Humanizer — a text-cleaning tool by Humify Lab.
It makes AI-generated writing sound human again.
Kills the AI tone. Makes your text sound human again.
We change how it reads, not what it says.
Keep the original level or simplify the wording — you choose.
No “Did ChatGPT write this?” replies. Even on Reddit.
“I finally stopped sounding like a LinkedIn bot. For the first time ever, people believed I actually wrote my own post. Total freedom.”
Former LinkedIn Hostage
“Reddit stopped asking if I'm a GPT clone. Humanizer killed the AI tone and saved me from the 'Did ChatGPT write this?' replies forever.”
PTSD Survivor @ r/technology
“Even inside the Matrix, I kept sounding like corporate code. Humanizer fixed my tone instantly. My boss still sounds worse than me.”
Program Inside the Matrix
“My specs used to sound like AI sludge. Humanizer made them clear and human again. Nobody asks who really wrote them anymore.”
Product Manager
“Humanizer removed the advanced words AI kept inserting. Now my messages sound natural — like something I'd actually write.”
Software Engineer
“I used to sound like a confused bot in threads. Humanizer made my posts clear and sharp — people now think I'm human again.”
Twitter Resident Intellectual
Real examples of how Humanizer improves your text.
Over the last two days, I’ve been working through a set of tasks that looked straightforward at first, but several unexpected issues surfaced along the way - especially around how the service handles edge cases when the upstream module returns a partial payload. One thing that became clear during debugging is that our assumptions around what the upstream guarantees were «mostly accurate», but not entirely consistent across environments.
To break it down more clearly, here are the main areas that required attention:
— The “resilience layer” behaved differently under load, which forced a deeper look into timeout propagation;
— Some interface contracts were documented as stable, but the actual runtime behavior showed subtle deviations;
— A few helper utilities rely on implicit defaults, leading to outcomes that were «technically correct», but not aligned with what the calling code expected.
The last two days I've been working through a set of tasks that looked simple at first, and then a bunch of weird issues came up - mostly around how the service behaves when the upstream module only returns a partial payload. While debugging, it became clear that our assumptions about the upstream guarantees were mostly right, but they don't actually behave the same way in every environment.
A few things needed attention:
- The "resilience layer" behaves differently under load, so I had to dig into how timeouts propagate.
- Some interface contracts are documented as stable, but the real runtime behavior doesn't fully match what's written.
- A few helper utilities depend on implicit defaults, so the results are technically correct, but not what the calling code expects.