..

From Chaos To Code

Table of Contents

  1. Welcome to the AI Coding Circus: A Developer’s Tale
  2. Meet the AI Dream Team: Your New Quirky Coding Companions
  3. Starting Fresh: How to Keep AI Models From Going Rogue
  4. Taming Legacy Code: When AI Meets Your Ancient Codebase
  5. AI Gone Wild: Tales From the Code Generation Trenches
  6. Speaking AI’s Language: How to Stop Getting Unexpected Microservices
  7. The Daily AI Dance: A Day in the Life of Modern Development
  8. AI Personality Types: Choosing the Right Tool for the Job
  9. Survival Guide: Embracing the Beautiful Chaos of AI Development
  10. Resources & Cheat Sheet: Your AI Coding Emergency Kit

1. Welcome to the AI Coding Circus: A Developer’s Tale

If you’ve ever wanted an army of AI interns to handle your repetitive tasks, find hidden references, or refactor messy code, you’re in the right place. Over the past few months, I’ve built (and broken) enough projects with LLMs to fill a small library.

Here’s what I’ll cover:

  • How to plan new features using Aider Architect + o1/o3 reasoning models
  • How to generate code using Claude while keeping scope in check
  • How to use GPT-4o for thorough code reviews
  • How to use LangChain to coordinate all these steps effectively

Think of these tools like a team of developers with very different personalities:

  • Claude is the enthusiastic architect who just discovered microservices
  • GPT-4o is the thorough but verbose senior dev
  • Aider is the practical programmer who just wants to ship code
  • DeepSeek is the archeologist who knows where all the bodies are buried
  • Ollama is the fast but sometimes forgetful junior dev
  • Qdrant is the team member with photographic memory
  • LangChain is the project manager keeping everyone in sync

The key is knowing when to use each one. Sometimes you need Claude’s creativity, other times you need GPT-4o’s thoroughness, and occasionally you just need Aider to tell everyone to calm down and write a simple function.

2. Meet the AI Dream Team: Your New Quirky Coding Companions

I rely on a constellation of tools to keep me sane:

  • Aider: Works in two modes—/chat-mode architect for planning, /chat-mode code for generation.
  • Claude: Your brilliant but overenthusiastic architect.
    • Pros: Incredible at understanding complex systems and generating detailed implementations
    • Best for: Architecture discussions, complex refactoring, documentation 
      Best practices: Set clear scope and requirements upfront
  • GPT-4o: My final reviewer. Tends to be verbose but offers thorough checks.
  • Ollama: Local embeddings and smaller models to quickly index or query code without slamming external APIs.
  • DeepSeek: Another local tool for “deep” reasoning over code. Slower but thorough.
  • Repomix: Your code’s personal travel agent.
    • Bundles repos like a pro
    • Counts tokens so Claude doesn’t have a meltdown
    • Respects .gitignore (more than some team members do) 
      # When you run repomix and realize your codebase is...large
$ repomix bundle
"Sir, that's 500K tokens of technical debt"
  • Qdrant: The team member with photographic memory.
    • “Where’s that JSON parsing logic?”
    • “Which files touch the payment system?”
    • “Who wrote this comment and why were they so angry?”
  • LangChain: The “Orchestra Conductor” that ties multiple LLMs and steps together (embedding, searching, chaining prompts, etc.).

No single tool does everything perfectly. I tend to let them tag-team each task like an unstoppable pro-wrestling faction.

3. Starting Fresh: How to Keep AI Models From Going Rogue

3.1 Incremental Development in Practice

Here’s how I build features step by step:

// Initial task: Add user preferences
public class UserPreferences {
    // Step 1: Basic structure with validation
    private Map<String, String> preferences = new HashMap<>();
    private static final int MAX_KEY_LENGTH = 50;
    
    public void setPreference(String key, String value) {
        validateKey(key);  // Start with basic validation
        preferences.put(key, value);
    }
    
    // Step 2: Add robust validation
    private void validateKey(String key) {
        if (key == null || key.trim().isEmpty()) {
            throw new IllegalArgumentException("Key cannot be null or empty");
        }
        if (key.length() > MAX_KEY_LENGTH) {
            throw new IllegalArgumentException("Key length exceeds " + MAX_KEY_LENGTH);
        }
    }
    
