🚀 Vibe Coding — Issue #37
Stop Coding. Start Deleting: Why Less Code Is Your New AI Advantage
Simplicity • Dead Code • Dependencies • AI Coding • Maintainability • Technical Debt
AI has completely changed the economics of writing code.
Need a helper function? Generate it.
Need another component? Generate it.
Need an abstraction? Generate it.
Need 500 lines for a new feature?
Done in seconds.
But there’s a problem.
When creating code becomes almost free, developers can accumulate code faster than they can understand it.
And suddenly the valuable skill isn’t:
How quickly can you create code?
It’s:
How confidently can you decide what shouldn’t exist?
Welcome to Issue #37 of Vibe Coding—where we’re going to do something AI coding tools rarely encourage us to do:
Delete things.
🧠 The AI Code Accumulation Problem
Traditional development had natural friction.
Writing 500 lines took time.
That friction forced developers to think:
Do I really need this?
AI removes much of that friction.
Now it’s easy to accumulate:
❌ helper functions used once
❌ duplicate utilities
❌ unnecessary abstractions
❌ unused components
❌ speculative features
❌ redundant validation
❌ libraries solving tiny problems
❌ compatibility code nobody needs anymore
Each piece seems harmless.
Together they become a system nobody completely understands.
🎯 Principle #1: Every Line Has a Cost
Code isn’t an asset simply because it works.
Every line creates future responsibility.
Someone may eventually need to:
- understand it
- test it
- debug it
- secure it
- document it
- update it
- migrate it
Vibe Rule
The cheapest code to maintain is code that doesn’t exist.
Before asking AI to add something, ask:
Can we solve this without adding more code?
🔍 Principle #2: Ask AI What Can Disappear
Most developers ask AI:
“What should I add?”
Try reversing the question.
Analyze this module and identify code that may
no longer be necessary.
Look for:
- unused functions
- duplicate logic
- unnecessary abstractions
- redundant validation
- obsolete compatibility code
- dependencies that could be removed
Do not delete anything yet.
Explain the evidence for each recommendation.
That final instruction matters.
AI should identify candidates.
You decide what gets deleted.
🗑️ Principle #3: Find Dead Code
Dead code is especially dangerous because it looks alive.
A function might exist.
A component might compile.
A library might still be installed.
But nothing actually uses it.
Ask:
Trace references to this code.
Which functions, variables, exports,
components, and dependencies appear unused?
For each one, explain how we could verify
that it is safe to remove.
AI becomes a codebase archaeologist.
🧩 Principle #4: Delete Abstractions That Don’t Earn Their Keep
Abstraction sounds sophisticated.
But every abstraction creates another concept developers must understand.
Consider:
Component
↓
Service
↓
Manager
↓
Helper
↓
Adapter
↓
Utility
↓
Actual Function
Sometimes that’s necessary.
Sometimes it’s architecture theatre.
Ask:
What problem is each layer solving?
If the answer is unclear, the abstraction may be creating complexity rather than removing it.
Vibe Rule
Abstraction should remove cognitive load—not relocate it.
📦 Principle #5: Question Every Dependency
Installing packages is easy.
Maintaining them isn’t.
Every dependency potentially introduces:
- updates
- vulnerabilities
- breaking changes
- compatibility issues
- bundle size
- additional APIs to understand
Before installing another library, ask:
Do I need a dependency for this?
Compare:
1. using the dependency
2. implementing the minimum functionality directly
Consider:
- complexity
- maintenance
- security
- bundle size
- long-term ownership
Sometimes the package wins.
Sometimes ten lines of understandable code are better.
⚡ Principle #6: Delete Features, Not Just Code
One of the hardest questions in software development:
Does anyone actually use this?
Teams often keep features because:
“Someone might need it.”
That sentence has created enormous amounts of software complexity.
AI can help analyze feature relationships and consequences, but usage data and human judgment should drive the decision.
Ask:
- Who uses this?
- How often?
- What happens if it’s removed?
