Anti-Grammarly AI tool adds typos on purpose to make AI-generated text appear more human

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A new AI tool called Sinceerly is flipping digital communication rules by intentionally adding typos and imperfections to emails. Created as satire by Harvard MBA student Dan Horwitz, the anti-Grammarly tool addresses a growing concern: flawless writing now signals AI use rather than professionalism, prompting users to simulate human authenticity through deliberate mistakes.

Anti-Grammarly Tool Emerges to Combat AI Detection

A new AI tool is challenging decades of digital communication norms by intentionally introducing errors into perfectly polished text. Sinceerly, created by Dan Horwitz, a Harvard MBA student, functions as an anti-Grammarly solution designed to make AI-generated text appear more human

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. The tool promises to eliminate telltale signs of AI composition by killing em-dashes, removing overused phrases like "not just," and strategically adding typos on purpose

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Source: Gizmodo

Source: Gizmodo

Sinceerly offers three distinct settings: "Subtle," "Human," and "CEO," each calibrated to inject different levels of imperfection into text

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. The CEO setting, notably, reflects a disdain for initial capitals and delivers professionally clipped prose, while the Human setting introduces more casual phrasing and deliberate misspellings like "sceret" instead of "secret"

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The Satire Behind Digital Communication's Latest Paradox

While Sinceerly's website presents the concept with deadpan seriousness, Horwitz confirmed the tool is fundamentally tongue-in-cheek, closer to satire than a genuine productivity solution

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. "The whole thing is meant to make people think twice and have a little fun," Horwitz explained

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. Yet the satire touches on a genuine shift in how flawless writing is perceived in the age of ChatGPT and other LLM platforms.

The irony runs deep: tools like Grammarly were built to eliminate errors and improve clarity, but now that same polish carries different implications

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. According to Fast Company, Horwitz is an investment partner at venture capital firm Dorm Room and comes from Harvard Business School

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. His creation highlights how AI slop—the generic, overly polished output from language models—has become instantly recognizable through AI writing tropes like repetitive phrase structures and synonym-heavy formulations

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Why Imperfection Signals Human Authenticity

This trend reflects a fundamental inversion in digital communication standards. For decades, clean grammar and structured writing served as markers of professionalism. Now that same perfection can feel artificial, lacking the human touch that distinguishes genuine correspondence from automated responses

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. Typos and informal writing increasingly function as signs of human authenticity, even status, while overly perfect emails may trigger suspicion

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Source: Fast Company

Source: Fast Company

As one writer with dyslexia noted, tiny errors have become "signatures of our humanity" that signal actual care rather than mindless reliance on an LLM

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. This shift matters because it changes what "good writing" even means

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. The scenario creates a strange dynamic where users simulate imperfection to maintain believability—essentially using AI to hide the fact that AI was used in the first place

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Trust and the Future of AI-Mediated Writing

The emergence of anti-Grammarly tools raises critical questions about trust in digital communication. If both polished and imperfect writing can be generated by AI, distinguishing between human and machine becomes increasingly difficult

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. Everyday users may find themselves adjusting tone—intentionally or not—to appear more genuine, incorporating shorter sentences, casual phrasing, or minor errors into professional emails

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Looking ahead, this anti-perfection trend likely represents just the beginning of a broader evolution. As AI writing tools advance, the focus will shift from correctness to believability, with future tools adapting tone, style, and strategic mistakes based on context and audience

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. The goal will center on making communication feel natural rather than flawless, potentially blurring the line between human and machine even further. As one observer noted, the scenario creates an absurd feedback loop: "An AI correcting the mistakes of another AI to generate an email that'll be read by another AI? It's LLMs all the way down"

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