AI Code Detector
Analyse source code for the writing patterns that separate AI-generated code from hand-written code — comment style, naming consistency, placeholder scaffolding, debug leftovers — with every finding traced to a line number. Runs entirely in your browser.
Why Use AI Code Detector?
Most tools ranking for "AI code detector" hand you a single number — "87% AI-generated" — with no explanation of where it came from, and accuracy claims north of 99% that nobody can substantiate. That combination is worse than useless when the stakes are real, because a confident number with no reasoning behind it is exactly what gets an innocent student accused. This tool inverts that. Every point of the score traces to a named signal, every signal shows the lines that triggered it, and the honest caveats sit on the results page rather than buried in a footer. When there isn't enough code to say anything, it says so instead of guessing. It supports ten languages, parses strings and comments properly so a URL in a string doesn't get counted as a comment, and runs entirely in your browser — your proprietary code, your students' submissions, and your candidates' take-homes are never uploaded anywhere.
How to Use AI Code Detector
- Paste your code into the box, drop a file onto it, or click Upload. Below 15 lines of code the tool returns "Not enough code to judge" rather than guessing; 40 lines or more is where results become worth acting on.
- Leave the language on Auto-detect, or pick it manually if the guess is wrong. Language affects how comments and strings are parsed, so it matters for accuracy.
- Click Analyse Code. Everything runs locally in your browser — nothing is uploaded.
- Read the two signal columns, not just the score. The left column lists AI-style patterns found; the right lists human-style ones. The headline number is only a summary of those.
- Click any signal to expand it and see the exact lines that triggered it. This is the part that actually tells you something — verify each one against the code yourself.
- Check the confidence label. "Low confidence" or "Not enough code to judge" means the result is inconclusive, not that the code is human-written.
- Load the two built-in samples (AI-written Python, human-written JavaScript) to see what strong signals in each direction look like before you trust the tool on your own code.
Worked Examples
Assistant-generated Python utility module (the built-in sample)
A 91-line Python module with type hints throughout, a full docstring on every function, and a comment above nearly every statement. Load it with the "AI-written Python" button.
Strong AI indicators — score 96, moderate confidence. Three signals: very high comment density (52% of content lines), comments restate the code (7 instances), every function documented (4 of 4). Expanding the second signal cites the comment "Initialize the results list" sitting directly above `results = []`.
Confidence caps at moderate rather than high because consistency signals like these are also produced by strict linting and formatters.
A real migration script written under deadline (the built-in sample)
A 65-line Node.js script with mixed quote styles, a commented-out line kept for reference, and TODO notes naming a colleague. Load it with the "Human-written JavaScript" button.
Strong human indicators — score 1, high confidence. Four signals: debug statements left in (a stray `console.log("here")`), maintenance notes left in place (a FIXME about row paging that names a colleague), casual lowercase comments, and mixed naming conventions (`userId` and `user_id` in the same file).The FIXME is the strongest single signal here. Assistants rarely emit maintenance notes unprompted, and when they do the text is generic rather than naming a person and a specific unresolved problem.
Clean, heavily-linted human TypeScript — the hard case
A 60-line React hook written by a developer, formatted by Prettier, passing a strict ESLint config, with no comments at all.
Mixed signals — score 50, low confidence. No signals fired in either direction. Note shown: "Very few signals fired either way. Treat this as inconclusive rather than as evidence of anything."
This is the result that matters most. A tool that confidently labelled this file AI-generated because it looks tidy would be exactly the failure mode that gets people falsely accused.
About AI Code Detector
An AI code detector analyses source code for the stylistic fingerprints that tend to separate machine-generated code from code a person typed. It is not a plagiarism checker and it does not look the code up in a database — it measures how the code is written. The signals are real and measurable. Coding assistants comment far more heavily than working developers, and they comment differently: they narrate what the next line does ("# Initialize the results list" above `results = []`) where an experienced developer comments why a decision was made. They document every function in a uniform format. They wrap everything in defensive try/except blocks with interchangeable generic messages. They leave template placeholders like `YOUR_API_KEY_HERE` and `// ... rest of the implementation`. They decorate log output with emoji. And crucially, they lack the debris of real work: no commented-out code kept "just in case", no `console.log("here")` somebody forgot to delete, no `FIXME: ask Priya about paging`, no variable called `tmp2`, no mixed tabs and spaces from a bad merge. This tool checks seventeen such signals in both directions and shows you the line number behind every one. Now the part most tools in this category won't tell you: none of this proves authorship, and it cannot. Style is not identity. A developer who runs Prettier, Black, or gofmt on save produces the same mechanical consistency an assistant does. A developer who was taught to document everything looks generated. Conversely, generated code that someone reviewed, renamed, restructured, and debugged reads as human — because at that point it substantially is. There is no watermark in ordinary source code, and short of one, detection is inference from style rather than measurement of origin. The industry has already learned this the hard way with prose: OpenAI withdrew its own AI Text Classifier in July 2023, citing its low rate of accuracy. Code is a harder case than prose, not an easier one, because code has far less stylistic room to move — idiomatic solutions converge, two competent developers writing the same binary search produce nearly the same function, and formatters erase what little variation remains. Treat any AI code detector, this one included, as a way to find things worth asking about — never as evidence.