- Does it still support the product’s goal?
- Is maintaining it worth the complexity?
Features have maintenance costs too.
📉 Principle #7: Measure Complexity, Not Output
AI creates a strange productivity illusion.
Developer A generates:
3,000 lines.
Developer B solves the same problem with:
600 lines.
Who was more productive?
You can’t answer from those numbers.
Lines written are not value delivered.
Sometimes:
+500 lines = progress
Sometimes:
-500 lines = progress
The better metrics are:
✔ easier to understand
✔ easier to modify
✔ fewer dependencies
✔ fewer failure points
✔ fewer concepts
✔ fewer places bugs can hide
🤖 The AI Deletion Review
Here’s a workflow worth adding to your projects.
After implementing a feature, don’t immediately move on.
Run a Deletion Review.
Prompt:
The feature is complete and working.
Now review the implementation with one goal:
REMOVE UNNECESSARY COMPLEXITY.
Identify:
1. code that can be deleted
2. abstractions that can be collapsed
3. duplicate logic
4. unnecessary dependencies
5. redundant configuration
6. speculative functionality
7. comments explaining code that should simply be clearer
Rank each suggestion:
HIGH VALUE
MEDIUM VALUE
LOW VALUE
Do not change behavior.
This flips AI from:
code generator
into:
complexity reducer.
🔥 The Negative-Code Mindset
Developers naturally celebrate additions.
New feature.
New component.
New integration.
New framework.
But mature engineering also celebrates subtraction.
🗑️ 400 obsolete lines removed.
📦 3 dependencies eliminated.
🧩 2 unnecessary layers collapsed.
⚡ 5 duplicate utilities replaced with one clear function.
That’s progress.
🧠 Advanced Pattern: Generate → Challenge → Reduce
The typical AI workflow is:
Prompt
↓
Generate
↓
Generate More
↓
Add Feature
↓
Generate More
Try this instead:
UNDERSTAND
↓
GENERATE
↓
VERIFY
↓
CHALLENGE
↓
SIMPLIFY
↓
DELETE
↓
SHIP
AI shouldn’t only accelerate creation.
It should accelerate clarity.
🧪 Issue #37 Challenge: Delete 100 Lines
Open a project you’ve been working on for a while.
Your challenge:
Delete 100 lines.
Not randomly.
Find:
🔍 dead code
🔍 duplication
🔍 outdated comments
🔍 unused imports
🔍 unnecessary wrappers
🔍 obsolete functions
🔍 overcomplicated conditions
Then run the tests.
If everything still works…
Ask yourself:
Why was that code there?
Now imagine performing this exercise regularly.
💡 The Bigger Shift
For decades, programming skill was partly constrained by how quickly developers could turn ideas into code.
AI changes that.
Generating implementation is becoming dramatically easier.
So other skills become more valuable:
Judgment.
Taste.
Architecture.
Simplicity.
Understanding.
Restraint.
Knowing what to build matters.
Knowing what not to build may matter even more.
🧠 The Core Lesson of Issue #37
AI can generate thousands of lines before lunch.
That doesn’t mean your application needs them.
The future of Vibe Coding isn’t:
Prompt → Generate → Ship
It’s:
Think → Generate → Question → Simplify → Ship
The goal isn’t the most code.
The goal isn’t the fewest lines either.
The goal is:
The least complexity necessary to solve the problem well.
AI gives us an extraordinary ability to create.
Great developers will pair that ability with something equally powerful:
The confidence to delete.
🔮 Coming in Issue #38
The AI Dependency Trap: When Your Coding Assistant Becomes a Crutch
AI can make you dramatically faster.
But what happens when you can’t explain the code it created?
Next we’ll explore:
- recognizing AI dependency
- maintaining fundamental coding skills
- understanding generated code
- when to code without AI
- using AI to learn instead of merely copy
- developing judgment that survives the next generation of tools
Because the goal of Vibe Coding isn’t to become dependent on AI.
It’s to become a more capable developer because of it. 🚀