    // Step 3: Add type safety and conversion
    public <T> T getPreference(String key, Class<T> type) {
        String value = preferences.get(key);
        if (value == null) return null;
        
        return convertToType(value, type);
    }
    
    // Step 4: Add conversion logic
    @SuppressWarnings("unchecked")
    private <T> T convertToType(String value, Class<T> type) {
        if (type == String.class) return (T) value;
        if (type == Integer.class) return (T) Integer.valueOf(value);
        if (type == Boolean.class) return (T) Boolean.valueOf(value);
        throw new UnsupportedOperationException("Type not supported: " + type);
    }
}

// Step 5: Add comprehensive tests
@Test
public void testUserPreferences() {
    UserPreferences prefs = new UserPreferences();
    
    // Happy path
    prefs.setPreference("theme", "dark");
    assertEquals("dark", prefs.getPreference("theme", String.class));
    
    // Type conversion
    prefs.setPreference("notifications", "true");
    assertTrue(prefs.getPreference("notifications", Boolean.class));
    
    // Validation
    assertThrows(IllegalArgumentException.class, () -> 
        prefs.setPreference("", "value"));
}

Each step builds on the previous one, adding functionality incrementally:

  1. Start with core structure
  2. Add basic validation
  3. Implement type safety
  4. Add conversion logic
  5. Write comprehensive tests

3.2 The AI Review Dance

3.3 Review & Refinement With GPT-4o (and More Claude!)

  1. The Review Dance: 
    Me: "Review StockFetcher.java"
GPT-4o: "Let me write a thesis on stock market data patterns..."
Me: "Just check for bugs please"
GPT-4o: "Oh! You're missing error handling here and here"
  2. The Refinement Tango:
    • Feed GPT-4o’s feedback to Claude
    • Watch Claude try to rewrite everything
    • Gently guide it back to just fixing the specific issues
    • Repeat until code actually works
  3. The Final Waltz: 
    Me: "One last review before commit?"
Claude: "What if we added WebSocket support?"
Me: "NO"
Claude: "...fine, the code looks good as is."

Pro Tip: Keep a “prompt diary” of successful interactions. When Claude suggests adding Redis to a Hello World program, you’ll know exactly how to talk it down.

Real Story: During one planning session, I accidentally let the AI brainstorm without boundaries. It designed a system that would:

  • Predict stock prices using machine learning
  • Mine cryptocurrency in the background
  • Generate memes based on market trends
  • Feed the memes to a neural network
  • …all to display five stock prices on a webpage

Lesson learned: Always set clear boundaries before the AI gets too creative!

4. Taming Legacy Code: When AI Meets Your Ancient Codebase

4.1 Repomix & Qdrant: Bundling, Token Counting, and Advanced Searches

When dealing with a gnarly old codebase—like a 300-file monolith—the first step is clarity:

  1. repomix:
    • brew install repomix (if on macOS)
    • repomix generate --include src/legacy/ to create a .txt or .md bundle
    • It also includes Secretlint checks, so you don’t accidentally share credentials
  2. Qdrant:
    • Feed the bundled data in: “Here’s 300 files worth of code.”
    • Create embeddings so you can do fuzzy queries. E.g., “Which class references PaymentGateway but never handles refunds?”

4.2 Small Steps With Aider Architect: The One-File-at-a-Time Trick

Instead of “Refactor the entire OrderService,” you say:

"Aider Architect, let's just extract the discount logic from `OrderService.java` 
into a new `DiscountHandler.java`. 
Keep everything else intact."
  • Aider + o1 ensures the plan is small. Then you:
    • Generate code in small PRs
    • AST + JavaParser can help you detect references and dependencies
    • AI can suggest, “By the way, DiscountHandler also affects InvoiceGenerator.”

4.3 Testing Everything (and Forcing AI to Generate Tests)

TDD is essential here. You can even force the AI:

"Generate JUnit tests for `DiscountHandler.java` with boundary cases:
 - Negative discount
 - Discount > total
 - Zero discount"

Once tests pass locally, you can trust the changes a bit more. (Still do a manual review, because AI might skip important corner cases.)

