150k Lines of Vibe-Coded Elixir: The Good, The Bad, The Ugly

The concept of "vibe coding"—using AI to generate large portions of code based on high-level descriptions—has moved from theoretical discussion to practical application. One developer recently documented their journey of generating 150,000 lines of Elixir code through this method, creating a comprehensive real-world case study.

This massive undertaking wasn't just an academic exercise. The project aimed to build a substantial application while leveraging AI assistance at every step, testing the limits of current-generation coding tools. The results reveal a complex picture of modern software development that challenges traditional assumptions about code creation.

The experiment provides valuable insights into how AI-assisted development is reshaping the software industry, offering both remarkable efficiencies and unexpected complications that developers must navigate.

The most striking advantage of the 150,000-line Elixir project was the dramatic acceleration of development velocity. What traditionally requires months of careful coding and architectural planning emerged in a fraction of the time, allowing the developer to focus on higher-level design decisions rather than boilerplate implementation.

Code quality proved surprisingly robust. The AI-generated Elixir code demonstrated strong adherence to the language's functional programming paradigms, with proper pattern matching, immutability, and process supervision patterns emerging naturally from the generation process.

Key benefits observed included:

• Rapid prototyping of complex features
• Consistent implementation patterns across modules
• Automatic inclusion of error handling and edge cases
• Reduced cognitive load for repetitive coding tasks

The Elixir language itself proved remarkably well-suited for this approach. Its clear syntax and strong conventions meant the AI could generate code that felt idiomatic and maintainable, reducing the friction often encountered when generating code for more ambiguous or flexible languages.

Despite the impressive output, the project revealed significant technical debt accumulating beneath the surface. The sheer volume of generated code created a maintenance challenge that traditional review processes struggled to address effectively.

Architectural coherence emerged as a primary concern. While individual modules functioned correctly, the overall system architecture sometimes lacked the cohesive vision that human architects typically provide, leading to subtle inconsistencies in design patterns and data flow.

Specific challenges included:

• Difficulty tracing the origin of specific architectural decisions
• Inconsistent naming conventions across different modules
• Over-reliance on certain patterns without considering alternatives
• Limited documentation of the reasoning behind code structure

The review process itself became more complex. Instead of reading code line-by-line, developers needed to evaluate entire system behaviors and architectural patterns, requiring a different skill set and more time than traditional code review approaches.

The most sobering discovery was the debugging complexity that emerged when things went wrong. When the 150,000 lines of code encountered edge cases or unexpected conditions, the AI-generated solutions sometimes created cascading issues that were difficult to untangle.

Performance optimization presented another challenge. While the generated code was functionally correct, it often lacked the fine-tuned optimizations that experienced developers would naturally apply, leading to suboptimal resource usage in production environments.

Critical issues that emerged:

• Memory usage patterns that didn't scale efficiently
• Database query optimization opportunities missed
• Concurrency patterns that could be more efficient
• Error recovery mechanisms that were overly complex

The human oversight requirement became crystal clear. While AI could generate vast amounts of code, critical thinking about system design, performance, and long-term maintainability remained firmly in the human domain. The project demonstrated that AI assistance is a powerful tool, but not a replacement for engineering judgment.

The 150,000-line Elixir experiment offers valuable lessons for the broader software development community. It suggests that the future of coding isn't about AI replacing developers, but rather about developers learning to collaborate effectively with AI tools.

Successful AI-assisted development requires new skills. Developers must become adept at crafting precise prompts, evaluating generated code for quality and correctness, and understanding the limitations of current AI capabilities.

Key principles for effective collaboration:

• Start with clear, well-defined requirements
• Review generated code with the same rigor as human-written code
• Maintain architectural oversight throughout the process
• Invest in automated testing to catch issues early
• Document AI-human collaboration patterns for team knowledge

The experiment also highlights the importance of language choice in AI-assisted development. Elixir's strong typing, clear conventions, and functional paradigm made it particularly suitable for AI generation, suggesting that language design will play an increasingly important role in the AI coding ecosystem.

The 150,000-line Elixir project represents more than just an interesting technical experiment—it's a window into the future of software development. The results show that AI assistance can dramatically accelerate development while maintaining reasonable code quality, but only when paired with thoughtful human oversight.

For development teams considering similar approaches, the key takeaway is balance. AI tools offer tremendous potential for productivity gains, but they require new workflows, skills, and quality assurance processes to be truly effective.

As these tools continue to evolve, the most successful organizations will be those that learn to harness AI's capabilities while maintaining the critical thinking and architectural vision that remain uniquely human strengths. The future of coding isn't about choosing between human and machine—it's about finding the optimal collaboration between both.