scriptiii
100% Client-Side

Rules, not a model

AI text humanizer

Take the machine out of the wording: plain words instead of inflated ones, no filler openers, one hedge instead of two, and sentences that vary in length.

Every rule runs in this tab. Your text is never uploaded, never stored, and no model is called.

What it found

TellCountExamples

Short answer: paste the text and press rewrite. The tool swaps inflated vocabulary for plain words, deletes filler openers, unstack hedges, thins em dashes and splits sentences longer than 30 words. It shows which rules fired, and the before/after numbers, so you can judge the result instead of trusting a score.

What a rule-based rewrite can and cannot do

It cannot invent a better sentence. It can remove the parts of a sentence that sound like a machine wrote them — and in most AI text those parts are the vocabulary and the rhythm, not the argument. The argument survives; the padding does not.

That is also the limit: if the text needs a real restructure, this tool will not do it, and a language model would have to. The page is built so that the honest option stays available locally, instead of sending your document to a server for a rewrite.

Read the numbers, not a grade

Machine-written English tends to have uniform sentence lengths and a high rate of a small set of words. Burstiness — the standard deviation of sentence length — is the clearest of those signals: human writing mixes a four-word sentence with a twenty-eight-word one. The tool reports it before and after but promises nothing: splitting one very long sentence can lower the number while making the paragraph read better, and a measurement is not a score.

Humanizer questions

Is this the same as asking an AI to paraphrase?

No, and the difference matters. A model rewrites sentences it has never seen; this tool applies a fixed list of substitutions and edits — inflated words become plain ones, openers go, hedges unstack, long sentences split. It cannot reword a thought, so the meaning is preserved exactly, at the cost of being less transformative than a paraphrase.

Will it get past AI detectors?

Nothing can promise that, and this page will not pretend to. Detectors guess from statistics like sentence-length variance and word choice; the tool raises burstiness and removes the phrases those guesses lean on, but a detector is a guess either way. Treat the before and after numbers as what they are: measurements, not a verdict.

What counts as a tell?

Inflated verbs (utilize, leverage, facilitate), vague importance (crucial, pivotal, comprehensive), stock phrases (in today’s fast-paced world, a plethora of, it is worth noting that), stacked hedges (may potentially), the “not only X but also Y” construction, filler openers (Moreover, Furthermore), and em dashes used as a rhythm crutch.

Why does it show numbers instead of a score?

Because a single “human score” would be invented. The tool reports what it can measure — word count, sentences, average length, burstiness (the standard deviation of sentence length) and em dashes — before and after, plus a list of exactly which rules fired and how often.

Where does my text go?

Nowhere. Every rule runs in this tab on the string in the textarea; there is no request, no model and no storage. That is the reason the tool exists as rules rather than as a black box with an API key.

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