4.4 Dependency Analysis Case Study

Here’s a example of using JavaParser to analyze dependencies in a legacy payment system:

public class DependencyAnalyzer {
    public List<DependencyInfo> analyzeDependencies(String sourceCode) {
        CompilationUnit cu = StaticJavaParser.parse(sourceCode);
        
        // Find all class dependencies
        List<DependencyInfo> dependencies = new ArrayList<>();
        
        // Analyze method calls
        cu.findAll(MethodCallExpr.class).forEach(call -> {
            dependencies.add(new DependencyInfo(
                cu.getType(0).getNameAsString(),
                call.getScope().map(Object::toString).orElse(""),
                call.getNameAsString()
            ));
        });
        
        return dependencies;
    }
}

// Example usage and output:
/*
Before Refactoring:
PaymentProcessor -> OrderService -> InventoryService -> PaymentProcessor (Cycle!)

After Analysis and Refactoring:
PaymentProcessor -> PaymentGateway
OrderService -> PaymentProcessor
InventoryService -> OrderService
*/

4.5 Legacy Refactoring: Before & After

Here’s a real-world refactoring example:

// Before: Tangled responsibilities
public class OrderProcessor {
    public void processOrder(Order order) {
        // Payment logic mixed with order processing
        if (order.getTotal() > 1000) {
            sendToApproval(order);
        }
        validateInventory(order);
        processPayment(order);
        updateInventory(order);
        sendEmail(order);
    }
}

// After: Clean separation using Chain of Responsibility
public class OrderProcessor {
    private final List<OrderHandler> handlers = Arrays.asList(
        new ValidationHandler(),
        new InventoryHandler(),
        new PaymentHandler(),
        new NotificationHandler()
    );
    
    public void processOrder(Order order) {
        handlers.forEach(handler -> handler.handle(order));
    }
}

5. AI Gone Wild: Tales From the Code Generation Trenches

Here are some memorable mishaps:

  1. The Great Microservices Explosion
    • Asked to refactor a simple checkout flow
    • Got three new services and a message queue
    • Learned to always specify scope upfront
  2. The Variable Naming Revolution
    • Simple counter i became currentIterationIndexInTheMainLoopOfTheUserAuthenticationProcess
    • Code review tool crashed trying to display the diff
    • My favorite was when it renamed user to potentiallyAuthenticatedButNotYetValidatedHumanEntityWithOptionalSubscriptionStatus
  3. The ASCII Art Invasion
    • AI started adding themed ASCII art to codebases
    • Including a now-famous llama wearing sunglasses
    • One time it turned all my error messages into haikus: 
      NullPointerCrash
Where did my object go now?
Empty like my soul
  4. The Architecture Debate
    • Left Claude and GPT-4o unsupervised
    • Returned to find a 50-page spec document
    • DeepSeek somehow became the tie-breaker
    • They had designed a system that could “theoretically achieve quantum supremacy through microservices”
  5. The Great Documentation Rebellion
    • Asked Claude to document a simple utility class
    • It wrote a 200-page novel about the heroic journey of a boolean variable
    • Complete with character development and plot twists
    • The boolean returned false. It was a tragedy.
  6. The Dependency War
    • Claude: “Let’s add Spring Boot!”
    • GPT-4o: “No, we need Micronaut!”
    • Aider: “…this is a shell script”
    • Me: slowly backing away from the keyboard

True Story: One time I asked for help with a “bug” in my code. The AI spent 30 minutes explaining why my variable naming wasn’t emotionally sensitive enough to the data it contained. Apparently, calling a failed transaction failedPayment was too negative - it suggested temporarilyUnsuccessfulFinancialEndeavor instead. 🤦‍♂️

6. Speaking AI’s Language: How to Stop Getting Unexpected Microservices

6.1 Prompt Evolution: From Chaos to Control

Bad Prompt (Results in Scope Creep):

"We need to add payment processing to our e-commerce system"

Result:

