The Art of Hacky Code: From Clean to Compressed Through AI Prompting

In coding, there are two worlds. Clean, enterprise-grade code that’s safe and predictable. And raw, compressed, hacker-style code that bends the rules and gets results fast.

Most developers think you have to pick a side. But with AI, you don’t. You can take clean, verbose code and flip it into sleek, compressed, lightning-fast implementations — without losing functionality.

This is about writing code that’s not just smart, but unfairly effective.

The Starting Point: Enterprise-Style IoT Data Converter
Our journey began with a typical enterprise IoT data transformation requirement as part of the Deloitte Job simulation exercise. We needed to convert between different JSON formats for device data:

Input Format 1 (Flat Structure):

{
    "deviceID": "dh28dslkja",
    "deviceType": "LaserCutter",
    "timestamp": 1624445837783,
    "location": "japan/tokyo/keiyō-industrial-zone/daikibo-factory-meiyo/section-1",
    "operationStatus": "healthy",
    "temp": 22
}
Target Output (Structured):

{
    "deviceID": "dh28dslkja",
    "deviceType": "LaserCutter",
    "timestamp": 1624445837783,
    "location": {
        "country": "japan",
        "city": "tokyo",
        "area": "keiyō-industrial-zone",
        "factory": "daikibo-factory-meiyo",
        "section": "section-1"
    },
    "data": {
        "status": "healthy",
        "temperature": 22
    }
}

Phase 1: Clean Code Implementation
Initially, following clean code principles, the implementation was verbose and explicit:

def convertFromFormat1(jsonObject):
    # Validate input
    if not isinstance(jsonObject, dict):
        raise ValueError("Input must be a dictionary")

    # Check required fields
    required = ["deviceID", "deviceType", "timestamp", "location"]
    for field in required:
        if field not in jsonObject or jsonObject[field] is None:
            raise KeyError(f"Missing required field: '{field}'")

    # Parse location string into structured object
    location_parts = [part.strip() for part in jsonObject["location"].split('/') if part.strip()]
    location = {
        "country": location_parts[0] if len(location_parts) > 0 else "unknown",
        "city": location_parts[1] if len(location_parts) > 1 else "unknown", 
        "area": location_parts[2] if len(location_parts) > 2 else "unknown",
        "factory": location_parts[3] if len(location_parts) > 3 else "unknown",
        "section": location_parts[4] if len(location_parts) > 4 else "unknown"
    }

    # Extract sensor data (exclude device metadata fields)
    metadata_fields = {"deviceID", "deviceType", "timestamp", "location"}
    sensor_data = {}
    for key, value in jsonObject.items():
        if key not in metadata_fields:
            if key == "operationStatus":
                sensor_data["status"] = value
            elif key == "temp":
                sensor_data["temperature"] = value
            else:
                sensor_data[key] = value

    # Build result
    result = {
        "deviceID": jsonObject["deviceID"],
        "deviceType": jsonObject["deviceType"],
        "timestamp": jsonObject["timestamp"],
        "location": location,
        "data": sensor_data
    }

    return result
Stats: 35+ lines, highly readable, follows enterprise standards.

Phase 2: The Compression Strategy
The Prompting Technique
The key to successful code compression through AI prompting lies in progressive refinement:

Start with working code — Ensure functionality before optimizing
Use specific language — “make it hacky”, “compress this”,“act like a hacker”
Iterative refinement — Address errors and edge cases progressively
Request explanations — Ask for comments to understand the compressed logic
Critical Prompting Phrases
“possible more shorten lines of the code . act like a hacker”
“dont make them all separate function put them into singular one”
“find better way then the above”
“now make this one hacky way . remember to add comments”

Phase 3: The Compressed Result
Through iterative AI-assisted compression, the 35-line function became:


def convertFromFormat1(j):
    # Validate dict type and check all required fields exist and are not None in one mega line
    if not isinstance(j, dict) or any(f not in j or j[f] is None for f in ["deviceID", "deviceType", "timestamp", "location"]): raise ValueError("Invalid input")
    # Split location string by '/', strip whitespace, filter empties
    l = [p.strip() for p in j["location"].split('/') if p.strip()]
    # Field mapping dictionary for sensor data normalization
    m = {"operationStatus": "status", "temp": "temperature"}
    return {
        "deviceID": j["deviceID"], "deviceType": j["deviceType"], "timestamp": j["timestamp"],
        # Zip location parts with keys, pad with "unknown" if missing parts, slice to exactly 5 items
        "location": dict(zip(["country", "city", "area", "factory", "section"], (l + ["unknown"] * 5)[:5])),
        # Extract non-metadata fields, apply field mapping, build sensor data dict
        "data": {m.get(k, k): v for k, v in j.items() if k not in {"deviceID", "deviceType", "timestamp", "location"}}
    }

Stats: 6 functional lines, 83% size reduction, maintained functionality.

