Deep dive into building Dependify - an AI-powered tool that automates code maintenance, dependency updates, and technical debt reduction using Groq inference and Modal containers.
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As developers, we all know the pain. You're excited to build that new feature, but first you need to update 47 outdated dependencies, refactor that legacy code from 2019, and somehow make sense of the uncommented functions your past self left behind.
The stats are brutal: 41% of developers spend most of their time dealing with technical debt, dedicating 16.4 hours per week to maintenance tasks. That's almost half your work week gone to debugging and refactoring instead of innovation.
This problem hit me hard during a particularly frustrating week where I spent more time updating dependencies than actually coding. That's when I decided to build Dependify - an AI-powered developer tool that automates the tedious parts of code maintenance.
The core challenge was processing large codebases efficiently while maintaining code quality. Here's how I solved it:
# Simplified version of our scanning logic
async def scan_codebase(repo_path):
files = identify_target_files(repo_path)
scan_results = []
for file_batch in chunk_files(files, batch_size=10):
# Parallel processing using Modal containers
results = await process_batch_with_groq(file_batch)
scan_results.extend(results)
return prioritize_improvements(scan_results)
The system uses Groq's lightning-fast inference to analyze code patterns, identify outdated dependencies, and spot refactoring opportunities. What makes this special is the parallel processing - instead of scanning files sequentially, we process multiple files simultaneously using Modal containers.
Not all technical debt is created equal. Dependify ranks improvements based on:
function calculatePriorityScore(improvement) {
const impactWeight = 0.4;
const riskWeight = 0.3;
const dependencyWeight = 0.3;
return (
improvement.impact * impactWeight +
(1 - improvement.risk) * riskWeight +
improvement.dependencyChainValue * dependencyWeight
);
}
This is where the magic happens. The system doesn't just identify problems - it fixes them:
The proof is in the production. We successfully improved Microsoft's Magma repository by auto-updating all major outdated dependencies, resulting in merged PR #63. This wasn't a toy project - it was production-ready code improvements for a significant open-source project.
Modal handles our compute-intensive workloads with elegant scaling. Each file gets processed in its own container, allowing us to handle massive codebases without breaking a sweat.
Traditional LLM APIs were too slow for real-time code analysis. Groq's specialized inference hardware gives us the speed we need for responsive code scanning.
Developers get live updates on their maintenance progress through a sleek dashboard built with Next.js, with real-time notifications powered by Supabase.
Developers won't wait 10 minutes for code analysis. We optimized for sub-minute scan times, even for large repositories.
Our AI doesn't just make changes - it explains them. Every modification comes with clear reasoning and diff previews.
The tool needed to fit seamlessly into existing workflows. GitHub integration was non-negotiable.
We're just getting started. The roadmap includes:
If you're building AI-powered developer tools:
The future of software development isn't about replacing developers - it's about freeing them from the tedious work so they can focus on what they do best: solving problems and building amazing products.
Want to follow the journey? Check out Dependify on GitHub and let me know what maintenance tasks you'd love to automate!