveraging maximum productivity with minimal original code.
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 is a new software construction approach, with its core philosophy being:
Almost entirely reusing mature open-source components, combining them into a complete system with minimal "glue code"
It emphasizes "connecting" rather than "creating," and is especially efficient in the AI era.
Traditional software engineering often requires developers to:
This leads to high development costs, long cycles, and low success rates.
However, the current ecosystem has fundamentally changed:
In this environment, writing code from scratch is no longer the most efficient way.
Thus, "Glue Coding" has emerged as a new paradigm.
Any functionality with an existing mature implementation should not be reinvented.
Directly copying and using community-validated code is a normal engineering process, not laziness.
Utilize existing frameworks instead of trying to write a "better wheel" yourself.
All open-source libraries should ideally remain immutable and be used as black boxes.
The code you write should only be responsible for:
This is what is called the glue layer.
Break down the system's desired functionalities into individual requirements.
Let AI refine requirements into reusable modules, capabilities, and corresponding subtasks.
Utilize GPT's web browsing capabilities (e.g., Grok):
Method: Let AI help you find GitHub Topics corresponding to your needs, then browse popular repositories under that topic.
Example Prompt:
I need to implement [Your Requirement]. Please help me:
1. Analyze which technical fields this requirement might involve
2. Recommend corresponding GitHub Topics keywords
3. Provide GitHub Topics links (format: https://github.com/topics/xxx)
Common Topics Examples: | Requirement | Recommended Topic | |:---|:---| | Telegram Bot | telegram-bot | | Data Analysis | data-analysis | | AI Agent | ai-agent | | CLI Tool | cli | | Web Scraper | web-scraping |
Advanced Tips:
https://github.com/topics/python?q=telegramPull the selected repositories locally and organize them by category.
Place these repositories within the project structure, for example:
/services
/libs
/third_party
/glue
And emphasize: Open-source repositories, as third-party dependencies, must absolutely not be modified.
The role of glue code includes:
The final system is composed of multiple mature modules.
Because it uses community-validated, mature code.
A large amount of functionality can be directly reused.
Time costs, maintenance costs, and learning costs are significantly reduced.
Relies on mature frameworks rather than individual implementations.
Capabilities can be easily upgraded by replacing components.
GPT can assist in searching, deconstructing, and integrating, making it a natural enhancer for glue engineering.
| 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" |
As AI capabilities continue to strengthen, future developers will no longer need to write large amounts of code themselves, but rather:
Glue Coding will become the new standard for software productivity.