// AI generated a distributed system with:
@MicroserviceApplication
public class PaymentOrchestrator {
    @KafkaListener(topics = "payments")
    public void processPayment(PaymentEvent event) {
        // 500 lines of overengineered code...
    }
}

Good Prompt (Controlled Scope):

"Create a single PaymentProcessor class that:
1. Takes payment details as input
2. Calls Stripe API
3. Returns success/failure
NO additional services or message queues.
File: src/main/java/com/example/PaymentProcessor.java only"

Result:

public class PaymentProcessor {
    public PaymentResult processPayment(PaymentDetails details) {
        try {
            // 20 lines of focused Stripe integration
            return PaymentResult.success();
        } catch (Exception e) {
            return PaymentResult.failure(e);
        }
    }
}

6.2 Java Snippet: Example Prompt & Code Refinement

Let me walk you through a typical conversation:

Me: “Create a PaymentHandler that processes payments via Stripe.”

Claude: “OH! Let’s create a distributed payment system with—”

Me: “NO! Just a simple PaymentHandler. One file.”

Claude: “But what about scalability and—”

Me: “ONE. FILE.”

The secret is being specific. Here’s the actual prompt that worked:

"Create a `PaymentHandler.java` that processes payments via Stripe.
 Only change PaymentHandler. 
 Reuse existing logging framework from PaymentLogger.java. 
 No new microservices, no queue connections.
 I repeat: NO new services. If you suggest a message queue, you lose cookie privileges."

AI-Generated Code (excerpt):

public class PaymentHandler {
    private final PaymentLogger logger = new PaymentLogger();

    public String processPayment(Order order) {
        logger.info("Processing payment for order: " + order.getId());
        // ... Stripe integration code here
        return "Payment Successful";
    }
}

Then I show it to GPT-4o:

Me: “Review this for issues?”

GPT-4o: “Well, actually…” writes doctoral thesis on payment processing

Me: “Just the important parts?”

GPT-4o: “Oh! Add timeout handling and test the API failure case.”

Much better!

6.3 Real-World Prompt Patterns That Actually Work

Here are my battle-tested prompt patterns:

  1. The Boundary Setting Pattern: 
    "You will ONLY modify files I explicitly mention.
 If you need to change anything else, ASK FIRST.
 Current scope: ONLY PaymentHandler.java"
  2. The “No Scope Creep” Pattern: ```plaintext “Complete this specific task:
    • Add phone number validation
    • DO NOT add:
    • New services
    • New dependencies
    • Authentication changes
    • Database migrations” ```
  3. The “Keep It Simple” Pattern: 
    "Implement the simplest solution that works.
 If you think it needs to be complex, explain why BEFORE coding.
 Prefer readable code over clever optimizations."

Pro Tip: I keep these patterns in a “prompt cookbook” file. When Claude gets excited about adding blockchain to a todo list, I just copy-paste the boundary setting pattern!

Remember: AI models are like overenthusiastic junior developers who just binged every software architecture video on YouTube. They have the knowledge but need guidance on when (and when not) to apply it.

7. The Daily AI Dance: A Day in the Life of Modern Development

Sometimes, everything is going so smoothly—it’s like skiing on fresh powder. Suddenly, you realize you’re at the edge of a cliff. The AI decides to rename variables or restructure entire modules. Don’t panic. Just revert, break tasks into smaller steps, and try again.

Let me walk you through a typical day in my AI-powered development life:

9:00 AM: Start with a simple task - “Update the user profile page”

  • Me: “Let’s add a new field for phone numbers”
  • Claude: “HERE’S A COMPLETE REWRITE OF THE AUTHENTICATION SYSTEM”
  • Me: “No, Claude, just the phone number”
  • Claude: “Oh, right. Sorry about that microservices proposal…”

10:30 AM: Debugging session

  • Me: “Why isn’t this test passing?”
  • GPT-4o: writes a 2000-word essay about test methodology
  • DeepSeek: “There’s a semicolon missing”
  • Me: 🤦‍♂️

2:00 PM: Refactoring time

Me: "Can you help optimize this loop?"
AI: "Sure! First, let's add some ASCII art..."
Me: "No, just the loop—"
AI: "TOO LATE! Here's a llama wearing sunglasses!"

