- How accurate is this AI code detector?
- The detector uses 5 independent heuristic signals based on peer-reviewed research analyzing 100,000+ code samples. It identifies statistical patterns common to AI-generated code including low complexity, minimal error handling, generic naming, and uniform style. Like all AI detection tools, it provides probability scores rather than absolute answers. Accuracy is highest on 20+ line code samples with meaningful logic.
- Can it detect GitHub Copilot, ChatGPT, and Claude code?
- Yes. The detector identifies patterns common to all major AI coding tools including GitHub Copilot, ChatGPT (GPT-4, GPT-4o), Claude (3.5 Sonnet, Opus), Gemini Code Assist, Amazon CodeWhisperer, Cursor, Tabnine, and Cody. All LLM-based code generators share similar statistical patterns — low branching complexity, generic naming, minimal defensive coding — that the multi-signal analysis detects.
- Which programming languages are supported?
- The detector auto-detects and supports 12+ languages: JavaScript, TypeScript, Python, Java, C/C++, Go, Rust, C#, PHP, Ruby, Swift, and Kotlin. Language detection happens automatically from syntax patterns. Each language's analysis adapts to its specific conventions for functions, error handling, and style norms.
- Is my code sent to a server?
- No. All analysis runs 100% in your browser using pure JavaScript. Your code never leaves your device — there is no server-side processing, no API calls, and no data storage. This makes it safe for analyzing proprietary code, internal codebases, and sensitive projects.
- What are the 5 detection signals?
- The 5 signals are: (1) Complexity Analysis (25% weight) — cyclomatic complexity, nesting depth, function size, guard clauses. (2) Error Handling (20% weight) — null checks, try-catch, input validation, boundary conditions. (3) Comment Patterns (15% weight) — comment density, generic vs specific comments, AI documentation style. (4) Naming & Vocabulary (20% weight) — generic names, identifier diversity, convention consistency. (5) Style Uniformity (20% weight) — line length variation, indentation consistency, blank line regularity.
- Why does AI code have lower complexity?
- AI code generators predict the most probable next token, which favors simple, common patterns. Research shows human code averages 3.97 cyclomatic complexity while AI averages 1.8-2.5. AI avoids deep nesting, complex branching logic, and edge case handling because these patterns are less statistically common in training data. This makes AI code appear 'clean' but often less robust.
- Can clean human code trigger false positives?
- Yes. Developers who follow strict clean-code practices — short functions, consistent naming, thorough formatting with tools like Prettier or Black — may produce code that resembles AI patterns. Similarly, auto-formatted code (gofmt, rustfmt) and boilerplate/config files may score higher. The tool is most accurate on logic-heavy code with meaningful branching and error handling.
- Can I use this for hiring and technical interviews?
- Yes. Technical interviewers and hiring managers can use this to screen coding assignments and take-home challenges for potential AI generation. However, we recommend using results as one data point alongside live coding discussions, not as definitive proof. Some candidates may legitimately write clean, well-structured code that resembles AI patterns.
- Does it work on code snippets or only full files?
- It works on both, but accuracy improves with more code. Short snippets (under 10 lines) may not provide enough patterns for reliable analysis. For best results, paste at least 20-30 lines of meaningful code with logic, conditionals, and error handling. Config files and import blocks are less informative.
- How is this different from other AI code detectors?
- Most AI code detectors require uploading your code to their servers and use proprietary ML models. This tool uses pure heuristic analysis running 100% in your browser. Your code is never uploaded. It's completely free, unlimited, and private. The trade-off is that ML-based tools trained on specific AI model outputs may catch model-specific patterns, but this tool detects fundamental structural differences that apply across all AI generators.
- What does cyclomatic complexity mean in code detection?
- Cyclomatic complexity counts the number of independent execution paths through code — each if, for, while, switch, and logical operator adds a path. Human code averages 3.97 complexity per function (more decision points, edge cases, branching logic). AI code averages 1.8-2.5 (simpler, flatter logic). This difference reflects that humans handle real-world complexity while AI generates the 'most probable' simple solution.
- Is this tool free to use?
- Yes. The AI code detector is completely free with no usage limits, no signup required, and no watermarks. Since all processing happens locally in your browser, there are no server costs. It will remain free.
- Can students use AI code detectors to check before submitting?
- Yes. Students can paste their code into this detector before submitting assignments to check if their writing patterns resemble AI-generated code. If your code scores high for AI patterns, consider adding more comments explaining your thought process, using domain-specific variable names instead of generic ones like 'result' or 'data', and adding error handling. This self-check helps ensure your authentic work doesn't get flagged by institutional AI detection tools.
- Does the detector work on React, Next.js, and frontend components?
- Yes. The detector supports JavaScript and TypeScript and works well on React components, Next.js pages, Vue components, and other frontend code. It analyzes the same 5 signals regardless of framework. Note that large component files with many similar handler functions may show some AI-like patterns (uniform structure) even when human-written, because component architecture naturally creates repetitive patterns.
