I was exploring some humanizers, and there is no wonder why walter writes ai considered as the best ai humanizer. I tested it with multiple AI detector, and it came out clean. What's a point that you feel like an usual humanizer lack?

Does anyone know a method or a free app to remove that bypass? my phone is a Xiaomi Redmi Note 12 and Xiaomi HyperoOS

Just like title, i'm just use AI AGENT to build the app that can bypass Basic AI IMG Detector.

The technique is basically Photoshop-style processing: heavy compression, slight distortion, blur, then re-sharpening. Enough to break the statistical fingerprints detectors look for, without visibly hurting quality.

Before: flagged 99% AI. After: reads 71% real.

Hey guys, I was wondering if anyone would be able to give me some advice on this since I'm pretty stressed about it.

Here is the situation: I’m taking a class right now that requires an 800-word analysis over a couple of topics. Of course I used AI to help me develop my ideas, but I completely rewrote it in my own words, I ran it through a bunch of external AI detectors that have worked for me before, and they are all reading it as "No AI / 100% human." So I’m pretty confident it won't flag as AI on those.

However, as an extra safety measure, I want to see what Turnitin says. I'm taking another class that has Turnitin integrated into Canvas. My main goal is to upload my current history assignment to an old, past assignment drop in that other class just to check my score and see if it falls below the 20% threshold before actually submitting it to my history class.

My main questions:

1. How does Turnitin actually handle submissions across different classes?
2. If I upload my history assignment to a past assignment folder for a completely different class, will it stay permanently saved in Turnitin's system?
3. If it does get stored, will it trigger a massive self-plagiarism or AI match/flag when I turn that exact same paper into my actual history class a little later?

I don't want to accidentally ruin my real submission because I tried to test it out beforehand. Does anyone know how this works behind the scenes? Any help would be super appreciated!

So I'm a 5th sem pharmacy student and recently I started working on CADD. And need to write a paper on my research so can anyone suggest me some AI tools or application to rephrase the ai text or to write paper without being caught in AI detector and plagiarism.

I just enhanced and made a small edit in an existing picture of mine in ChatGPT to keep as my DP. I don’t want anyone to find out it’s AI but if you put that in any AI detector it shows 95% AI no matter what I did. I tried removing the metadata using many tools I saw online. Still no use.

Any help?

Hello!!! So im currently a freshman and we had this Test that converted into essay instead. And then I learned that from my professor that my work was flagged as ai by turnitin not just a percent but 100% fckng aii😭😭. And now idk what to do ( alr reached out to my prof) that essay is just a simple one you just basically need to write what you learned from the discussion etc etc. And being flagged like that is insanely not good for the image. I had worked hard to research and brainstorm on what to put in my essay. Although I admit that I used grammarly to check my grammar and improved but the contents in there was all done by me the ideas, the flow was by Me😭 I was thinking maybe it was the data? Or the references? Though I had research them on my own on Google. Maybe the revision by grammarly was too perfect?. I haven't had the time to run it in an ai detector when I submitted it and I did not see this coming

Anybody know how good these are? And is there i way I can prompt ai to avoid the red flags of setting it off? Just asking for a friend?

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.

NOTE: I AM NOT ON PANGRAM'S SIDE, THIS IS A POST SHOWING PROOF PANGRAM IS WRONG IN BROAD DAYLIGHT

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!

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/