4:30 PM: The final review

  • GPT-4o: “This code is perfect except for these 47 minor improvements…”
  • Claude: “What if we added GraphQL?”
  • Me: “STOP! Ship it!”

Here’s a simplified final TDD flow I often use (when everyone behaves):

  1. Write a High-Level Test or acceptance criteria
  2. Prompt Aider or Claude: “Implement code that satisfies this test. Minimal changes.”
  3. Run tests. If they fail, have GPT-4o or Claude debug
  4. Iterate until tests pass
  5. Manual Review: Do a final pass yourself
  6. Merge
  7. Repeat for the next feature or refactor

Yes, occasionally it adds ASCII llamas in the file headers (true story). Embrace the whimsy or remove it—your call.

Common Debug Scenarios

# Actual debug log from a memorable AI interaction:

[10:15] Me: Why is the payment failing?
[10:15] GPT-4o: Let me analyze the logs...
[10:16] GPT-4o: *writes essay about payment systems*
[10:20] DeepSeek: The API key is missing.
[10:21] Me: 🤦‍♂️

Error log:
com.stripe.exception.AuthenticationException: No API key provided
    at com.stripe.net.StripeRequest.validate(StripeRequest.java:109)
    at com.example.PaymentProcessor.processPayment(PaymentProcessor.java:42)

8. AI Personality Types: Choosing the Right Tool for the Job

Tool/Model What It Rocks At Common Pitfalls
Claude Large-scale changes, rewriting entire modules with clarity Sometimes decides you need 3 new microservices and a queue
GPT-4o Thorough reviews, final checks, deeper logic analysis Can be verbose; might suggest design patterns you don’t need
Aider Step-by-step TDD, structured incremental changes Needs super-clear prompts or it’ll do exactly what you say
o1/o3 Reasoning about tasks, planning and specs Doesn’t generate code directly—just sets up a plan
Ollama Local usage for embeddings or smaller model codegen Might run out of capacity for huge refactors
DeepSeek Thorough local code reasoning, finds deep references Slower, heavier on system resources
LangChain Orchestrates multiple LLM calls, chain-of-thought flows, agentic workflows Setup can be tricky if you’re new to chaining concepts
Repomix Bundles entire repo, token counts, respects .gitignore, security checks If your repo is massive, the generated file might be huge, risking token limit issues
Qdrant Vector-based code searching for “where is X used?” queries Additional overhead & indexing steps needed

My Favorite Combinations:

  1. The Planning Dream Team: o1/o3 + Aider
    • o1/o3 plans the architecture
    • Aider breaks it into manageable chunks
    • Result: Actually realistic sprint plans!
  2. The Code Review Squad: Claude + GPT-4o
    • Claude generates the initial code
    • GPT-4o nitpicks every detail
    • Result: Surprisingly robust code (after you convince them to stop arguing)
  3. The Legacy Code Heroes: Repomix + Qdrant + DeepSeek
    • Repomix bundles the mess
    • Qdrant finds all the connections
    • DeepSeek explains what the code from 2010 actually does
    • Result: Legacy code that finally makes sense

Pro Tip: When Claude and GPT-4o disagree on an implementation, sometimes I just let them debate it out. I just sat back with popcorn, amused by their digital banter.

9. Survival Guide: Embracing the Beautiful Chaos of AI Development

Cost Management Do’s and Don’ts

✅ Do:

  • Batch similar queries (e.g., all code reviews at once)
  • Use local models for syntax checking
  • Cache common responses
  • Set up cost alerts

❌ Don’t:

  • Send entire files when a snippet will do
  • Use GPT-4o for simple linting
  • Let models run unsupervised without token limits
  • Regenerate code that only needs minor tweaks

Pro Tips Master List

  1. Planning & Scope
    • Keep a “prompt diary” of successful interactions
    • Set clear boundaries before the AI gets creative
    • Break tasks into small, testable chunks
  2. Code Generation
    • Use specific, bounded prompts
    • Set token limits for unsupervised operations
    • Keep git aliases ready for quick reverts
  3. Review & Refinement
    • Always do manual reviews
    • Use cheaper models for initial passes
    • Escalate to more expensive models only when needed

