AI code detector
Coding assistants like GitHub Copilot, ChatGPT, and Claude generate billions of lines of source code every week. This tool evaluates code snippets to determine whether they bear the marks of generative programming models.
From textbook-standard algorithmic structures to boilerplate docstring conventions and predictable variable naming habits, AI-written code exhibits measurable patterns distinct from seasoned human engineers.
Drag a file here, or
JPEG, PNG, WebP, AVIF, PDF or .txt · up to 10 MB · you can also paste a screenshot
By uploading you agree to our Terms and Privacy Policy. Your file, IP address, approximate location and device details are stored.
What this checks
- Detection of formulaic docstrings and LLM-style explanatory code comments
- Generic variable naming patterns and canonical textbook implementations
- Over-defensive boilerplate error handling typical of language models
- Syntactic uniformity and absence of idiosyncratic developer shortcuts
- Support for Python, JavaScript, TypeScript, Go, Java, and C++
Frequently asked questions
Can AI-generated code be detected reliably?
Yes for standard boilerplate and typical algorithmic tasks, where LLMs reuse canonical patterns. Highly custom business logic or code heavily edited by humans will naturally lower confidence.
Is it wrong for developers to use AI code generators?
AI code assistants are widely accepted productivity tools in modern software engineering. This tool is primarily used for educational integrity, technical interview screening, and code provenance tracking.