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Clean Architecture for Solo Developers Using AI

A solo developer embarked on a project centered on web scraping, collaborating with the AI assistant Claude to craft a proxy management system. Initial success gave way to architectural challenges as complexities grew. Ultimately, adopting atomic design principles improved maintainability. The developer now promotes AI tools for cleaner, more efficient architecture.

A Solo Developer’s Journey with AI

As a developer with undergraduate-level programming experience and a decade’s experience teaching computer science and programming at GCSE and A Level, I had an ambitious idea for a project that involved web scraping. The concept was solid: develop a system to manage proxy servers for reliable data collection. However, the implementation requirements quickly exceeded my personal programming expertise. This is my story of addressing technical debt with AI assistance.

Enter Claude, Anthropic’s AI assistant. I chose to work with Claude specifically because Anthropic’s commitment to ethical AI development aligns with my own principles. More practically, Claude’s unique ability to handle complex software architecture and generate structured artifacts made it an ideal programming partner.

The collaboration started promisingly. I provided the vision and requirements, Claude helped with implementation details, and together we built a working proxy management system. Claude could generate code, tests, and documentation, while I reviewed, validated, and steered the development direction.

One particularly valuable feature was Claude’s ability to generate “artifacts” – structured blocks of code, documentation, or other content that could be directly used in the project. This capability proved essential for maintaining consistency and quality throughout the development process.

Where Things Started to Go Wrong

Despite this promising start, I made a crucial mistake. While I was directing the project and making decisions, I wasn’t fully embracing the role of software architect. Instead, I was falling into a pattern of:

  1. Identifying a feature/problem
  2. Asking Claude for a solution
  3. Implementing the solution
  4. Moving on to the next feature/problem

This approach worked fine initially. The code worked. The tests passed. We were shipping features and solving problems. But then came the bug that would change everything – a seemingly simple issue that would require 70+ updates to fix, teaching us valuable lessons about software architecture along the way.

The Initial Architecture

Our proxy manager started simple enough:

  • Manage a pool of proxies
  • Track their performance
  • Rotate through them based on health
  • Handle failures gracefully

The initial implementation appeared sound:

When Requirements Grew

Then came the requirement: “We need to track detailed proxy health metrics and adjust rotation based on performance.”

Simple enough, we thought. We added health tracking:

This is where things started to unravel. The seemingly innocent _calculate_health method needed to know about:

  • Success rates
  • Response times
  • Failure patterns
  • Historical performance
  • Timeout thresholds

While _update_rotation_order needed to understand:

  • Health scores
  • Proxy availability
  • Load balancing requirements
  • Priority rules
  • Failover strategies

Every component became entangled with every other component.

The Cascade of Changes

What followed was a nightmare of cascading changes. Here’s a real excerpt from our git log:

Each fix introduced new problems because our monolithic ProxyManager was violating the Single Responsibility Principle in multiple ways:

  • It was both calculating and consuming health scores
  • It was managing proxies while also monitoring them
  • It was handling both statistics and rotation logic
  • It was mixing validation with utilisation

The Moment of Clarity

After two weeks and 70+ updates, we had our epiphany. The problem wasn’t with any individual piece of code. The problem was architectural. Our ProxyManager was trying to be everything to everyone.

Looking at our CHANGELOG.md was enlightening:

We were treating symptoms, not the disease. The disease was our monolithic architecture.

The Solution: Atomic Design

The breakthrough came when we started thinking in terms of atomic components. Each piece should have one clear responsibility. This led to a clean, maintainable architecture:

Each component had:

  • A single responsibility
  • Clear interfaces
  • Independent testing
  • Focused documentation

Key Lessons

  1. Signs of Architectural Problems
  • Methods requiring knowledge of other methods’ internals
  • Changes having unpredictable ripple effects
  • Growing number of “temporary” fixes
  • Increasing time spent on regression testing
  1. The Cost of Monolithic Design
  • Quick fixes compound exponentially
  • Interconnected components create cascading failures
  • Testing becomes increasingly complex
  • Developer confidence decreases with each change
  1. The Value of Atomic Design
  • Single Responsibility Principle (SRP) prevents entanglement
  • Clear boundaries contain changes
  • Independent components enable confident modification
  • Testing becomes straightforward and reliable

The Path Forward

This experience led us to create the AI Architect Kit, a template and framework for solo developers, leveraging AI assistants when building maintainable systems from the start. It embodies everything we learned about:

  • Clean architecture
  • Component separation
  • Testing strategies
  • Documentation standards

The time you invest in proper architecture pays back manifold in maintenance and development speed.

Next in the Series

In the next post, we’ll explore the principles we established to prevent these issues, including:

  • Defining component boundaries
  • Establishing testing practices
  • Creating documentation standards
  • Implementing version control practices

This is part 1 of a 5-part series on clean architecture in AI-assisted development.

 


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Ruari Mears
Ruari Mears

Hey there! I'm Ruari, creator of 'soneaca', AKA 'Society Neurodiversity & Cats' – where personal experience meets social analysis, transformation meets understanding, and thoughts on social justice are often served with a side of seriously spicy curry.

Drawing from my journey through trauma, recovery, and personal growth, I explore the intersections of neurodiversity, class, politics, identity, food, and ethics. As an educator, former actor, computer scientist, biker and passionate vegetarian cook, I bring diverse insights to my writing. Living in a beautifully neurodiverse household, I share authentic perspectives on everything from ADHD and autism to gender identity, mixed-race relationships, and social justice. I'll also share my culinary adventures – from growing extreme chillies to crafting vegan versions of Indian and Italian classics that challenge the notion that ethical eating means compromising on flavour.

My writing examines how personal experiences connect to broader social issues, from economic inequality to systemic oppression, while exploring how our food choices reflect our values and connection to other sentient beings. I believe in the power of open dialogue and accessible knowledge – which is why all content will always be freely available to everyone, regardless of financial situation.

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