AI Code Detector & Copilot Analyzer
Scan JavaScript, TypeScript, Python, and backend code for signatures of ChatGPT, GitHub Copilot, and Claude generation: trivial comment density, textbook boilerplate, and syntax uniformity.
AI Markers Detected
- •6 trivial restatement comments typical of instruction-tuned LLMs
- •Standard LLM tutorial/example usage boilerplate block detected
Specific Line Flags
// Function to calculate fibonacci numbers
// Returns the nth fibonacci number
// Base case: if n is 0 or 1, return n
// Initialize variables for iteration
// Loop through numbers from 2 to n
// Return the calculated result
// Example usage:
How AI Code Detection Works
Large Language Models like ChatGPT (GPT-4o), Claude 3.5 Sonnet, and GitHub Copilot write code differently than production software engineers. While AI-generated code is syntactically clean, it consistently exhibits stylometric signatures: redundant explanatory comments, textbook docstrings on trivial functions, generic error suppression (catch (e) { console.error(e) }), and template example usage blocks.
1. Comment Redundancy
Instruction-tuned models are trained to explain each step. They frequently add comments like // Loop through items immediately above a for loop, which human developers rarely write.
2. Pragmatic Shortcuts
Human code contains real-world engineering artifacts: ticket references (TODO: #312), telemetry loggers, degradation fallbacks, and project-specific acronyms that LLMs cannot guess.
3. Zero Data Retention
All analysis runs directly in your browser JavaScript engine. Your proprietary source code, credentials, and business logic are never transmitted to any external API or backend server.