Troubleshooting & Common Issues
It says "Not enough code to judge"
The sample has fewer than 15 lines of actual code. This is deliberate. Short snippets genuinely carry no stylistic signal — two developers writing the same ten-line function produce nearly identical code, so any verdict would be noise dressed up as a result. Paste 40+ lines, or the whole file, for a read worth acting on.
My own hand-written code came back as AI-generated
The most common cause is a formatter and a documentation habit. Prettier, Black, and gofmt produce the mechanical consistency the tool reads as generated, and if you document every function in a uniform style you'll trip that signal too. Open each fired signal and check the cited lines — if they're all consistency signals rather than content signals (placeholders, tautological comments, emoji), the result is a false positive and the tool's own note will say so.
Code I know was AI-generated came back as human
Expected, and not a bug. If you or anyone else reviewed the output, renamed variables, deleted the explanatory comments, and debugged it in place, the stylistic traces are gone — the tool measures style, and the style is now yours. Detection also fails when the assistant was told to write terse, uncommented code in the first place.
The detected language is wrong
Auto-detection scores the file against per-language patterns and can miss on short files or unusual dialects. Pick the language manually from the dropdown and re-run. Language selection changes how comments and string literals are parsed, so a wrong guess can distort the comment-density signal significantly.
My language isn't in the list
Ten languages are supported: JavaScript, TypeScript, Python, Java, C#, C/C++, Go, Ruby, PHP, and Rust. For anything else, pick the closest comment syntax — a C-family language for `//` and `/* */`, or Python for `#` — and the structural signals will still work, though language-specific function detection will be less reliable.
I need to check a whole repository, not one file
This tool analyses one file at a time by design, because style varies enormously between files in a real repo and an averaged repo-wide score would hide exactly the signal you want. Run the files you're actually curious about individually and compare, which is also more defensible if you ever have to explain the result to someone.
The score changed after I reformatted the code
That is the honest behaviour of any style-based detector, and it's the clearest demonstration of why these scores aren't proof. Running a formatter changes indentation consistency, line-length variance, and quote consistency — several of the seventeen signals directly. If a formatter can move the number, the number was never measuring authorship.
Frequently Asked Questions
Is there a way to detect AI in coding?
You can detect stylistic patterns that correlate with AI generation — heavy narrating comments, uniform documentation, template placeholders, boilerplate error handling, and the absence of human debris like TODO notes and commented-out code. That's what this tool measures. What you cannot do is detect AI authorship with certainty. Source code carries no watermark, and the same patterns appear in carefully-formatted human code. Detection here means informed inference from style, not verification of origin.
How accurate are AI code detectors?
Far less accurate than their marketing claims. Any tool advertising 99%+ accuracy on code is describing performance on a curated test set of unmodified assistant output against unformatted human code — the easy case. Accuracy collapses in the two situations that matter in practice: AI code that a human then edited, and clean human code that a formatter and linter have normalised. Code is harder than prose because idiomatic solutions converge, leaving little stylistic room to measure. Treat any single score as a prompt to look closer, never as a finding.
Can teachers tell if code is AI-generated?
Not reliably, and not from a detector score alone. A tool like this can flag that a submission has no debugging traces, comments every line in a tutorial voice, and contains `YOUR_API_KEY_HERE` — all worth asking about. But it cannot distinguish that from a diligent student who comments thoroughly, and it will clear any student who edited the output. The defensible approach used by most CS departments is process evidence rather than output analysis: commit history, in-person walkthroughs where the student explains their own code, and small oral checks. Use a detector to decide what to ask about, then ask.
Is this AI code detector free?
Yes — completely free, no account, no email, no credit card, and no usage cap. There's no paid tier, because there's no server cost to cover: the analysis runs as JavaScript in your browser, so the tool costs the same whether you check one file or a thousand.
Does my code get uploaded to your servers?
No. The entire analysis — parsing, signal detection, scoring — happens locally in your browser. Nothing is transmitted, logged, or stored. You can verify this by opening your browser's network tab while running an analysis, or by disconnecting from the internet after the page loads and confirming the tool still works. This makes it safe for proprietary code, client work, student submissions, and hiring take-homes.
Which programming languages are supported?
JavaScript, TypeScript, Python, Java, C#, C/C++, Go, Ruby, PHP, and Rust. Language selection controls how comments and string literals are parsed — important, because a naive parser counts a `//` inside a URL string as a comment and skews the comment-density signal. Language is auto-detected by default and can be overridden if the guess is wrong.
Can AI-generated code be made undetectable?
Yes, trivially — which is the honest answer to why these tools have a ceiling. Deleting the explanatory comments removes the largest signal group. Renaming variables, running a formatter, and doing the ordinary work of reviewing and editing generated code removes most of the rest. This isn't a flaw to be patched in a future version; it follows from the fact that style is a choice rather than a fingerprint. Detectors work on unmodified output and stop working the moment someone edits it.
What's the difference between an AI code detector and a plagiarism checker?
A plagiarism checker compares your code against a corpus of existing code and reports matching passages — it answers "has this been seen before?" and can cite a source. An AI code detector compares your code against stylistic patterns and reports a likelihood — it answers "does this read like machine output?" and cannot cite anything but its own heuristics. Plagiarism results are evidence because they point at a specific prior work; AI-detection results are not, because they point only at a style. Institutions should treat the two very differently.
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