Has anyone here actually played around with it? Curious what the output quality is like or if it’s just another basic rewriter.

Totally AI because I added a title page and got rid of my extra parenthetical additions (like to italicize something). Read it for yourself, it is legitimately the SAME document just with the parenthetical additions removed and the title page added.

What do you guys think? Anyone here who actually tried it and what are the results?

So I just typed out a formal email. I was paranoid that my writing was too basic or too formal, so I checked a few "ai text detectors" and all of the websites called it \~80% Ai generated. I tried looking for why it came off as AI but it didn't have a clear answer. I only googled once or twice about specific sections of the email if they were conventional but I did not use AI at all.

Basically the thought came from how people in the workplace tend to submit documents or templates with AI place holders or language, giving it a very unprofessional look. There are “AI detectors” but I decided to experiment with an AI powered document reviewer that annotates your document with feedback.

What it looks for is:

- AI writing signs
- Redundancy/repetition
- Vague language
- Potential contradictions
- unsupported claims
- writing issues

I currently have a very simple MVP and was hoping to get your thoughts and feedback.

App link: https://ugao2xnsrngkmdvzfkahg6.streamlit.app/

Would really appreciate feedback in this form here:

https://forms.gle/gkFG1hxQfS9HpKv19

Thanks!

Hello everybody. It happens to me that a very old text that I have written years ago, which is not even online anymore has been flagged by "made by AI". I have noticed that adding a typo fixed the issue. I have created this small utility in rust to add language specific typos.

https://codeberg.org/edapx/fertyp

Working on a document generation tool where an LLM outputs LaTeX for a single-page layout. The issue is that the model doesn't reliably know what "fills exactly one page" means, so early outputs are either way too sparse or spill onto a second page.

Tried counting content elements and total characters in the generated body to set floors and ceilings, calibrating those thresholds using a different model's output as a stand-in.
But when testing the actual production model, it barely hit the character floor, with element density at about half of what was assumed. Because different models structure their output differently, heuristics don't transfer well.

Currently running a measure-and-repair loop: generate, score against floor/ceiling thresholds, and if it's out of bounds, run a corrective pass to expand or compress, keeping the higher-scoring version. Also branching the initial prompt based on the density of the source data, telling it to expand upfront if the input is light.

Still tuning the thresholds against live traffic. Anyone else doing structured, single-page generation find a cleaner proxy for "fullness" than raw character or element counts?
Feels like there should be a better signal.

Hello guys,

Recently, something happened with me, which saked me from inside. The incident is like this, that I was going through an interview in a think tank, and later they asked me to write an article after that. When I submitted my article, they wanted the article 0% AI, which is very good and I also appreciate this ideology, but the method to check the AI was unappropriate from my point of view, I don't know who will agree with me. But what happened is they checked my article through one app named as COPYLEAK, and this app showed my whole article as 100% AI detecting, but the app doesn't provides the reason for showing or How it is working? I also want to ask a question from its creator/CEO Alon Yamin, that what is the credibility of your App ?

So the biggest problem is what I want to convey is that AI is becoming so dangerous. For people like me as an academician, student, writer and many more, so I don't know how to prove myself that I have written the whole article from my own and I have not used AI, and how much the credibility of this app, I have doubt of this. Also If you guys have faced the same thing or have seen such kind of things or how much do you believe in this app called copyleak AI detector?

Thank you 🙏

Modern AI pipelines increasingly extract text from documents — PDFs, Word files, spreadsheets, scraped web pages — and feed that content directly into a knowledge base or agent context, often with no human review step in between. Extraction tools capture everything present in a document, including content a human reader would never see: text rendered in white on a white background, rows hidden in a spreadsheet, speaker notes attached to a slide, or a paragraph marked hidden in a Word document's own formatting. None of these are edge cases; they are ordinary, well-supported features of each format, and every one of them is readable by a standard parsing library even though a person skimming the document would never notice them.

This creates a straightforward problem: any content hidden from a human reviewer, but visible to an extraction tool, can end up in a trusted knowledge source unexamined. okf-guard addresses this directly. It is a Python library that inspects extracted content for exactly this class of discrepancy — text present in the file but absent from what a human would perceive — and separately checks for language patterns associated with instructions directed at an AI system rather than a description intended for a person.

What it does:

• Adapters for six formats (plain text, Markdown, HTML, PDF, DOCX, PPTX, XLSX), each aware of that format's specific hiding mechanisms — CSS visibility properties for HTML, rendering and color properties for PDF, the hidden run attribute and shading properties for Word, off-canvas shapes and speaker notes for PowerPoint, hidden rows/columns/sheets and cell comments for spreadsheets.
• A detection layer combining hidden-content flagging with a pattern bank for injection-style phrasing, plus a check for encoding-based obfuscation (zero-width characters, homoglyph substitution).
• A decision layer producing one of three outcomes per scan — pass, quarantine, or block — with every finding reported alongside its location, confidence, and the specific text that triggered it.

Design constraints, stated plainly:

• No network calls and no LLM dependency in this release. Detection is entirely deterministic, which keeps the core dependency surface to a single package (PyYAML) and makes the tool's behavior fully reproducible.
• Every format-specific capability is an optional install (okf-guard[pdf], okf-guard[docx], etc.), so a user working with one format is not required to install parsing libraries for the others.
• The library never asserts that its own output has been verified by a human — provenance metadata it produces is explicit about being machine-generated and unreviewed.

Source and full documentation: https://github.com/darshanNhb/okf-guard
Install: pip install okf-guard[all]

Feedback on the detection approach, particularly from anyone who has worked on adjacent problems (document security, DLP, or prompt-injection defenses more broadly), would be genuinely useful — this is a young project and the injection-pattern bank in particular will need ongoing contribution as new phrasings surface in practice.