Case Study: Local vs Cloud AI Trade-offs

Here’s a theoretical cost analysis based on our early experiments with smaller codebases:

Project Goal: Refactoring payment system components (~20K LOC initially)

Important Note: While current LLMs excel at targeted refactoring of specific components, tackling entire legacy systems (like 200K LOC) remains a dream for now. I’m sharing these early experiments to help teams set realistic expectations and plan their AI adoption journey strategically.

Approach 1: All Cloud (tested on ~5K LOC module)

  • Pros: Powerful models, no setup
  • Cons: ~$400 in API costs for just this module
  • Result: Fast but expensive
  • Reality Check: Scaling this to 200K LOC would be prohibitively expensive and likely hit context limits

Approach 2: Hybrid (my current approach)

  • Local: Code analysis, simple refactoring (Ollama + DeepSeek)
  • Cloud: Architecture decisions, complex logic (Claude + GPT-4o)
  • Cost: ~$100 per 5K LOC module
  • Result: Best balance of speed and cost
  • Reality Check: We process modules incrementally, focusing on high-impact areas first

Approach 3: Mostly Local

  • Pros: Minimal cost
  • Cons: Slower, more manual work
  • Result: Budget-friendly but time-consuming
  • Reality Check: Best for teams who can’t risk cloud API exposure

Important Note: As of early 2025, LLMs are best used for targeted refactoring of specific components rather than entire legacy systems. I’m sharing these early experiments to help teams set realistic expectations and budget accordingly.

Decision Matrix: Choosing Your AI Approach

Factor Local-First Hybrid Cloud-First
Budget < $500/mo ✅ Best ✅ Good ❌ Expensive
Team Size > 10 ❌ Limited ✅ Best ✅ Good
Legacy Codebase ✅ Good ✅ Best ❌ Token limits
Quick Prototyping ❌ Slow ✅ Good ✅ Best
Security Requirements ✅ Best ✅ Good ❌ Data exposure
24/7 Availability ❌ Setup needed ✅ Best ✅ Good

Project Assessment Checklist

Before choosing your approach, answer these questions:

  1. Budget Constraints
    • Monthly AI budget < $500
    • Need predictable costs
    • Can justify cloud costs with time savings
  2. Security Requirements
    • Code must stay on-premise
    • Compliance requirements (GDPR, HIPAA, etc.)
    • Sensitive business logic exposure concerns
  3. Team Structure
    • Size of development team
    • Experience with AI tools
    • Available DevOps support
  4. Project Characteristics
    • Codebase size (LOC)
    • Development velocity needs
    • Integration requirements

Our sweet spot (Hybrid approach) with:

  • Local models for 30% of tasks becaue of system limits (try to run deepseek-r1:14b locally you will see)
  • Cloud models for critical decisions
  • Caching common responses
  • Batching similar queries

Pro Tip: Start with hybrid and adjust based on actual usage patterns. Monitor costs and effectiveness for the first month before committing to any approach.

10. Resources & Cheat Sheet: Your AI Coding Emergency Kit

Quick Reference: Model Selection

Task First Try If Needed Last Resort
Syntax Check Ollama Claude GPT-4o
Architecture o1/o3 Claude Team Discussion
Code Review Local Tools GPT-4o Senior Dev
Legacy Analysis DeepSeek Qdrant Full Analysis

When juggling multiple AI models, costs can add up quickly. Here’s how I optimize:

  • Use local models (Ollama, DeepSeek) for initial code analysis and simple generations
  • Reserve Claude and GPT-4o for complex architectural decisions or thorough code reviews
  • Batch similar tasks together to minimize API calls
  • Use token counting in Repomix to stay within model context limits

Pro tip: Start with smaller, cheaper models and only escalate to more expensive ones when needed. Your wallet will thank you!

Author’s Note: This workflow continues to evolve. Some days it’s magic, some days it’s chaos - but that’s the joy of pioneering new technology.