Key Compression Techniques Discovered

1. Variable Name Minimization jsonObject → j location_parts → l field_mappings → m
2. Conditional Chain Compression # Before: Multiple if statements

if not isinstance(jsonObject, dict):
    raise ValueError("Input must be a dictionary")
for field in required:
    if field not in jsonObject:
        raise KeyError(f"Missing field: {field}")

After: Single conditional with any()

if not isinstance(j, dict) or any(f not in j or j[f] is None for f in required): 
    raise ValueError("Invalid input")

1. Dictionary Construction Magic

# Before: Explicit conditionals
location = {
    "country": location_parts[0] if len(location_parts) > 0 else "unknown",
    "city": location_parts[1] if len(location_parts) > 1 else "unknown",
    # ... more repetition
}

After: Zip with padding

"location": dict(zip(["country", "city", "area", "factory", "section"], 
                    (l + ["unknown"] * 5)[:5]))

1. Comprehension Optimization

# Before: Loop with conditionals
sensor_data = {}
for key, value in jsonObject.items():
    if key not in metadata_fields:
        if key == "operationStatus":
            sensor_data["status"] = value
        elif key == "temp":
            sensor_data["temperature"] = value
        else:
            sensor_data[key] = value

After: Dictionary comprehension with mapping

"data": {m.get(k, k): v for k, v in j.items() 
         if k not in {"deviceID", "deviceType", "timestamp", "location"}}

Advanced Technique: Format 2 Implementation
The second format required timestamp conversion and nested object handling:

def convertFromFormat2(j):
    # Validate all required fields and nested device object in one line
    if not isinstance(j, dict) or any(f not in j or j[f] is None for f in ["device", "timestamp", "data"]) or not isinstance(j["device"], dict) or any(k not in j["device"] for k in ["id", "type"]): raise ValueError("Invalid input")
    # Convert ISO timestamp to unix if string, keep as-is if already number
    ts = int(datetime.fromisoformat(j["timestamp"].rstrip('Z')).timestamp() * 1000) if isinstance(j["timestamp"], str) else j["timestamp"]
    # Build location dict using dict comprehension with get() for missing fields
    loc_keys = ["country", "city", "area", "factory", "section"]
    return {
        "deviceID": j["device"]["id"], "deviceType": j["device"]["type"], "timestamp": ts,
        "location": {k: j.get(k, "unknown") for k in loc_keys},  # Extract location fields or default to "unknown"
        "data": j["data"]  # Copy data object as-is
    }

The Debugging Dance: When Compression Breaks
Common Pitfalls and Solutions

1. Type System Conflicts

Problem: Trying to slice a dictionary

"location": dict(zip(keys, values))[:5] # ❌ Can't slice dict

Solution: Slice the list first

"location": dict(zip(keys, (values + ["unknown"] * 5)[:5])) # ✅

1. Operator Precedence Issues

Problem: Wrong operator precedence

l + ["unknown"] * 5[:5] # ❌ Slices the number 5, not the list

Solution: Use parentheses

(l + ["unknown"] * 5)[:5] # ✅
Best Practices for AI-Assisted Code Compression

1. Progressive Refinement Start with working, verbose code Compress incrementally Test after each compression step
2. Strategic Prompting Use action-oriented language: “compress”, “minify”, “make hacky” Request specific constraints: “single function”, “fewer lines” Ask for explanations: “add comments to explain”
3. Error Handling Strategy When compression introduces bugs:

Share the exact error message
Ask for “alternative hacky ways”
Request specific fixes: “fix the return statement”

1. Comment Integration Request comments for complex compressed logic Use inline comments for particularly dense sections Explain the “black magic” for future maintainability When to Use Hacky Code Appropriate Scenarios

Competitive Programming: Where brevity and speed matter

Prototyping: Quick proof-of-concepts

Code Golf: Minimizing character count

Learning Exercise: Understanding Python’s advanced features
When to Avoid

Production Systems: Where maintainability is crucial

Team Environments: Where code readability affects collaboration

Complex Logic: Where compression obscures business logic

Critical Systems: Where debugging ease is paramount
Performance Implications

Interestingly, compressed code often performs better:

Metrics Comparison:

Lines of Code: 83% reduction (35 → 6 lines)

Function Calls: Fewer intermediate variables and function calls

Memory Usage: Reduced temporary object creation

Readability: Significantly decreased (trade-off)

_Conclusion: _The Art of Balanced Compression

AI-assisted code compression reveals the tension between readability and efficiency. Through strategic prompting and iterative refinement, we can transform verbose enterprise code into compact, functional implementations.

The key insights:

AI excels at mechanical transformations — Converting loops to comprehensions, merging conditionals
Progressive prompting works best — Build complexity incrementally
Comments are crucial — Compressed code needs explanation
Know your trade-offs — Compression sacrifices readability for brevity
The Developer’s Choice
The question isn’t whether to write clean or hacky code, but when to use each approach. AI-assisted development gives us the power to rapidly explore both extremes, letting us choose the right tool for the right context.

With develop skills with AI assistance, code becomes more like clay — shapeable, moldable, and optimizable for the specific needs of the moment.

“The best code is not the most beautiful or the most compressed, but the most appropriate for its purpose and audience.”

Further Reading
Python Code Golf Techniques
Competitive Programming Optimization Strategies
When Clean Code Principles Don’t Apply

About the Author: This exploration emerged from real AI-assisted development sessions, demonstrating practical techniques for code transformation through strategic prompting and iterative refinement.