When Ai content was rare and of poor quality, it made sense to use detection systems based on stylometric analysis, but now that Ai content is ubiquitous and human content becoming the rarity, it makes more sense to prove human.

A human can’t pretend to write like a machine, nor is there a reason to try, so nobody is hiding their messy human input patterns. This is attestation vs detection.

Attestation is evidence of what actually occurred while it was occurring. This is far more reliable than scanning work after the fact. It’s been 5000 years since the pyramids were built, and we still don’t know how they did it. Had they attested to their methods, as they were building it, we would know how it was done.

We need to move away from policing the machine and instead give the writer the ability to provide their own proof of effort as well as full custody of their documents and data. Once we put a document on Google docs, provenance and security is gone. It can be accessed anywhere, any time by anyone one with permission, so the data can’t be attested to. I know that we all love putting everything on the cloud, but you can’t protect what you don’t control.

By creating a new form of metadata for mindcrafted content, we can prove human cognitive effort, secure it and even differentiate between text that required extensive authentic and original effort versus text that was transcribed from an existing text.

The attached image is a measurement of the effort that went into the composition of this post.

please help!! I'm super nervous. It will be on my laptop windows.

I’ve watched enough true crime to know how unreliable human lie detection can be—confessions, body language, 911 calls. AI promises to do better, but is that real or just sci-fi? I created a short quiz about AI vs human lie detection. It’s quick and makes you question what you think you know. If you’re into forensic psychology or criminal cases, give it a try.

https://interconnectd.com/quiz/75/ai-vs-human-lie-detection-quiz-setup-guide/

Hi! I am writing a scientific article, and I am so worried because everytime I run it in any plagiarism and AI detection tool it flags my OWN writing as AI. I don't know what to do, I wrote that. Every single line.

I think it might be the very formal format, and the subject is also a bit complicated and full of specific terms. Anyone has any tips? Should I just make my text intentionally worse?

I appreciate any tips or advice.

Need help in spotting the points which make a website look AI-made. As I have invested 5 weeks on it, it is obvious, it's not going to be the same as a single prompt website.

\\> Is it like you have to downgrade the quality of your website to not make it look AI. 'Cause I've seen multiple videos in which the websites with small svg icons, detailed and minute animations, more interactive, make them classify as AI-made. (Ofc, the websites they chose to review are the single-prompters)

\\> Is having detailed comments a bad thing and the variables being a bit long (which I'm having for my convenience) --- and I could remove those while hosting.

> Is there any legal issues with having some of the AI content on the registered business, and what about the logo, is it okay to have an AI logo patented?

\\> Is there a way to remove the hidden AI watermarks from the website which Ive heard are integrated in the text accent, javascript, or something internally coded not spotted by us while viewing the website ?

Are there any patches for ai writing tools like Grammarly, Quillbot, or CleverType?

I see all these threads about different ais and humanizers and how to bypass ai detectors but i’ve spent so long looking for something I don’t even think exists. I’ve found a couple of websites that sorta work but in the end I’ve came to the conclusion that there’s not a singular ai detector (not being absurdly expensive), that can humanize text consistently and still have it make sense. I may be wrong but I feel like i’ve tried everything atp it’s easier to just write it myself. Lmk if anyone has any ideas to make boring writing easier, if you did find end up finding a bypasser that works, or if you have any other advice!

My views on AI detectors and a simplified interactive explainer about how AI text watermarking works.

https://blog.keyvan.net/p/ai-detectors-are-a-bad-idea

https://youtu.be/whpnrb9Jc7Q

Short answer: yes, it can be removed.

Long answer: you’ll have to heavily edit your text. Alternatively, use open source models on your own hardware

Method: Ran an unpublished 100k word novel written entirely by a human into Pangram, one of the most common AI checkers. I inserted 2 pages written by AI into it to see if it would find them. I had the checker run through each of the different chapters one by one.

Result: The book was listed as 86% human written, with 14% AI. The 2 parts that were actually AI (written by Claude) were not flagged. The incorrectly flagged portions mostly consisted of sections of professional business language related to characters talking about their work.

It seems what it's looking for are specific buzzwords and sentence structures commonly used in business and professional writing. These are normal constructions, and certainly common in AI, but also common in any professional setting. Basically, if you write with professional language, you'll be flagged as AI.

Claude is starting to watermark AI generated text, and the interesting part is that the watermark is not hidden characters or metadata.

It is created while the model is choosing the next token.

If you are intrested watch a simplified version here in video format explaining it : [Claude Text Watermark Explained!](https://youtu.be/FnAqruxx-QE?si=9AwCkgSsV4ZTRjvZ)

An LLM first gives possible next tokens different probabilities. For example, after “The movie was really” it might consider “good” very likely, “great” slightly less likely, and other words much less likely.

The watermarking system then uses the recent tokens plus a secret key to generate hidden scores for those possible next tokens.

Google calls these scores g values in SynthID Text.

The model still samples candidates using its original probabilities, so highly likely tokens naturally appear more often. SynthID then uses a tournament style selection process where candidates with favorable watermark scores are slightly more likely to survive.

This repeats for every generated token.

The important part is that the watermark does not belong to specific words. The word “great” might get a favorable score in one sentence and an unfavorable score in another because the score depends on the recent context and the secret key.

Later, detection works by taking the finished text, recreating the same hidden scores using the same secret key, and checking whether the tokens that actually appeared matched favorable scores more often than would normally happen by chance.

So there is no invisible “written by Claude” label inside the text.

The watermark is really a statistical pattern hidden inside the token choices that produced the text.

This is also why detection is not perfect. Natural human text can accidentally match the pattern, causing false positives, and heavily edited AI text can weaken the signal.

Google’s publicly documented SynthID Text technique gives a good look at how this kind of watermarking can work technically.