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Research on the Frontier of AI-Generated Code

We study how AI writes code, where it fails, and how teams verify it before production. Data-driven findings, published openly.

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Latest Research

From the Lab

Field notes, experiments, and hard data on AI code reliability.

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AI code research cover
Featured

Why 43% of AI-Generated Code Fails in Production — And What We Found

A year of analyzing over 10,000 AI-generated pull requests revealed seven failure patterns that human reviewers consistently miss. Here is the full breakdown from the OpeClaud Research Lab.

OpeClaud Research Lab 8 min read
Seven AI bug patterns
Code Review

Top 7 AI Code Bugs That Pass Code Review

AI assistants write code fast — but they repeat the same seven mistakes that reviewers miss.

R. Prasanga6 min read
Cursor and Copilot code
AI Assistants

Why Cursor and Copilot Code Fails Under Real Traffic

It compiles, it passes tests, and it still breaks in production. Here's the pattern behind it.

S. Mitchell5 min read
Verification pipeline
Verification

A Practical Pipeline for AI Code Verification

A step-by-step system for scanning AI code before it reaches your main branch.

O. Research Lab8 min read
Safety checklist
Safety

AI Coding Assistants: A Safety Checklist for Production

Ten checks to run before merging anything an AI wrote. Print this and pin it to your PR template.

R. Prasanga6 min read
Debugging AI code
Debugging

How to Debug AI-Generated Code Without Losing Your Mind

AI bugs don't look like human bugs. A practical playbook for isolating and fixing them.

S. Mitchell7 min read
The future of AI code
Outlook

The Next Decade of AI-Assisted Development

From code generation to code verification — where the industry is heading in the next ten years.

O. Research Lab9 min read